Quantitative structure-retention relationships of flavonoids unraveled by immobilized artificial membrane chromatography.

PubMed

Santoro, Adriana Leandra; Carrilho, Emanuel; Lanças, Fernando Mauro; Montanari, Carlos Alberto

2016-06-10

The pharmacokinetic properties of flavonoids with differing degrees of lipophilicity were investigated using immobilized artificial membranes (IAMs) as the stationary phase in high performance liquid chromatography (HPLC). For each flavonoid compound, we investigated whether the type of column used affected the correlation between the retention factors and the calculated octanol/water partition (log Poct). Three-dimensional (3D) molecular descriptors were calculated from the molecular structure of each compound using i) VolSurf software, ii) the GRID method (computational procedure for determining energetically favorable binding sites in molecules of known structure using a probe for calculating the 3D molecular interaction fields, between the probe and the molecule), and iii) the relationship between partition and molecular structure, analyzed in terms of physicochemical descriptors. The VolSurf built-in Caco-2 model was used to estimate compound permeability. The extent to which the datasets obtained from different columns differ both from each other and from both the calculated log Poct and the predicted permeability in Caco-2 cells was examined by principal component analysis (PCA). The immobilized membrane partition coefficients (kIAM) were analyzed using molecular descriptors in partial least square regression (PLS) and a quantitative structure-retention relationship was generated for the chromatographic retention in the cholesterol column. The cholesterol column provided the best correlation with the permeability predicted by the Caco-2 cell model and a good fit model with great prediction power was obtained for its retention data (R(2)=0.96 and Q(2)=0.85 with four latent variables). Copyright © 2015 Elsevier B.V. All rights reserved.

Molecular structure and gas chromatographic retention behavior of the components of Ylang-Ylang oil.

PubMed

Olivero, J; Gracia, T; Payares, P; Vivas, R; Díaz, D; Daza, E; Geerlings, P

1997-05-01

Using quantitative structure-retention relationships (QSRR) methodologies the Kovats gas chromatographic retention indices for both apolar (DB-1) and polar (DB-Wax) columns for 48 compounds from Ylang-Ylang essential oil were empirically predicted from calculated and experimental data on molecular structure. Topological, geometric, and electronic descriptors were obtained for model generation. Relationships between descriptors and the retention data reported were established by linear multiple regression, giving equations that can be used to predict the Kovats indices for compounds present in essential oils, both in DB-1 and DB-Wax columns. Factor analysis was performed to interpret the meaning of the descriptors included in the models. The prediction model for the DB-1 column includes descriptors such as Randic's first-order connectivity index (1X), the molecular surface (MSA), the sum of the atomic charge on all the hydrogens (QH), Randic's third-order connectivity index (3X) and the molecular electronegativity (chi). The prediction model for the DB-Wax column includes the first three descriptors mentioned for the DB-1 column (1X, MSA and QH) and the most negative charge (MNC), the global softness (S), and the difference between Randic's and Kier and Hall's third-order connectivity indexes (3X-3XV).

Many-Body Descriptors for Predicting Molecular Properties with Machine Learning: Analysis of Pairwise and Three-Body Interactions in Molecules.

PubMed

Pronobis, Wiktor; Tkatchenko, Alexandre; Müller, Klaus-Robert

2018-06-12

Machine learning (ML) based prediction of molecular properties across chemical compound space is an important and alternative approach to efficiently estimate the solutions of highly complex many-electron problems in chemistry and physics. Statistical methods represent molecules as descriptors that should encode molecular symmetries and interactions between atoms. Many such descriptors have been proposed; all of them have advantages and limitations. Here, we propose a set of general two-body and three-body interaction descriptors which are invariant to translation, rotation, and atomic indexing. By adapting the successfully used kernel ridge regression methods of machine learning, we evaluate our descriptors on predicting several properties of small organic molecules calculated using density-functional theory. We use two data sets. The GDB-7 set contains 6868 molecules with up to 7 heavy atoms of type CNO. The GDB-9 set is composed of 131722 molecules with up to 9 heavy atoms containing CNO. When trained on 5000 random molecules, our best model achieves an accuracy of 0.8 kcal/mol (on the remaining 1868 molecules of GDB-7) and 1.5 kcal/mol (on the remaining 126722 molecules of GDB-9) respectively. Applying a linear regression model on our novel many-body descriptors performs almost equal to a nonlinear kernelized model. Linear models are readily interpretable: a feature importance ranking measure helps to obtain qualitative and quantitative insights on the importance of two- and three-body molecular interactions for predicting molecular properties computed with quantum-mechanical methods.

A Quantum Chemical and Statistical Study of Phenolic Schiff Bases with Antioxidant Activity against DPPH Free Radical

PubMed Central

Anouar, El Hassane

2014-01-01

Phenolic Schiff bases are known as powerful antioxidants. To select the electronic, 2D and 3D descriptors responsible for the free radical scavenging ability of a series of 30 phenolic Schiff bases, a set of molecular descriptors were calculated by using B3P86 (Becke’s three parameter hybrid functional with Perdew 86 correlation functional) combined with 6-31 + G(d,p) basis set (i.e., at the B3P86/6-31 + G(d,p) level of theory). The chemometric methods, simple and multiple linear regressions (SLR and MLR), principal component analysis (PCA) and hierarchical cluster analysis (HCA) were employed to reduce the dimensionality and to investigate the relationship between the calculated descriptors and the antioxidant activity. The results showed that the antioxidant activity mainly depends on the first and second bond dissociation enthalpies of phenolic hydroxyl groups, the dipole moment and the hydrophobicity descriptors. The antioxidant activity is inversely proportional to the main descriptors. The selected descriptors discriminate the Schiff bases into active and inactive antioxidants. PMID:26784873

Investigation of anticancer properties of caffeinated complexes via computational chemistry methods

NASA Astrophysics Data System (ADS)

Sayin, Koray; Üngördü, Ayhan

2018-03-01

Computational investigations were performed for 1,3,7-trimethylpurine-2,6-dione, 3,7-dimethylpurine-2,6-dione, their Ru(II) and Os(III) complexes. B3LYP/6-311 ++G(d,p)(LANL2DZ) level was used in numerical calculations. Geometric parameters, IR spectrum, 1H-, 13C and 15N NMR spectrum were examined in detail. Additionally, contour diagram of frontier molecular orbitals (FMOs), molecular electrostatic potential (MEP) maps, MEP contour and some quantum chemical descriptors were used in the determination of reactivity rankings and active sites. The electron density on the surface was similar to each other in studied complexes. Quantum chemical descriptors were investigated and the anticancer activity of complexes were more than cisplatin and their ligands. Additionally, molecular docking calculations were performed in water between related complexes and a protein (ID: 3WZE). The most interact complex was found as Os complex. The interaction energy was calculated as 342.9 kJ/mol.

Global chemical reactivity parameters for several chiral beta-blockers from the Density Functional Theory viewpoint

PubMed Central

TALMACIU, MONA MARIA; BODOKI, EDE; OPREAN, RADU

2016-01-01

Background and aim Beta-adrenergic antagonists have been established as first line treatment in the medical management of hypertension, acute coronary syndrome and other cardiovascular diseases, as well as for the prevention of initial episodes of gastrointestinal bleeding in patients with cirrhosis and esophageal varices, glaucoma, and have recently become the main form of treatment of infantile hemangiomas. The aim of the present study is to calculate for 14 beta-blockers several quantum chemical descriptors in order to interpret various molecular properties such as electronic structure, conformation, reactivity, in the interest of determining how such descriptors could have an impact on our understanding of the experimental observations and describing various aspects of chemical binding of beta-blockers in terms of these descriptors. Methods The 2D chemical structures of the beta-blockers (14 molecules with one stereogenic center) were cleaned in 3D, their geometry was preoptimized using the software MOPAC2012, by PM6 method, and then further refined using standard settings in MOE; HOMO and LUMO descriptors were calculated using semi-empirical molecular orbital methods AM1, MNDO and PM3, for the lowest energy conformers and the quantum chemical descriptors (HLG, electronegativity, chemical potential, hardness and softness, electrophilicity) were then calculated. Results According to HOMO-LUMO gap and the chemical hardness the most stable compounds are alprenolol, bisoprolol and esmolol. The softness values calculated for the study molecules revolve around 0.100. Propranolol, sotalol and timolol have among the highest electrophilicity index of the studied beta-blocker molecules. Results obtained from calculations showed that acebutolol, atenolol, timolol and sotalol have the highest values for the electronegativity index. Conclusions The future aim is to determine whether it is possible to find a valid correlation between these descriptors and the physicochemical behavior of the molecules from this class. The HLG could be correlated to the experimentally recorded electrochemical properties of the molecules. HOMO could be correlated to the observed oxidation potential, since the required voltage is related to the energy of the HOMO, because only the electron from this orbital is involved in the oxidation process. PMID:27857521

Molecular moment similarity between several nucleoside analogs of thymidine and thymidine. sil@watson.ibm.com.

PubMed

Silverman, B D; Pitman, M C; Platt, D E

1999-06-01

Molecular moment descriptors of the shape and charge distributions of twenty five nucleoside structures have been examined. The structures include thymidine as well as the difluorotoluene nucleoside analog which has been found to pair efficiently with adenine by polymerase catalysis. The remaining twenty three structures have been chosen to be as structurally similar to thymidine and to the difluorotoluene nucleoside analog as possible. The moment descriptors which include a description of the relationship of molecular charge to shape show the difluorotoluene nucleoside to be one of the most proximate molecules to thymidine in the space of the molecular moments. The calculations, therefore, suggest that polymerase specificity might be not only a consequence of molecular steric features alone but also of the molecular electrostatic environment and its registration with molecular shape.

Periodic table-based descriptors to encode cytotoxicity profile of metal oxide nanoparticles: a mechanistic QSTR approach.

PubMed

Kar, Supratik; Gajewicz, Agnieszka; Puzyn, Tomasz; Roy, Kunal; Leszczynski, Jerzy

2014-09-01

Nanotechnology has evolved as a frontrunner in the development of modern science. Current studies have established toxicity of some nanoparticles to human and environment. Lack of sufficient data and low adequacy of experimental protocols hinder comprehensive risk assessment of nanoparticles (NPs). In the present work, metal electronegativity (χ), the charge of the metal cation corresponding to a given oxide (χox), atomic number and valence electron number of the metal have been used as simple molecular descriptors to build up quantitative structure-toxicity relationship (QSTR) models for prediction of cytotoxicity of metal oxide NPs to bacteria Escherichia coli. These descriptors can be easily obtained from molecular formula and information acquired from periodic table in no time. It has been shown that a simple molecular descriptor χox can efficiently encode cytotoxicity of metal oxides leading to models with high statistical quality as well as interpretability. Based on this model and previously published experimental results, we have hypothesized the most probable mechanism of the cytotoxicity of metal oxide nanoparticles to E. coli. Moreover, the required information for descriptor calculation is independent of size range of NPs, nullifying a significant problem that various physical properties of NPs change for different size ranges. Copyright © 2014 Elsevier Inc. All rights reserved.

Effect of substituents on prediction of TLC retention of tetra-dentate Schiff bases and their Copper(II) and Nickel(II) complexes.

PubMed

Stevanović, Nikola R; Perušković, Danica S; Gašić, Uroš M; Antunović, Vesna R; Lolić, Aleksandar Đ; Baošić, Rada M

2017-03-01

The objectives of this study were to gain insights into structure-retention relationships and to propose the model to estimating their retention. Chromatographic investigation of series of 36 Schiff bases and their copper(II) and nickel(II) complexes was performed under both normal- and reverse-phase conditions. Chemical structures of the compounds were characterized by molecular descriptors which are calculated from the structure and related to the chromatographic retention parameters by multiple linear regression analysis. Effects of chelation on retention parameters of investigated compounds, under normal- and reverse-phase chromatographic conditions, were analyzed by principal component analysis, quantitative structure-retention relationship and quantitative structure-activity relationship models were developed on the basis of theoretical molecular descriptors, calculated exclusively from molecular structure, and parameters of retention and lipophilicity. Copyright © 2016 John Wiley & Sons, Ltd.

Prediction on the inhibition ratio of pyrrolidine derivatives on matrix metalloproteinase based on gene expression programming.

PubMed

Li, Yuqin; You, Guirong; Jia, Baoxiu; Si, Hongzong; Yao, Xiaojun

2014-01-01

Quantitative structure-activity relationships (QSAR) were developed to predict the inhibition ratio of pyrrolidine derivatives on matrix metalloproteinase via heuristic method (HM) and gene expression programming (GEP). The descriptors of 33 pyrrolidine derivatives were calculated by the software CODESSA, which can calculate quantum chemical, topological, geometrical, constitutional, and electrostatic descriptors. HM was also used for the preselection of 5 appropriate molecular descriptors. Linear and nonlinear QSAR models were developed based on the HM and GEP separately and two prediction models lead to a good correlation coefficient (R (2)) of 0.93 and 0.94. The two QSAR models are useful in predicting the inhibition ratio of pyrrolidine derivatives on matrix metalloproteinase during the discovery of new anticancer drugs and providing theory information for studying the new drugs.

RED: a set of molecular descriptors based on Renyi entropy.

PubMed

Delgado-Soler, Laura; Toral, Raul; Tomás, M Santos; Rubio-Martinez, Jaime

2009-11-01

New molecular descriptors, RED (Renyi entropy descriptors), based on the generalized entropies introduced by Renyi are presented. Topological descriptors based on molecular features have proven to be useful for describing molecular profiles. Renyi entropy is used as a variability measure to contract a feature-pair distribution composing the descriptor vector. The performance of RED descriptors was tested for the analysis of different sets of molecular distances, virtual screening, and pharmacological profiling. A free parameter of the Renyi entropy has been optimized for all the considered applications.

Prediction of Mutagenicity of Chemicals from Their Calculated Molecular Descriptors: A Case Study with Structurally Homogeneous versus Diverse Datasets.

PubMed

Basak, Subhash C; Majumdar, Subhabrata

2015-01-01

Variation in high-dimensional data is often caused by a few latent factors, and hence dimension reduction or variable selection techniques are often useful in gathering useful information from the data. In this paper we consider two such recent methods: Interrelated two-way clustering and envelope models. We couple these methods with traditional statistical procedures like ridge regression and linear discriminant analysis, and apply them on two data sets which have more predictors than samples (i.e. n < p scenario) and several types of molecular descriptors. One of these datasets consists of a congeneric group of Amines while the other has a much diverse collection compounds. The difference of prediction results between these two datasets for both the methods supports the hypothesis that for a congeneric set of compounds, descriptors of a certain type are enough to provide good QSAR models, but as the data set grows diverse including a variety of descriptors can improve model quality considerably.

QuBiLS-MIDAS: a parallel free-software for molecular descriptors computation based on multilinear algebraic maps.

PubMed

García-Jacas, César R; Marrero-Ponce, Yovani; Acevedo-Martínez, Liesner; Barigye, Stephen J; Valdés-Martiní, José R; Contreras-Torres, Ernesto

2014-07-05

The present report introduces the QuBiLS-MIDAS software belonging to the ToMoCoMD-CARDD suite for the calculation of three-dimensional molecular descriptors (MDs) based on the two-linear (bilinear), three-linear, and four-linear (multilinear or N-linear) algebraic forms. Thus, it is unique software that computes these tensor-based indices. These descriptors, establish relations for two, three, and four atoms by using several (dis-)similarity metrics or multimetrics, matrix transformations, cutoffs, local calculations and aggregation operators. The theoretical background of these N-linear indices is also presented. The QuBiLS-MIDAS software was developed in the Java programming language and employs the Chemical Development Kit library for the manipulation of the chemical structures and the calculation of the atomic properties. This software is composed by a desktop user-friendly interface and an Abstract Programming Interface library. The former was created to simplify the configuration of the different options of the MDs, whereas the library was designed to allow its easy integration to other software for chemoinformatics applications. This program provides functionalities for data cleaning tasks and for batch processing of the molecular indices. In addition, it offers parallel calculation of the MDs through the use of all available processors in current computers. The studies of complexity of the main algorithms demonstrate that these were efficiently implemented with respect to their trivial implementation. Lastly, the performance tests reveal that this software has a suitable behavior when the amount of processors is increased. Therefore, the QuBiLS-MIDAS software constitutes a useful application for the computation of the molecular indices based on N-linear algebraic maps and it can be used freely to perform chemoinformatics studies. Copyright © 2014 Wiley Periodicals, Inc.

[Quantitative relationship between gas chromatographic retention time and structural parameters of alkylphenols].

PubMed

Ruan, Xiaofang; Zhang, Ruisheng; Yao, Xiaojun; Liu, Mancang; Fan, Botao

2007-03-01

Alkylphenols are a group of permanent pollutants in the environment and could adversely disturb the human endocrine system. It is therefore important to effectively separate and measure the alkylphenols. To guide the chromatographic analysis of these compounds in practice, the development of quantitative relationship between the molecular structure and the retention time of alkylphenols becomes necessary. In this study, topological, constitutional, geometrical, electrostatic and quantum-chemical descriptors of 44 alkylphenols were calculated using a software, CODESSA, and these descriptors were pre-selected using the heuristic method. As a result, three-descriptor linear model (LM) was developed to describe the relationship between the molecular structure and the retention time of alkylphenols. Meanwhile, the non-linear regression model was also developed based on support vector machine (SVM) using the same three descriptors. The correlation coefficient (R(2)) for the LM and SVM was 0.98 and 0. 92, and the corresponding root-mean-square error was 0. 99 and 2. 77, respectively. By comparing the stability and prediction ability of the two models, it was found that the linear model was a better method for describing the quantitative relationship between the retention time of alkylphenols and the molecular structure. The results obtained suggested that the linear model could be applied for the chromatographic analysis of alkylphenols with known molecular structural parameters.

Mutagenicity, anticancer activity and blood brain barrier: similarity and dissimilarity of molecular alerts.

PubMed

Toropov, Andrey A; Toropova, Alla P; Benfenati, Emilio; Salmona, Mario

2018-06-01

The aim of the present work is an attempt to define computable measure of similarity between different endpoints. The similarity of structural alerts of different biochemical endpoints can be used to solve tasks of medicinal chemistry. Optimal descriptors are a tool to build up models for different endpoints. The optimal descriptor is calculated with simplified molecular input-line entry system (SMILES). A group of elements (single symbol or pair of symbols) can represent any SMILES. Each element of SMILES can be represented by so-called correlation weight i.e. coefficient that should be used to calculate descriptor. Numerical data on the correlation weights are calculated by the Monte Carlo method, i.e. by optimization procedure, which gives maximal correlation coefficient between the optimal descriptor and endpoint for the training set. Statistically stable correlation weights observed in several runs of the optimization can be examined as structural alerts, which are promoters of the increase or the decrease of a biochemical activity of a substance. Having data on several runs of the optimization correlation weights, one can extract list of promoters of increase and list of promoters of decrease for an endpoint. The study of similarity and dissimilarity of the above lists has been carried out for the following pairs of endpoints: (i) mutagenicity and anticancer activity; (ii) mutagenicity and blood brain barrier; and (iii) blood brain barrier and anticancer activity. The computational experiment confirms that similarity and dissimilarity for pairs of endpoints can be measured.

Experimental and computational prediction of glass transition temperature of drugs.

PubMed

Alzghoul, Ahmad; Alhalaweh, Amjad; Mahlin, Denny; Bergström, Christel A S

2014-12-22

Glass transition temperature (Tg) is an important inherent property of an amorphous solid material which is usually determined experimentally. In this study, the relation between Tg and melting temperature (Tm) was evaluated using a data set of 71 structurally diverse druglike compounds. Further, in silico models for prediction of Tg were developed based on calculated molecular descriptors and linear (multilinear regression, partial least-squares, principal component regression) and nonlinear (neural network, support vector regression) modeling techniques. The models based on Tm predicted Tg with an RMSE of 19.5 K for the test set. Among the five computational models developed herein the support vector regression gave the best result with RMSE of 18.7 K for the test set using only four chemical descriptors. Hence, two different models that predict Tg of drug-like molecules with high accuracy were developed. If Tm is available, a simple linear regression can be used to predict Tg. However, the results also suggest that support vector regression and calculated molecular descriptors can predict Tg with equal accuracy, already before compound synthesis.

ChemoPy: freely available python package for computational biology and chemoinformatics.

PubMed

Cao, Dong-Sheng; Xu, Qing-Song; Hu, Qian-Nan; Liang, Yi-Zeng

2013-04-15

Molecular representation for small molecules has been routinely used in QSAR/SAR, virtual screening, database search, ranking, drug ADME/T prediction and other drug discovery processes. To facilitate extensive studies of drug molecules, we developed a freely available, open-source python package called chemoinformatics in python (ChemoPy) for calculating the commonly used structural and physicochemical features. It computes 16 drug feature groups composed of 19 descriptors that include 1135 descriptor values. In addition, it provides seven types of molecular fingerprint systems for drug molecules, including topological fingerprints, electro-topological state (E-state) fingerprints, MACCS keys, FP4 keys, atom pairs fingerprints, topological torsion fingerprints and Morgan/circular fingerprints. By applying a semi-empirical quantum chemistry program MOPAC, ChemoPy can also compute a large number of 3D molecular descriptors conveniently. The python package, ChemoPy, is freely available via http://code.google.com/p/pychem/downloads/list, and it runs on Linux and MS-Windows. Supplementary data are available at Bioinformatics online.

Rapid prediction of chemical metabolism by human UDP-glucuronosyltransferase isoforms using quantum chemical descriptors derived with the electronegativity equalization method.

PubMed

Sorich, Michael J; McKinnon, Ross A; Miners, John O; Winkler, David A; Smith, Paul A

2004-10-07

This study aimed to evaluate in silico models based on quantum chemical (QC) descriptors derived using the electronegativity equalization method (EEM) and to assess the use of QC properties to predict chemical metabolism by human UDP-glucuronosyltransferase (UGT) isoforms. Various EEM-derived QC molecular descriptors were calculated for known UGT substrates and nonsubstrates. Classification models were developed using support vector machine and partial least squares discriminant analysis. In general, the most predictive models were generated with the support vector machine. Combining QC and 2D descriptors (from previous work) using a consensus approach resulted in a statistically significant improvement in predictivity (to 84%) over both the QC and 2D models and the other methods of combining the descriptors. EEM-derived QC descriptors were shown to be both highly predictive and computationally efficient. It is likely that EEM-derived QC properties will be generally useful for predicting ADMET and physicochemical properties during drug discovery.

A promising tool to achieve chemical accuracy for density functional theory calculations on Y-NO homolysis bond dissociation energies.

PubMed

Li, Hong Zhi; Hu, Li Hong; Tao, Wei; Gao, Ting; Li, Hui; Lu, Ying Hua; Su, Zhong Min

2012-01-01

A DFT-SOFM-RBFNN method is proposed to improve the accuracy of DFT calculations on Y-NO (Y = C, N, O, S) homolysis bond dissociation energies (BDE) by combining density functional theory (DFT) and artificial intelligence/machine learning methods, which consist of self-organizing feature mapping neural networks (SOFMNN) and radial basis function neural networks (RBFNN). A descriptor refinement step including SOFMNN clustering analysis and correlation analysis is implemented. The SOFMNN clustering analysis is applied to classify descriptors, and the representative descriptors in the groups are selected as neural network inputs according to their closeness to the experimental values through correlation analysis. Redundant descriptors and intuitively biased choices of descriptors can be avoided by this newly introduced step. Using RBFNN calculation with the selected descriptors, chemical accuracy (≤1 kcal·mol(-1)) is achieved for all 92 calculated organic Y-NO homolysis BDE calculated by DFT-B3LYP, and the mean absolute deviations (MADs) of the B3LYP/6-31G(d) and B3LYP/STO-3G methods are reduced from 4.45 and 10.53 kcal·mol(-1) to 0.15 and 0.18 kcal·mol(-1), respectively. The improved results for the minimal basis set STO-3G reach the same accuracy as those of 6-31G(d), and thus B3LYP calculation with the minimal basis set is recommended to be used for minimizing the computational cost and to expand the applications to large molecular systems. Further extrapolation tests are performed with six molecules (two containing Si-NO bonds and two containing fluorine), and the accuracy of the tests was within 1 kcal·mol(-1). This study shows that DFT-SOFM-RBFNN is an efficient and highly accurate method for Y-NO homolysis BDE. The method may be used as a tool to design new NO carrier molecules.

A Promising Tool to Achieve Chemical Accuracy for Density Functional Theory Calculations on Y-NO Homolysis Bond Dissociation Energies

PubMed Central

Li, Hong Zhi; Hu, Li Hong; Tao, Wei; Gao, Ting; Li, Hui; Lu, Ying Hua; Su, Zhong Min

2012-01-01

A DFT-SOFM-RBFNN method is proposed to improve the accuracy of DFT calculations on Y-NO (Y = C, N, O, S) homolysis bond dissociation energies (BDE) by combining density functional theory (DFT) and artificial intelligence/machine learning methods, which consist of self-organizing feature mapping neural networks (SOFMNN) and radial basis function neural networks (RBFNN). A descriptor refinement step including SOFMNN clustering analysis and correlation analysis is implemented. The SOFMNN clustering analysis is applied to classify descriptors, and the representative descriptors in the groups are selected as neural network inputs according to their closeness to the experimental values through correlation analysis. Redundant descriptors and intuitively biased choices of descriptors can be avoided by this newly introduced step. Using RBFNN calculation with the selected descriptors, chemical accuracy (≤1 kcal·mol−1) is achieved for all 92 calculated organic Y-NO homolysis BDE calculated by DFT-B3LYP, and the mean absolute deviations (MADs) of the B3LYP/6-31G(d) and B3LYP/STO-3G methods are reduced from 4.45 and 10.53 kcal·mol−1 to 0.15 and 0.18 kcal·mol−1, respectively. The improved results for the minimal basis set STO-3G reach the same accuracy as those of 6-31G(d), and thus B3LYP calculation with the minimal basis set is recommended to be used for minimizing the computational cost and to expand the applications to large molecular systems. Further extrapolation tests are performed with six molecules (two containing Si-NO bonds and two containing fluorine), and the accuracy of the tests was within 1 kcal·mol−1. This study shows that DFT-SOFM-RBFNN is an efficient and highly accurate method for Y-NO homolysis BDE. The method may be used as a tool to design new NO carrier molecules. PMID:22942689

Quantitative structure-retention relationships for gas chromatographic retention indices of alkylbenzenes with molecular graph descriptors.

PubMed

Ivanciuc, O; Ivanciuc, T; Klein, D J; Seitz, W A; Balaban, A T

2001-02-01

Quantitative structure-retention relationships (QSRR) represent statistical models that quantify the connection between the molecular structure and the chromatographic retention indices of organic compounds, allowing the prediction of retention indices of novel, not yet synthesized compounds, solely from their structural descriptors. Using multiple linear regression, QSRR models for the gas chromatographic Kováts retention indices of 129 alkylbenzenes are generated using molecular graph descriptors. The correlational ability of structural descriptors computed from 10 molecular matrices is investigated, showing that the novel reciprocal matrices give numerical indices with improved correlational ability. A QSRR equation with 5 graph descriptors gives the best calibration and prediction results, demonstrating the usefulness of the molecular graph descriptors in modeling chromatographic retention parameters. The sequential orthogonalization of descriptors suggests simpler QSRR models by eliminating redundant structural information.

On the Relationship between Molecular Hit Rates in High-Throughput Screening and Molecular Descriptors.

PubMed

Hansson, Mari; Pemberton, John; Engkvist, Ola; Feierberg, Isabella; Brive, Lars; Jarvis, Philip; Zander-Balderud, Linda; Chen, Hongming

2014-06-01

High-throughput screening (HTS) is widely used in the pharmaceutical industry to identify novel chemical starting points for drug discovery projects. The current study focuses on the relationship between molecular hit rate in recent in-house HTS and four common molecular descriptors: lipophilicity (ClogP), size (heavy atom count, HEV), fraction of sp(3)-hybridized carbons (Fsp3), and fraction of molecular framework (f(MF)). The molecular hit rate is defined as the fraction of times the molecule has been assigned as active in the HTS campaigns where it has been screened. Beta-binomial statistical models were built to model the molecular hit rate as a function of these descriptors. The advantage of the beta-binomial statistical models is that the correlation between the descriptors is taken into account. Higher degree polynomial terms of the descriptors were also added into the beta-binomial statistic model to improve the model quality. The relative influence of different molecular descriptors on molecular hit rate has been estimated, taking into account that the descriptors are correlated to each other through applying beta-binomial statistical modeling. The results show that ClogP has the largest influence on the molecular hit rate, followed by Fsp3 and HEV. f(MF) has only a minor influence besides its correlation with the other molecular descriptors. © 2013 Society for Laboratory Automation and Screening.

Stargate GTM: Bridging Descriptor and Activity Spaces.

PubMed

Gaspar, Héléna A; Baskin, Igor I; Marcou, Gilles; Horvath, Dragos; Varnek, Alexandre

2015-11-23

Predicting the activity profile of a molecule or discovering structures possessing a specific activity profile are two important goals in chemoinformatics, which could be achieved by bridging activity and molecular descriptor spaces. In this paper, we introduce the "Stargate" version of the Generative Topographic Mapping approach (S-GTM) in which two different multidimensional spaces (e.g., structural descriptor space and activity space) are linked through a common 2D latent space. In the S-GTM algorithm, the manifolds are trained simultaneously in two initial spaces using the probabilities in the 2D latent space calculated as a weighted geometric mean of probability distributions in both spaces. S-GTM has the following interesting features: (1) activities are involved during the training procedure; therefore, the method is supervised, unlike conventional GTM; (2) using molecular descriptors of a given compound as input, the model predicts a whole activity profile, and (3) using an activity profile as input, areas populated by relevant chemical structures can be detected. To assess the performance of S-GTM prediction models, a descriptor space (ISIDA descriptors) of a set of 1325 GPCR ligands was related to a B-dimensional (B = 1 or 8) activity space corresponding to pKi values for eight different targets. S-GTM outperforms conventional GTM for individual activities and performs similarly to the Lasso multitask learning algorithm, although it is still slightly less accurate than the Random Forest method.

QSAR Study and Molecular Design of Open-Chain Enaminones as Anticonvulsant Agents

PubMed Central

Garro Martinez, Juan C.; Duchowicz, Pablo R.; Estrada, Mario R.; Zamarbide, Graciela N.; Castro, Eduardo A.

2011-01-01

Present work employs the QSAR formalism to predict the ED50 anticonvulsant activity of ringed-enaminones, in order to apply these relationships for the prediction of unknown open-chain compounds containing the same types of functional groups in their molecular structure. Two different modeling approaches are applied with the purpose of comparing the consistency of our results: (a) the search of molecular descriptors via multivariable linear regressions; and (b) the calculation of flexible descriptors with the CORAL (CORrelation And Logic) program. Among the results found, we propose some potent candidate open-chain enaminones having ED50 values lower than 10 mg·kg−1 for corresponding pharmacological studies. These compounds are classified as Class 1 and Class 2 according to the Anticonvulsant Selection Project. PMID:22272137

Imidazole derivatives as angiotensin II AT1 receptor blockers: Benchmarks, drug-like calculations and quantitative structure-activity relationships modeling

NASA Astrophysics Data System (ADS)

Alloui, Mebarka; Belaidi, Salah; Othmani, Hasna; Jaidane, Nejm-Eddine; Hochlaf, Majdi

2018-03-01

We performed benchmark studies on the molecular geometry, electron properties and vibrational analysis of imidazole using semi-empirical, density functional theory and post Hartree-Fock methods. These studies validated the use of AM1 for the treatment of larger systems. Then, we treated the structural, physical and chemical relationships for a series of imidazole derivatives acting as angiotensin II AT1 receptor blockers using AM1. QSAR studies were done for these imidazole derivatives using a combination of various physicochemical descriptors. A multiple linear regression procedure was used to design the relationships between molecular descriptor and the activity of imidazole derivatives. Results validate the derived QSAR model.

Predicting the activity of drugs for a group of imidazopyridine anticoccidial compounds.

PubMed

Si, Hongzong; Lian, Ning; Yuan, Shuping; Fu, Aiping; Duan, Yun-Bo; Zhang, Kejun; Yao, Xiaojun

2009-10-01

Gene expression programming (GEP) is a novel machine learning technique. The GEP is used to build nonlinear quantitative structure-activity relationship model for the prediction of the IC(50) for the imidazopyridine anticoccidial compounds. This model is based on descriptors which are calculated from the molecular structure. Four descriptors are selected from the descriptors' pool by heuristic method (HM) to build multivariable linear model. The GEP method produced a nonlinear quantitative model with a correlation coefficient and a mean error of 0.96 and 0.24 for the training set, 0.91 and 0.52 for the test set, respectively. It is shown that the GEP predicted results are in good agreement with experimental ones.

Quantum chemical studies on hypothetical Fischer type Mo(CO)5[C(OEt)Me] and Mo(CO)5[C(OMe)Et] carbene complexes

NASA Astrophysics Data System (ADS)

Gövdeli, Nezafet; Karakaş, Duran

2018-07-01

Quantum chemical calculations at B3LYP/LANL2DZ/6-31G(d) level were made on anti-eclipsed, anti-staggered, syn-eclipsed, syn-staggered conformers of hypothetical Fischer type Mo(CO)5[C(OEt)Me] and Mo(CO)5[C(OMe)Et] carbene complexes in the gas phase. The most stable conformer of the complexes was found to be anti-staggered according to the total energy values calculated at given level. Structural parameters, vibration spectra, charge distributions, molecular orbital energy diagrams, contour diagrams of frontier orbitals, molecular electrostatic potential maps and some electronic structure descriptors were obtained for the most stable conformers. NMR spectra of the most stable conformers were calculated at GIAO/B3LYP/LANL2DZ level. The most stable conformer geometry was found to be distorted octahedral. IR and NMR spectra of the complexes are consistent with their geometry. HOMOs of the complexes were found to be center-atomic character and LUMOs were carbene-carbon character. From the calculated charge analysis and molecular electrostatic potential maps, it is found that carbene-carbon acts as electrofil and metal center nucleophile. It is suggested that the catalytic properties of the carbene complexes may be due to the fact that the carbene-carbon behave as electrophile and metal center nucleophile. Some electronic structure descriptors of the complexes were calculated and the molecular properties were estimated.

Web-4D-QSAR: A web-based application to generate 4D-QSAR descriptors.

PubMed

Ataide Martins, João Paulo; Rougeth de Oliveira, Marco Antônio; Oliveira de Queiroz, Mário Sérgio

2018-06-05

A web-based application is developed to generate 4D-QSAR descriptors using the LQTA-QSAR methodology, based on molecular dynamics (MD) trajectories and topology information retrieved from the GROMACS package. The LQTAGrid module calculates the intermolecular interaction energies at each grid point, considering probes and all aligned conformations resulting from MD simulations. These interaction energies are the independent variables or descriptors employed in a QSAR analysis. A friendly front end web interface, built using the Django framework and Python programming language, integrates all steps of the LQTA-QSAR methodology in a way that is transparent to the user, and in the backend, GROMACS and LQTAGrid are executed to generate 4D-QSAR descriptors to be used later in the process of QSAR model building. © 2018 Wiley Periodicals, Inc. © 2018 Wiley Periodicals, Inc.

QSAR STUDY OF THE REDUCTION OF NITROAROMATICS BY FE (II) SPECIES

EPA Science Inventory

The development of predictive models for the reductive transformation of nitroaromatics requires further clarification of the effect of environmentally relevant variables on reaction kinetics and the identification of readily available molecular descriptors for calculating reacti...

Structure-activity relationships for serotonin transporter and dopamine receptor selectivity.

PubMed

Agatonovic-Kustrin, Snezana; Davies, Paul; Turner, Joseph V

2009-05-01

Antipsychotic medications have a diverse pharmacology with affinity for serotonergic, dopaminergic, adrenergic, histaminergic and cholinergic receptors. Their clinical use now also includes the treatment of mood disorders, thought to be mediated by serotonergic receptor activity. The aim of our study was to characterise the molecular properties of antipsychotic agents, and to develop a model that would indicate molecular specificity for the dopamine (D(2)) receptor and the serotonin (5-HT) transporter. Back-propagation artificial neural networks (ANNs) were trained on a dataset of 47 ligands categorically assigned antidepressant or antipsychotic utility. The structure of each compound was encoded with 63 calculated molecular descriptors. ANN parameters including hidden neurons and input descriptors were optimised based on sensitivity analyses, with optimum models containing between four and 14 descriptors. Predicted binding preferences were in excellent agreement with clinical antipsychotic or antidepressant utility. Validated models were further tested by use of an external prediction set of five drugs with unknown mechanism of action. The SAR models developed revealed the importance of simple molecular characteristics for differential binding to the D(2) receptor and the 5-HT transporter. These included molecular size and shape, solubility parameters, hydrogen donating potential, electrostatic parameters, stereochemistry and presence of nitrogen. The developed models and techniques employed are expected to be useful in the rational design of future therapeutic agents.

Lacosamide derivatives with anticonvulsant activity as carbonic anhydrase inhibitors. Molecular modeling, docking and QSAR analysis.

PubMed

Garro Martinez, Juan C; Vega-Hissi, Esteban G; Andrada, Matías F; Duchowicz, Pablo R; Torrens, Francisco; Estrada, Mario R

2014-01-01

Lacosamide is an anticonvulsant drug which presents carbonic anhydrase inhibition. In this paper, we analyzed the apparent relationship between both activities performing a molecular modeling, docking and QSAR studies on 18 lacosamide derivatives with known anticonvulsant activity. Docking results suggested the zinc-binding site of carbonic anhydrase is a possible target of lacosamide and lacosamide derivatives making favorable Van der Waals interactions with Asn67, Gln92, Phe131 and Thr200. The mathematical models revealed a poor relationship between the anticonvulsant activity and molecular descriptors obtained from DFT and docking calculations. However, a QSAR model was developed using Dragon software descriptors. The statistic parameters of the model are: correlation coefficient, R=0.957 and standard deviation, S=0.162. Our results provide new valuable information regarding the relationship between both activities and contribute important insights into the essential molecular requirements for the anticonvulsant activity.

Virtual lock-and-key approach: the in silico revival of Fischer model by means of molecular descriptors.

PubMed

Lauria, Antonino; Tutone, Marco; Almerico, Anna Maria

2011-09-01

In the last years the application of computational methodologies in the medicinal chemistry fields has found an amazing development. All the efforts were focused on the searching of new leads featuring a close affinity on a specific biological target. Thus, different molecular modeling approaches in simulation of molecular behavior for a specific biological target were employed. In spite of the increasing reliability of computational methodologies, not always the designed lead, once synthesized and screened, are suitable for the chosen biological target. To give another chance to these compounds, this work tries to resume the old concept of Fischer lock-and-key model. The same can be done for the "re-purposing" of old drugs. In fact, it is known that drugs may have many physiological targets, therefore it may be useful to identify them. This aspect, called "polypharmacology", is known to be therapeutically essential in the different treatments. The proposed protocol, the virtual lock-and-key approach (VLKA), consists in the "virtualization" of biological targets through the respectively known inhibitors. In order to release a real lock it is necessary the key fits the pins of the lock. The molecular descriptors could be considered as pins. A tested compound can be considered a potential inhibitor of a biological target if the values of its molecular descriptors fall in the calculated range values for the set of known inhibitors. The proposed protocol permits to transform a biological target in a "lock model" starting from its known inhibitors. To release a real lock all pins must fit. In the proposed protocol, it was supposed that the higher is the number of fit pins, the higher will be the affinity to the considered biological target. Therefore, each biological target was converted in a sequence of "weighted" molecular descriptor range values (locks) by using the structural features of the known inhibitors. Each biological target lock was tested by performing a molecular descriptors "fitting" on known inhibitors not used in the model construction (keys or test set). The results showed a good predictive capability of the protocol (confidence level 80%). This method gives interesting and convenient results because of the user-defined descriptors and biological targets choice in the process of new inhibitors discovery. Copyright © 2011 Elsevier Masson SAS. All rights reserved.

Novel dimer based descriptors with solvational computation for QSAR study of oxadiazoylbenzoyl-ureas as novel insect-growth regulators.

PubMed

Fan, Feng; Cheng, Jiagao; Li, Zhong; Xu, Xiaoyong; Qian, Xuhong

2010-02-01

Molecular aggregation state of bioactive compounds plays a key role in its bio-interactive procedure. In this article, based on the structure information of dimers, the simplest model of molecular aggregation state, and combined with solvational computation, total four descriptors (DeltaV, MR2, DeltaE(1), and DeltaE(2)) were calculated for QSAR study of a novel insect-growth regulator, N-(5-phenyl-1,3,4-oxadiazol-2-yl)-N'-benzoyl urea. Two QSAR models were constructed with r(2) = 0.671, q(2) = 0.516 and r(2) = 0.816, q(2) = 0.695, respectively. It implicates that the bioactivity may strongly depend on the characters of molecular aggregation state, especially on the dimeric transport ability from oil phase to water phase. Copyright 2009 Wiley Periodicals, Inc.

Introduction to molecular topology: basic concepts and application to drug design.

PubMed

Gálvez, Jorge; Gálvez-Llompart, María; García-Domenech, Ramón

2012-09-01

In this review it is dealt the use of molecular topology (MT) in the selection and design of new drugs. After an introduction of the actual methods used for drug design, the basic concepts of MT are defined, including examples of calculation of topological indices, which are numerical descriptors of molecular structures. The goal is making this calculation familiar to the potential students and allowing a straightforward comprehension of the topic. Finally, the achievements obtained in this field are detailed, so that the reader can figure out the great interest of this approach.

The great descriptor melting pot: mixing descriptors for the common good of QSAR models.

PubMed

Tseng, Yufeng J; Hopfinger, Anton J; Esposito, Emilio Xavier

2012-01-01

The usefulness and utility of QSAR modeling depends heavily on the ability to estimate the values of molecular descriptors relevant to the endpoints of interest followed by an optimized selection of descriptors to form the best QSAR models from a representative set of the endpoints of interest. The performance of a QSAR model is directly related to its molecular descriptors. QSAR modeling, specifically model construction and optimization, has benefited from its ability to borrow from other unrelated fields, yet the molecular descriptors that form QSAR models have remained basically unchanged in both form and preferred usage. There are many types of endpoints that require multiple classes of descriptors (descriptors that encode 1D through multi-dimensional, 4D and above, content) needed to most fully capture the molecular features and interactions that contribute to the endpoint. The advantages of QSAR models constructed from multiple, and different, descriptor classes have been demonstrated in the exploration of markedly different, and principally biological systems and endpoints. Multiple examples of such QSAR applications using different descriptor sets are described and that examined. The take-home-message is that a major part of the future of QSAR analysis, and its application to modeling biological potency, ADME-Tox properties, general use in virtual screening applications, as well as its expanding use into new fields for building QSPR models, lies in developing strategies that combine and use 1D through nD molecular descriptors.

QSAR analyses of conformationally restricted 1,5-diaryl pyrazoles as selective COX-2 inhibitors: application of connection table representation of ligands.

PubMed

Prasanna, S; Manivannan, E; Chaturvedi, S C

2005-04-15

As a part of our continuing efforts in discerning the structural and physicochemical requirements for selective COX-2 over COX-1 inhibition among the fused pyrazole ring systems, herein we report the QSAR analyses of the title compounds. The conformational flexibility of the title compounds was examined using a simple connection table representation. The conformational investigation was aided by calculating a connection table parameter called fraction of rotable bonds, b_rotR encompassing the number of rotable bonds and b_count, the number of bonds including implicit hydrogens of each ligand. The hydrophobic and steric correlation of the title compounds towards selective COX-2 inhibition was reported previously in one of our recent publications. In this communication, we attempt to calculate Wang-Ford charges of the non-hydrogen common atoms of AM1 optimized geometries of the title compounds. Owing to the partial conformational flexibility of title compounds, conformationally restricted and unrestricted descriptors were calculated from MOE. Correlation analysis of these 2D, 3D and Wang-Ford charges was accomplished by linear regression analysis. 2D molecular descriptor b_single, 3D molecular descriptors glob, std_dim3 showed significant contribution towards COX-2 inhibitory activity. Balaban J, a connectivity topological index showed a negative and positive contribution towards COX-1 and selective COX-2 over COX-1 inhibition, respectively. Wang-Ford charges calculated on C(7) showed a significant contribution towards COX-1 inhibitory activity whereas charges calculated on C(8) were crucial in governing the selectivity of COX-2 over COX-1 inhibition among these congeners.

EDULISS: a small-molecule database with data-mining and pharmacophore searching capabilities

PubMed Central

Hsin, Kun-Yi; Morgan, Hugh P.; Shave, Steven R.; Hinton, Andrew C.; Taylor, Paul; Walkinshaw, Malcolm D.

2011-01-01

We present the relational database EDULISS (EDinburgh University Ligand Selection System), which stores structural, physicochemical and pharmacophoric properties of small molecules. The database comprises a collection of over 4 million commercially available compounds from 28 different suppliers. A user-friendly web-based interface for EDULISS (available at http://eduliss.bch.ed.ac.uk/) has been established providing a number of data-mining possibilities. For each compound a single 3D conformer is stored along with over 1600 calculated descriptor values (molecular properties). A very efficient method for unique compound recognition, especially for a large scale database, is demonstrated by making use of small subgroups of the descriptors. Many of the shape and distance descriptors are held as pre-calculated bit strings permitting fast and efficient similarity and pharmacophore searches which can be used to identify families of related compounds for biological testing. Two ligand searching applications are given to demonstrate how EDULISS can be used to extract families of molecules with selected structural and biophysical features. PMID:21051336

Linear indices of the "molecular pseudograph's atom adjacency matrix": definition, significance-interpretation, and application to QSAR analysis of flavone derivatives as HIV-1 integrase inhibitors.

PubMed

Marrero-Ponce, Yovani

2004-01-01

This report describes a new set of molecular descriptors of relevance to QSAR/QSPR studies and drug design, atom linear indices fk(xi). These atomic level chemical descriptors are based on the calculation of linear maps on Rn[fk(xi): Rn--> Rn] in canonical basis. In this context, the kth power of the molecular pseudograph's atom adjacency matrix [Mk(G)] denotes the matrix of fk(xi) with respect to the canonical basis. In addition, a local-fragment (atom-type) formalism was developed. The kth atom-type linear indices are calculated by summing the kth atom linear indices of all atoms of the same atom type in the molecules. Moreover, total (whole-molecule) linear indices are also proposed. This descriptor is a linear functional (linear form) on Rn. That is, the kth total linear indices is a linear map from Rn to the scalar R[ fk(x): Rn --> R]. Thus, the kth total linear indices are calculated by summing the atom linear indices of all atoms in the molecule. The features of the kth total and local linear indices are illustrated by examples of various types of molecular structures, including chain-lengthening, branching, heteroatoms-content, and multiple bonds. Additionally, the linear independence of the local linear indices to other 0D, 1D, 2D, and 3D molecular descriptors is demonstrated by using principal component analysis for 42 very heterogeneous molecules. Much redundancy and overlapping was found among total linear indices and most of the other structural indices presently in use in the QSPR/QSAR practice. On the contrary, the information carried by atom-type linear indices was strikingly different from that codified in most of the 229 0D-3D molecular descriptors used in this study. It is concluded that the local linear indices are an independent indices containing important structural information to be used in QSPR/QSAR and drug design studies. In this sense, atom, atom-type, and total linear indices were used for the prediction of pIC50 values for the cleavage process of a set of flavone derivatives inhibitors of HIV-1 integrase. Quantitative models found are significant from a statistical point of view (R of 0.965, 0.902, and 0.927, respectively) and permit a clear interpretation of the studied properties in terms of the structural features of molecules. A LOO cross-validation procedure revealed that the regression models had a fairly good predictability (q2 of 0.679, 0.543, and 0.721, respectively). The comparison with other approaches reveals good behavior of the method proposed. The approach described in this paper appears to be an excellent alternative or guides for discovery and optimization of new lead compounds.

Relationship between reaction rate constants of organic pollutants and their molecular descriptors during Fenton oxidation and in situ formed ferric-oxyhydroxides.

PubMed

Jia, Lijuan; Shen, Zhemin; Su, Pingru

2016-05-01

Fenton oxidation is a promising water treatment method to degrade organic pollutants. In this study, 30 different organic compounds were selected and their reaction rate constants (k) were determined for the Fenton oxidation process. Gaussian09 and Material Studio software sets were used to carry out calculations and obtain values of 10 different molecular descriptors for each studied compound. Ferric-oxyhydroxide coagulation experiments were conducted to determine the coagulation percentage. Based upon the adsorption capacity, all of the investigated organic compounds were divided into two groups (Group A and Group B). The percentage adsorption of organic compounds in Group A was less than 15% (wt./wt.) and that in the Group B was higher than 15% (wt./wt.). For Group A, removal of the compounds by oxidation was the dominant process while for Group B, removal by both oxidation and coagulation (as a synergistic process) took place. Results showed that the relationship between the rate constants (k values) and the molecular descriptors of Group A was more pronounced than for Group B compounds. For the oxidation-dominated process, EHOMO and Fukui indices (f(0)x, f(-)x, f(+)x) were the most significant factors. The influence of bond order was more significant for the synergistic process of oxidation and coagulation than for the oxidation-dominated process. The influences of all other molecular descriptors on the synergistic process were weaker than on the oxidation-dominated process. Copyright © 2015. Published by Elsevier B.V.

Inductive electronegativity scale. Iterative calculation of inductive partial charges.

PubMed

Cherkasov, Artem

2003-01-01

A number of novel QSAR descriptors have been introduced on the basis of the previously elaborated models for steric and inductive effects. The developed "inductive" parameters include absolute and effective electronegativity, atomic partial charges, and local and global chemical hardness and softness. Being based on traditional inductive and steric substituent constants these 3D descriptors provide a valuable insight into intramolecular steric and electronic interactions and can find broad application in structure-activity studies. Possible interpretation of physical meaning of the inductive descriptors has been suggested by considering a neutral molecule as an electrical capacitor formed by charged atomic spheres. This approximation relates inductive chemical softness and hardness of bound atom(s) with the total area of the facings of electrical capacitor formed by the atom(s) and the rest of the molecule. The derived full electronegativity equalization scheme allows iterative calculation of inductive partial charges on the basis of atomic electronegativities, covalent radii, and intramolecular distances. A range of inductive descriptors has been computed for a variety of organic compounds. The calculated inductive charges in the studied molecules have been validated by experimental C-1s Electron Core Binding Energies and molecular dipole moments. Several semiempirical chemical rules, such as equalized electronegativity's arithmetic mean, principle of maximum hardness, and principle of hardness borrowing could be explicitly illustrated in the framework of the developed approach.

Spherical harmonics coefficients for ligand-based virtual screening of cyclooxygenase inhibitors.

PubMed

Wang, Quan; Birod, Kerstin; Angioni, Carlo; Grösch, Sabine; Geppert, Tim; Schneider, Petra; Rupp, Matthias; Schneider, Gisbert

2011-01-01

Molecular descriptors are essential for many applications in computational chemistry, such as ligand-based similarity searching. Spherical harmonics have previously been suggested as comprehensive descriptors of molecular structure and properties. We investigate a spherical harmonics descriptor for shape-based virtual screening. We introduce and validate a partially rotation-invariant three-dimensional molecular shape descriptor based on the norm of spherical harmonics expansion coefficients. Using this molecular representation, we parameterize molecular surfaces, i.e., isosurfaces of spatial molecular property distributions. We validate the shape descriptor in a comprehensive retrospective virtual screening experiment. In a prospective study, we virtually screen a large compound library for cyclooxygenase inhibitors, using a self-organizing map as a pre-filter and the shape descriptor for candidate prioritization. 12 compounds were tested in vitro for direct enzyme inhibition and in a whole blood assay. Active compounds containing a triazole scaffold were identified as direct cyclooxygenase-1 inhibitors. This outcome corroborates the usefulness of spherical harmonics for representation of molecular shape in virtual screening of large compound collections. The combination of pharmacophore and shape-based filtering of screening candidates proved to be a straightforward approach to finding novel bioactive chemotypes with minimal experimental effort.

Bond-based linear indices of the non-stochastic and stochastic edge-adjacency matrix. 1. Theory and modeling of ChemPhys properties of organic molecules.

PubMed

Marrero-Ponce, Yovani; Martínez-Albelo, Eugenio R; Casañola-Martín, Gerardo M; Castillo-Garit, Juan A; Echevería-Díaz, Yunaimy; Zaldivar, Vicente Romero; Tygat, Jan; Borges, José E Rodriguez; García-Domenech, Ramón; Torrens, Francisco; Pérez-Giménez, Facundo

2010-11-01

Novel bond-level molecular descriptors are proposed, based on linear maps similar to the ones defined in algebra theory. The kth edge-adjacency matrix (E(k)) denotes the matrix of bond linear indices (non-stochastic) with regard to canonical basis set. The kth stochastic edge-adjacency matrix, ES(k), is here proposed as a new molecular representation easily calculated from E(k). Then, the kth stochastic bond linear indices are calculated using ES(k) as operators of linear transformations. In both cases, the bond-type formalism is developed. The kth non-stochastic and stochastic total linear indices are calculated by adding the kth non-stochastic and stochastic bond linear indices, respectively, of all bonds in molecule. First, the new bond-based molecular descriptors (MDs) are tested for suitability, for the QSPRs, by analyzing regressions of novel indices for selected physicochemical properties of octane isomers (first round). General performance of the new descriptors in this QSPR studies is evaluated with regard to the well-known sets of 2D/3D MDs. From the analysis, we can conclude that the non-stochastic and stochastic bond-based linear indices have an overall good modeling capability proving their usefulness in QSPR studies. Later, the novel bond-level MDs are also used for the description and prediction of the boiling point of 28 alkyl-alcohols (second round), and to the modeling of the specific rate constant (log k), partition coefficient (log P), as well as the antibacterial activity of 34 derivatives of 2-furylethylenes (third round). The comparison with other approaches (edge- and vertices-based connectivity indices, total and local spectral moments, and quantum chemical descriptors as well as E-state/biomolecular encounter parameters) exposes a good behavior of our method in this QSPR studies. Finally, the approach described in this study appears to be a very promising structural invariant, useful not only for QSPR studies but also for similarity/diversity analysis and drug discovery protocols.

Quantum chemical and statistical study of megazol-derived compounds with trypanocidal activity

NASA Astrophysics Data System (ADS)

Rosselli, F. P.; Albuquerque, C. N.; da Silva, A. B. F.

In this work we performed a structure-activity relationship (SAR) study with the aim to correlate molecular properties of the megazol compound and 10 of its analogs with the biological activity against Trypanosoma cruzi (trypanocidal or antichagasic activity) presented by these molecules. The biological activity indication was obtained from in vitro tests and the molecular properties (variables or descriptors) were obtained from the optimized chemical structures by using the PM3 semiempirical method. It was calculated ˜80 molecular properties selected among steric, constitutional, electronic, and lipophilicity properties. In order to reduce dimensionality and investigate which subset of variables (descriptors) would be more effective in classifying the compounds studied, according to their degree of trypanocidal activity, we employed statistical methodologies (pattern recognition and classification techniques) such as principal component analysis (PCA), hierarchical cluster analysis (HCA), K-nearest neighbor (KNN), and discriminant function analysis (DFA). These methods showed that the descriptors molecular mass (MM), energy of the second lowest unoccupied molecular orbital (LUMO+1), charge on the first nitrogen at substituent 2 (qN'), dihedral angles (D1 and D2), bond length between atom C4 and its substituent (L4), Moriguchi octanol-partition coefficient (MLogP), and length-to-breadth ratio (L/Bw) were the variables responsible for the separation between active and inactive compounds against T. cruzi. Afterwards, the PCA, KNN, and DFA models built in this work were used to perform trypanocidal activity predictions for eight new megazol analog compounds.

PROFEAT Update: A Protein Features Web Server with Added Facility to Compute Network Descriptors for Studying Omics-Derived Networks.

PubMed

Zhang, P; Tao, L; Zeng, X; Qin, C; Chen, S Y; Zhu, F; Yang, S Y; Li, Z R; Chen, W P; Chen, Y Z

2017-02-03

The studies of biological, disease, and pharmacological networks are facilitated by the systems-level investigations using computational tools. In particular, the network descriptors developed in other disciplines have found increasing applications in the study of the protein, gene regulatory, metabolic, disease, and drug-targeted networks. Facilities are provided by the public web servers for computing network descriptors, but many descriptors are not covered, including those used or useful for biological studies. We upgraded the PROFEAT web server http://bidd2.nus.edu.sg/cgi-bin/profeat2016/main.cgi for computing up to 329 network descriptors and protein-protein interaction descriptors. PROFEAT network descriptors comprehensively describe the topological and connectivity characteristics of unweighted (uniform binding constants and molecular levels), edge-weighted (varying binding constants), node-weighted (varying molecular levels), edge-node-weighted (varying binding constants and molecular levels), and directed (oriented processes) networks. The usefulness of the network descriptors is illustrated by the literature-reported studies of the biological networks derived from the genome, interactome, transcriptome, metabolome, and diseasome profiles. Copyright © 2016 Elsevier Ltd. All rights reserved.

Improved Prediction of Blood-Brain Barrier Permeability Through Machine Learning with Combined Use of Molecular Property-Based Descriptors and Fingerprints.

PubMed

Yuan, Yaxia; Zheng, Fang; Zhan, Chang-Guo

2018-03-21

Blood-brain barrier (BBB) permeability of a compound determines whether the compound can effectively enter the brain. It is an essential property which must be accounted for in drug discovery with a target in the brain. Several computational methods have been used to predict the BBB permeability. In particular, support vector machine (SVM), which is a kernel-based machine learning method, has been used popularly in this field. For SVM training and prediction, the compounds are characterized by molecular descriptors. Some SVM models were based on the use of molecular property-based descriptors (including 1D, 2D, and 3D descriptors) or fragment-based descriptors (known as the fingerprints of a molecule). The selection of descriptors is critical for the performance of a SVM model. In this study, we aimed to develop a generally applicable new SVM model by combining all of the features of the molecular property-based descriptors and fingerprints to improve the accuracy for the BBB permeability prediction. The results indicate that our SVM model has improved accuracy compared to the currently available models of the BBB permeability prediction.

Developing descriptors to predict mechanical properties of nanotubes.

PubMed

Borders, Tammie L; Fonseca, Alexandre F; Zhang, Hengji; Cho, Kyeongjae; Rusinko, Andrew

2013-04-22

Descriptors and quantitative structure property relationships (QSPR) were investigated for mechanical property prediction of carbon nanotubes (CNTs). 78 molecular dynamics (MD) simulations were carried out, and 20 descriptors were calculated to build quantitative structure property relationships (QSPRs) for Young's modulus and Poisson's ratio in two separate analyses: vacancy only and vacancy plus methyl functionalization. In the first analysis, C(N2)/C(T) (number of non-sp2 hybridized carbons per the total carbons) and chiral angle were identified as critical descriptors for both Young's modulus and Poisson's ratio. Further analysis and literature findings indicate the effect of chiral angle is negligible at larger CNT radii for both properties. Raman spectroscopy can be used to measure C(N2)/C(T), providing a direct link between experimental and computational results. Poisson's ratio approaches two different limiting values as CNT radii increases: 0.23-0.25 for chiral and armchair CNTs and 0.10 for zigzag CNTs (surface defects <3%). In the second analysis, the critical descriptors were C(N2)/C(T), chiral angle, and M(N)/C(T) (number of methyl groups per total carbons). These results imply new types of defects can be represented as a new descriptor in QSPR models. Finally, results are qualified and quantified against experimental data.

Molecular Reactivity and Absorption Properties of Melanoidin Blue-G1 through Conceptual DFT.

PubMed

Frau, Juan; Glossman-Mitnik, Daniel

2018-03-02

This computational study presents the assessment of eleven density functionals that include CAM-B3LYP, LC-wPBE, M11, M11L, MN12L, MN12SX, N12, N12SX, wB97, wB97X and wB97XD related to the Def2TZVP basis sets together with the Solvation Model Density (SMD) solvation model in calculating the molecular properties and structure of the Blue-G1 intermediate melanoidin pigment. The chemical reactivity descriptors for the system are calculated via the conceptual Density Functional Theory (DFT). The choice of the active sites related to the nucleophilic, electrophilic, as well as radical attacks is made by linking them with the Fukui function indices, the electrophilic Parr functions and the condensed dual descriptor Δ f ( r ) . The prediction of the maximum absorption wavelength tends to be considerably accurate relative to its experimental value. The study found the MN12SX and N12SX density functionals to be the most appropriate density functionals in predicting the chemical reactivity of the studied molecule.

A SAR and QSAR study of new artemisinin compounds with antimalarial activity.

PubMed

Santos, Cleydson Breno R; Vieira, Josinete B; Lobato, Cleison C; Hage-Melim, Lorane I S; Souto, Raimundo N P; Lima, Clarissa S; Costa, Elizabeth V M; Brasil, Davi S B; Macêdo, Williams Jorge C; Carvalho, José Carlos T

2013-12-30

The Hartree-Fock method and the 6-31G** basis set were employed to calculate the molecular properties of artemisinin and 20 derivatives with antimalarial activity. Maps of molecular electrostatic potential (MEPs) and molecular docking were used to investigate the interaction between ligands and the receptor (heme). Principal component analysis and hierarchical cluster analysis were employed to select the most important descriptors related to activity. The correlation between biological activity and molecular properties was obtained using the partial least squares and principal component regression methods. The regression PLS and PCR models built in this study were also used to predict the antimalarial activity of 30 new artemisinin compounds with unknown activity. The models obtained showed not only statistical significance but also predictive ability. The significant molecular descriptors related to the compounds with antimalarial activity were the hydration energy (HE), the charge on the O11 oxygen atom (QO11), the torsion angle O1-O2-Fe-N2 (D2) and the maximum rate of R/Sanderson Electronegativity (RTe+). These variables led to a physical and structural explanation of the molecular properties that should be selected for when designing new ligands to be used as antimalarial agents.

Quantitative structure-property relationship analysis for the retention index of fragrance-like compounds on a polar stationary phase.

PubMed

Rojas, Cristian; Duchowicz, Pablo R; Tripaldi, Piercosimo; Pis Diez, Reinaldo

2015-11-27

A quantitative structure-property relationship (QSPR) was developed for modeling the retention index of 1184 flavor and fragrance compounds measured using a Carbowax 20M glass capillary gas chromatography column. The 4885 molecular descriptors were calculated using Dragon software, and then were simultaneously analyzed through multivariable linear regression analysis using the replacement method (RM) variable subset selection technique. We proceeded in three steps, the first one by considering all descriptor blocks, the second one by excluding conformational descriptor blocks, and the last one by analyzing only 3D-descriptor families. The models were validated through an external test set of compounds. Cross-validation methods such as leave-one-out and leave-many-out were applied, together with Y-randomization and applicability domain analysis. The developed model was used to estimate the I of a set of 22 molecules. The results clearly suggest that 3D-descriptors do not offer relevant information for modeling the retention index, while a topological index such as the Randić-like index from reciprocal squared distance matrix has a high relevance for this purpose. Copyright © 2015 Elsevier B.V. All rights reserved.

Physicochemical Parameters Affecting the Electrospray Ionization Efficiency of Amino Acids after Acylation

PubMed Central

2017-01-01

Electrospray ionization (ESI) is widely used in liquid chromatography coupled to mass spectrometry (LC–MS) for the analysis of biomolecules. However, the ESI process is still not completely understood, and it is often a matter of trial and error to enhance ESI efficiency and, hence, the response of a given set of compounds. In this work we performed a systematic study of the ESI response of 14 amino acids that were acylated with organic acid anhydrides of increasing chain length and with poly(ethylene glycol) (PEG) changing certain physicochemical properties in a predictable manner. By comparing the ESI response of 70 derivatives, we found that there was a strong correlation between the calculated molecular volume and the ESI response, while correlation with hydrophobicity (log P values), pKa, and the inverse calculated surface tension was significantly lower although still present, especially for individual derivatized amino acids with increasing acyl chain lengths. Acylation with PEG containing five ethylene glycol units led to the largest gain in ESI response. This response was maximal independent of the calculated physicochemical properties or the type of amino acid. Since no actual physicochemical data is available for most derivatized compounds, the responses were also used as input for a quantitative structure–property relationship (QSPR) model to find the best physicochemical descriptors relating to the ESI response from molecular structures using the amino acids and their derivatives as a reference set. A topological descriptor related to molecular size (SPAN) was isolated next to a descriptor related to the atomic composition and structural groups (BIC0). The validity of the model was checked with a test set of 43 additional compounds that were unrelated to amino acids. While prediction was generally good (R2 > 0.9), compounds containing halogen atoms or nitro groups gave a lower predicted ESI response. PMID:28737384

Modeling complex metabolic reactions, ecological systems, and financial and legal networks with MIANN models based on Markov-Wiener node descriptors.

PubMed

Duardo-Sánchez, Aliuska; Munteanu, Cristian R; Riera-Fernández, Pablo; López-Díaz, Antonio; Pazos, Alejandro; González-Díaz, Humberto

2014-01-27

The use of numerical parameters in Complex Network analysis is expanding to new fields of application. At a molecular level, we can use them to describe the molecular structure of chemical entities, protein interactions, or metabolic networks. However, the applications are not restricted to the world of molecules and can be extended to the study of macroscopic nonliving systems, organisms, or even legal or social networks. On the other hand, the development of the field of Artificial Intelligence has led to the formulation of computational algorithms whose design is based on the structure and functioning of networks of biological neurons. These algorithms, called Artificial Neural Networks (ANNs), can be useful for the study of complex networks, since the numerical parameters that encode information of the network (for example centralities/node descriptors) can be used as inputs for the ANNs. The Wiener index (W) is a graph invariant widely used in chemoinformatics to quantify the molecular structure of drugs and to study complex networks. In this work, we explore for the first time the possibility of using Markov chains to calculate analogues of node distance numbers/W to describe complex networks from the point of view of their nodes. These parameters are called Markov-Wiener node descriptors of order k(th) (W(k)). Please, note that these descriptors are not related to Markov-Wiener stochastic processes. Here, we calculated the W(k)(i) values for a very high number of nodes (>100,000) in more than 100 different complex networks using the software MI-NODES. These networks were grouped according to the field of application. Molecular networks include the Metabolic Reaction Networks (MRNs) of 40 different organisms. In addition, we analyzed other biological and legal and social networks. These include the Interaction Web Database Biological Networks (IWDBNs), with 75 food webs or ecological systems and the Spanish Financial Law Network (SFLN). The calculated W(k)(i) values were used as inputs for different ANNs in order to discriminate correct node connectivity patterns from incorrect random patterns. The MIANN models obtained present good values of Sensitivity/Specificity (%): MRNs (78/78), IWDBNs (90/88), and SFLN (86/84). These preliminary results are very promising from the point of view of a first exploratory study and suggest that the use of these models could be extended to the high-throughput re-evaluation of connectivity in known complex networks (collation).

MultiDK: A Multiple Descriptor Multiple Kernel Approach for Molecular Discovery and Its Application to Organic Flow Battery Electrolytes.

PubMed

Kim, Sungjin; Jinich, Adrián; Aspuru-Guzik, Alán

2017-04-24

We propose a multiple descriptor multiple kernel (MultiDK) method for efficient molecular discovery using machine learning. We show that the MultiDK method improves both the speed and accuracy of molecular property prediction. We apply the method to the discovery of electrolyte molecules for aqueous redox flow batteries. Using multiple-type-as opposed to single-type-descriptors, we obtain more relevant features for machine learning. Following the principle of "wisdom of the crowds", the combination of multiple-type descriptors significantly boosts prediction performance. Moreover, by employing multiple kernels-more than one kernel function for a set of the input descriptors-MultiDK exploits nonlinear relations between molecular structure and properties better than a linear regression approach. The multiple kernels consist of a Tanimoto similarity kernel and a linear kernel for a set of binary descriptors and a set of nonbinary descriptors, respectively. Using MultiDK, we achieve an average performance of r 2 = 0.92 with a test set of molecules for solubility prediction. We also extend MultiDK to predict pH-dependent solubility and apply it to a set of quinone molecules with different ionizable functional groups to assess their performance as flow battery electrolytes.

Quantitative structure-toxicity relationship (QSTR) studies on the organophosphate insecticides.

PubMed

Can, Alper

2014-11-04

Organophosphate insecticides are the most commonly used pesticides in the world. In this study, quantitative structure-toxicity relationship (QSTR) models were derived for estimating the acute oral toxicity of organophosphate insecticides to male rats. The 20 chemicals of the training set and the seven compounds of the external testing set were described by means of using descriptors. Descriptors for lipophilicity, polarity and molecular geometry, as well as quantum chemical descriptors for energy were calculated. Model development to predict toxicity of organophosphate insecticides in different matrices was carried out using multiple linear regression. The model was validated internally and externally. In the present study, QSTR model was used for the first time to understand the inherent relationships between the organophosphate insecticide molecules and their toxicity behavior. Such studies provide mechanistic insight about structure-toxicity relationship and help in the design of less toxic insecticides. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Bond Ellipticity Alternation: An Accurate Descriptor of the Nonlinear Optical Properties of π-Conjugated Chromophores.

PubMed

Lopes, Thiago O; Machado, Daniel F Scalabrini; Risko, Chad; Brédas, Jean-Luc; de Oliveira, Heibbe C B

2018-03-15

Well-defined structure-property relationships offer a conceptual basis to afford a priori design principles to develop novel π-conjugated molecular and polymer materials for nonlinear optical (NLO) applications. Here, we introduce the bond ellipticity alternation (BEA) as a robust parameter to assess the NLO characteristics of organic chromophores and illustrate its effectiveness in the case of streptocyanines. BEA is based on the symmetry of the electron density, a physical observable that can be determined from experimental X-ray electron densities or from quantum-chemical calculations. Through comparisons to the well-established bond-length alternation and π-bond order alternation parameters, we demonstrate the generality of BEA to foreshadow NLO characteristics and underline that, in the case of large electric fields, BEA is a more reliable descriptor. Hence, this study introduces BEA as a prominent descriptor of organic chromophores of interest for NLO applications.

Prediction of the Fate of Organic Compounds in the Environment From Their Molecular Properties: A Review

PubMed Central

Mamy, Laure; Patureau, Dominique; Barriuso, Enrique; Bedos, Carole; Bessac, Fabienne; Louchart, Xavier; Martin-laurent, Fabrice; Miege, Cecile; Benoit, Pierre

2015-01-01

A comprehensive review of quantitative structure-activity relationships (QSAR) allowing the prediction of the fate of organic compounds in the environment from their molecular properties was done. The considered processes were water dissolution, dissociation, volatilization, retention on soils and sediments (mainly adsorption and desorption), degradation (biotic and abiotic), and absorption by plants. A total of 790 equations involving 686 structural molecular descriptors are reported to estimate 90 environmental parameters related to these processes. A significant number of equations was found for dissociation process (pKa), water dissolution or hydrophobic behavior (especially through the KOW parameter), adsorption to soils and biodegradation. A lack of QSAR was observed to estimate desorption or potential of transfer to water. Among the 686 molecular descriptors, five were found to be dominant in the 790 collected equations and the most generic ones: four quantum-chemical descriptors, the energy of the highest occupied molecular orbital (EHOMO) and the energy of the lowest unoccupied molecular orbital (ELUMO), polarizability (α) and dipole moment (μ), and one constitutional descriptor, the molecular weight. Keeping in mind that the combination of descriptors belonging to different categories (constitutional, topological, quantum-chemical) led to improve QSAR performances, these descriptors should be considered for the development of new QSAR, for further predictions of environmental parameters. This review also allows finding of the relevant QSAR equations to predict the fate of a wide diversity of compounds in the environment. PMID:25866458

Prediction of the Fate of Organic Compounds in the Environment From Their Molecular Properties: A Review.

PubMed

Mamy, Laure; Patureau, Dominique; Barriuso, Enrique; Bedos, Carole; Bessac, Fabienne; Louchart, Xavier; Martin-Laurent, Fabrice; Miege, Cecile; Benoit, Pierre

2015-06-18

A comprehensive review of quantitative structure-activity relationships (QSAR) allowing the prediction of the fate of organic compounds in the environment from their molecular properties was done. The considered processes were water dissolution, dissociation, volatilization, retention on soils and sediments (mainly adsorption and desorption), degradation (biotic and abiotic), and absorption by plants. A total of 790 equations involving 686 structural molecular descriptors are reported to estimate 90 environmental parameters related to these processes. A significant number of equations was found for dissociation process (pK a ), water dissolution or hydrophobic behavior (especially through the K OW parameter), adsorption to soils and biodegradation. A lack of QSAR was observed to estimate desorption or potential of transfer to water. Among the 686 molecular descriptors, five were found to be dominant in the 790 collected equations and the most generic ones: four quantum-chemical descriptors, the energy of the highest occupied molecular orbital (E HOMO ) and the energy of the lowest unoccupied molecular orbital (E LUMO ), polarizability (α) and dipole moment (μ), and one constitutional descriptor, the molecular weight. Keeping in mind that the combination of descriptors belonging to different categories (constitutional, topological, quantum-chemical) led to improve QSAR performances, these descriptors should be considered for the development of new QSAR, for further predictions of environmental parameters. This review also allows finding of the relevant QSAR equations to predict the fate of a wide diversity of compounds in the environment.

Convenient QSAR model for predicting the complexation of structurally diverse compounds with beta-cyclodextrins.

PubMed

Pérez-Garrido, Alfonso; Morales Helguera, Aliuska; Abellán Guillén, Adela; Cordeiro, M Natália D S; Garrido Escudero, Amalio

2009-01-15

This paper reports a QSAR study for predicting the complexation of a large and heterogeneous variety of substances (233 organic compounds) with beta-cyclodextrins (beta-CDs). Several different theoretical molecular descriptors, calculated solely from the molecular structure of the compounds under investigation, and an efficient variable selection procedure, like the Genetic Algorithm, led to models with satisfactory global accuracy and predictivity. But the best-final QSAR model is based on Topological descriptors meanwhile offering a reasonable interpretation. This QSAR model was able to explain ca. 84% of the variance in the experimental activity, and displayed very good internal cross-validation statistics and predictivity on external data. It shows that the driving forces for CD complexation are mainly hydrophobic and steric (van der Waals) interactions. Thus, the results of our study provide a valuable tool for future screening and priority testing of beta-CDs guest molecules.

Chemical graphs, molecular matrices and topological indices in chemoinformatics and quantitative structure-activity relationships.

PubMed

Ivanciuc, Ovidiu

2013-06-01

Chemical and molecular graphs have fundamental applications in chemoinformatics, quantitative structureproperty relationships (QSPR), quantitative structure-activity relationships (QSAR), virtual screening of chemical libraries, and computational drug design. Chemoinformatics applications of graphs include chemical structure representation and coding, database search and retrieval, and physicochemical property prediction. QSPR, QSAR and virtual screening are based on the structure-property principle, which states that the physicochemical and biological properties of chemical compounds can be predicted from their chemical structure. Such structure-property correlations are usually developed from topological indices and fingerprints computed from the molecular graph and from molecular descriptors computed from the three-dimensional chemical structure. We present here a selection of the most important graph descriptors and topological indices, including molecular matrices, graph spectra, spectral moments, graph polynomials, and vertex topological indices. These graph descriptors are used to define several topological indices based on molecular connectivity, graph distance, reciprocal distance, distance-degree, distance-valency, spectra, polynomials, and information theory concepts. The molecular descriptors and topological indices can be developed with a more general approach, based on molecular graph operators, which define a family of graph indices related by a common formula. Graph descriptors and topological indices for molecules containing heteroatoms and multiple bonds are computed with weighting schemes based on atomic properties, such as the atomic number, covalent radius, or electronegativity. The correlation in QSPR and QSAR models can be improved by optimizing some parameters in the formula of topological indices, as demonstrated for structural descriptors based on atomic connectivity and graph distance.

Bio-AIMS Collection of Chemoinformatics Web Tools based on Molecular Graph Information and Artificial Intelligence Models.

PubMed

Munteanu, Cristian R; Gonzalez-Diaz, Humberto; Garcia, Rafael; Loza, Mabel; Pazos, Alejandro

2015-01-01

The molecular information encoding into molecular descriptors is the first step into in silico Chemoinformatics methods in Drug Design. The Machine Learning methods are a complex solution to find prediction models for specific biological properties of molecules. These models connect the molecular structure information such as atom connectivity (molecular graphs) or physical-chemical properties of an atom/group of atoms to the molecular activity (Quantitative Structure - Activity Relationship, QSAR). Due to the complexity of the proteins, the prediction of their activity is a complicated task and the interpretation of the models is more difficult. The current review presents a series of 11 prediction models for proteins, implemented as free Web tools on an Artificial Intelligence Model Server in Biosciences, Bio-AIMS (http://bio-aims.udc.es/TargetPred.php). Six tools predict protein activity, two models evaluate drug - protein target interactions and the other three calculate protein - protein interactions. The input information is based on the protein 3D structure for nine models, 1D peptide amino acid sequence for three tools and drug SMILES formulas for two servers. The molecular graph descriptor-based Machine Learning models could be useful tools for in silico screening of new peptides/proteins as future drug targets for specific treatments.

Molecular structure, spectroscopic and docking analysis of 1,3-diphenylpyrazole-4-propionic acid: A good prostaglandin reductase inhibitor

NASA Astrophysics Data System (ADS)

Kavitha, T.; Velraj, G.

2018-03-01

The molecule 1,3-diphenylpyrazole-4-propionic acid (DPPA) was optimized to its minimum energy level using density functional theory (DFT) calculations. The vibrational frequencies of DPPA were calculated along with their potential energy distribution (PED) and the obtained values are validated with the help of experimental calculations. The reactivity nature of the molecule was investigated with the aid of various DFT methods such as global reactivity descriptors, local reactivity descriptors, molecular electrostatic potential (MEP), natural bond orbitals (NBOs), etc. The prediction of activity spectra for substances (PASS) result forecast that, DPPA can be more active as a prostaglandin (PG) reductase inhibitor. The PGs are biologically synthesized by the cyclooxygenase (COX) enzyme which exists in COX1 and COX2 forms. The PGs produced by COX2 enzyme induces inflammation and fungal infections and hence the inhibition of COX2 enzyme is indispensable in anti-inflammation and anti-fungal activities. The docking analysis of DPPA with COX enzymes (both COX1 and COX2) were carried out and eventually, it was found that DPPA can selectively inhibit COX2 enzyme and can serve as a PG reductase inhibitor thereby acting as a lead compound for the treatment of inflammation and fungal diseases.

Mining chemical reactions using neighborhood behavior and condensed graphs of reactions approaches.

PubMed

de Luca, Aurélie; Horvath, Dragos; Marcou, Gilles; Solov'ev, Vitaly; Varnek, Alexandre

2012-09-24

This work addresses the problem of similarity search and classification of chemical reactions using Neighborhood Behavior (NB) and Condensed Graphs of Reaction (CGR) approaches. The CGR formalism represents chemical reactions as a classical molecular graph with dynamic bonds, enabling descriptor calculations on this graph. Different types of the ISIDA fragment descriptors generated for CGRs in combination with two metrics--Tanimoto and Euclidean--were considered as chemical spaces, to serve for reaction dissimilarity scoring. The NB method has been used to select an optimal combination of descriptors which distinguish different types of chemical reactions in a database containing 8544 reactions of 9 classes. Relevance of NB analysis has been validated in generic (multiclass) similarity search and in clustering with Self-Organizing Maps (SOM). NB-compliant sets of descriptors were shown to display enhanced mapping propensities, allowing the construction of better Self-Organizing Maps and similarity searches (NB and classical similarity search criteria--AUC ROC--correlate at a level of 0.7). The analysis of the SOM clusters proved chemically meaningful CGR substructures representing specific reaction signatures.

Redox biotransformation and delivery of anthracycline anticancer antibiotics: How interpretable structure-activity relationships of lethality using electrophilicity and the London formula for dispersion interaction work.

PubMed

Pang, Siu-Kwong

2017-03-30

Quantum chemical methods and molecular mechanics approaches face a lot of challenges in drug metabolism study because of their either insufficient accuracy or huge computational cost, or lack of clear molecular level pictures for building computational models. Low-cost QSAR methods can often be carried out even though molecular level pictures are not well defined; however, they show difficulty in identifying the mechanisms of drug metabolism and delineating the effects of chemical structures on drug toxicity because a certain amount of molecular descriptors are difficult to be interpreted. In order to make a breakthrough, it was proposed that mechanistically interpretable molecular descriptors were used to correlate with biological activity to establish structure-activity plots. The mechanistically interpretable molecular descriptors used in this study include electrophilicity and the mathematical function in the London formula for dispersion interaction, and they were calculated using quantum chemical methods. The biological activity is the lethality of anthracycline anticancer antibiotics denoted as log LD50, which were obtained by intraperitoneal injection into mice. The results reveal that the plots for electrophilicity, which can be interpreted as redox reactivity of anthracyclines, can describe oxidative degradation for detoxification and reductive bioactivation for toxicity induction. The plots for the dispersion interaction function, which represent the attraction between anthracyclines and biomolecules, can describe efflux from and influx into target cells of toxicity. The plots can also identify three structural scaffolds of anthracyclines that have different metabolic pathways, resulting in their different toxicity behavior. This structure-dependent toxicity behavior revealed in the plots can provide perspectives on design of anthracycline anticancer antibiotics. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

A quantitative structure-activity relationship to predict efficacy of granular activated carbon adsorption to control emerging contaminants.

PubMed

Kennicutt, A R; Morkowchuk, L; Krein, M; Breneman, C M; Kilduff, J E

2016-08-01

A quantitative structure-activity relationship was developed to predict the efficacy of carbon adsorption as a control technology for endocrine-disrupting compounds, pharmaceuticals, and components of personal care products, as a tool for water quality professionals to protect public health. Here, we expand previous work to investigate a broad spectrum of molecular descriptors including subdivided surface areas, adjacency and distance matrix descriptors, electrostatic partial charges, potential energy descriptors, conformation-dependent charge descriptors, and Transferable Atom Equivalent (TAE) descriptors that characterize the regional electronic properties of molecules. We compare the efficacy of linear (Partial Least Squares) and non-linear (Support Vector Machine) machine learning methods to describe a broad chemical space and produce a user-friendly model. We employ cross-validation, y-scrambling, and external validation for quality control. The recommended Support Vector Machine model trained on 95 compounds having 23 descriptors offered a good balance between good performance statistics, low error, and low probability of over-fitting while describing a wide range of chemical features. The cross-validated model using a log-uptake (qe) response calculated at an aqueous equilibrium concentration (Ce) of 1 μM described the training dataset with an r(2) of 0.932, had a cross-validated r(2) of 0.833, and an average residual of 0.14 log units.

Anticorrosive Effects of Some Thiophene Derivatives Against the Corrosion of Iron: A Computational Study

NASA Astrophysics Data System (ADS)

Guo, Lei; Safi, Zaki S.; Kaya, Savas; Shi, Wei; Tüzün, Burak; Altunay, Nail; Kaya, Cemal

2018-05-01

It is known that iron is one of the most widely used metals in industrial production. In this work, the inhibition performances of three thiophene derivatives on the corrosion of iron were investigated in the light of several theoretical approaches. In the section including DFT calculations, several global reactivity descriptors such as EHOMO, ELUMO, ionization energy (I), electron affinity (A), HOMO-LUMO energy gap (ΔE), chemical hardness (η), softness (σ), as well as local reactivity descriptors like Fukui indices, local softness, and local electrophilicity were considered and discussed. The adsorption behaviors of considered thiophene derivatives on Fe(110) surface were investigated using molecular dynamics simulation approach. To determine the most active corrosion inhibitor among studied thiophene derivatives, we used the principle component analysis (PCA) and agglomerative hierarchical cluster analysis (AHCA). Accordingly, all data obtained using various theoretical calculation techniques are consistent with experiments.

Determination of Abraham model solute descriptors for the monomeric and dimeric forms of trans-cinnamic acid using measured solubilities from the Open Notebook Science Challenge.

PubMed

Bradley, Jean-Claude; Abraham, Michael H; Acree, William E; Lang, Andrew Sid; Beck, Samantha N; Bulger, David A; Clark, Elizabeth A; Condron, Lacey N; Costa, Stephanie T; Curtin, Evan M; Kurtu, Sozit B; Mangir, Mark I; McBride, Matthew J

2015-01-01

Calculating Abraham descriptors from solubility values requires that the solute have the same form when dissolved in all solvents. However, carboxylic acids can form dimers when dissolved in non-polar solvents. For such compounds Abraham descriptors can be calculated for both the monomeric and dimeric forms by treating the polar and non-polar systems separately. We illustrate the method of how this can be done by calculating the Abraham descriptors for both the monomeric and dimeric forms of trans-cinnamic acid, the first time that descriptors for a carboxylic acid dimer have been obtained. Abraham descriptors were calculated for the monomeric form of trans-cinnamic acid using experimental solubility measurements in polar solvents from the Open Notebook Science Challenge together with a number of water-solvent partition coefficients from the literature. Similarly, experimental solubility measurements in non-polar solvents were used to determine Abraham descriptors for the trans-cinnamic acid dimer. Abraham descriptors were calculated for both the monomeric and dimeric forms of trans-cinnamic acid. This allows for the prediction of further solubilities of trans-cinnamic acid in both polar and non-polar solvents with an error of about 0.10 log units. Graphical abstractMolar concentration of trans-cinnamic acid in various polar and non-polar solvents.

How diverse are diversity assessment methods? A comparative analysis and benchmarking of molecular descriptor space.

PubMed

Koutsoukas, Alexios; Paricharak, Shardul; Galloway, Warren R J D; Spring, David R; Ijzerman, Adriaan P; Glen, Robert C; Marcus, David; Bender, Andreas

2014-01-27

Chemical diversity is a widely applied approach to select structurally diverse subsets of molecules, often with the objective of maximizing the number of hits in biological screening. While many methods exist in the area, few systematic comparisons using current descriptors in particular with the objective of assessing diversity in bioactivity space have been published, and this shortage is what the current study is aiming to address. In this work, 13 widely used molecular descriptors were compared, including fingerprint-based descriptors (ECFP4, FCFP4, MACCS keys), pharmacophore-based descriptors (TAT, TAD, TGT, TGD, GpiDAPH3), shape-based descriptors (rapid overlay of chemical structures (ROCS) and principal moments of inertia (PMI)), a connectivity-matrix-based descriptor (BCUT), physicochemical-property-based descriptors (prop2D), and a more recently introduced molecular descriptor type (namely, "Bayes Affinity Fingerprints"). We assessed both the similar behavior of the descriptors in assessing the diversity of chemical libraries, and their ability to select compounds from libraries that are diverse in bioactivity space, which is a property of much practical relevance in screening library design. This is particularly evident, given that many future targets to be screened are not known in advance, but that the library should still maximize the likelihood of containing bioactive matter also for future screening campaigns. Overall, our results showed that descriptors based on atom topology (i.e., fingerprint-based descriptors and pharmacophore-based descriptors) correlate well in rank-ordering compounds, both within and between descriptor types. On the other hand, shape-based descriptors such as ROCS and PMI showed weak correlation with the other descriptors utilized in this study, demonstrating significantly different behavior. We then applied eight of the molecular descriptors compared in this study to sample a diverse subset of sample compounds (4%) from an initial population of 2587 compounds, covering the 25 largest human activity classes from ChEMBL and measured the coverage of activity classes by the subsets. Here, it was found that "Bayes Affinity Fingerprints" achieved an average coverage of 92% of activity classes. Using the descriptors ECFP4, GpiDAPH3, TGT, and random sampling, 91%, 84%, 84%, and 84% of the activity classes were represented in the selected compounds respectively, followed by BCUT, prop2D, MACCS, and PMI (in order of decreasing performance). In addition, we were able to show that there is no visible correlation between compound diversity in PMI space and in bioactivity space, despite frequent utilization of PMI plots to this end. To summarize, in this work, we assessed which descriptors select compounds with high coverage of bioactivity space, and can hence be used for diverse compound selection for biological screening. In cases where multiple descriptors are to be used for diversity selection, this work describes which descriptors behave complementarily, and can hence be used jointly to focus on different aspects of diversity in chemical space.

Predicting p Ka values from EEM atomic charges

PubMed Central

2013-01-01

The acid dissociation constant p Ka is a very important molecular property, and there is a strong interest in the development of reliable and fast methods for p Ka prediction. We have evaluated the p Ka prediction capabilities of QSPR models based on empirical atomic charges calculated by the Electronegativity Equalization Method (EEM). Specifically, we collected 18 EEM parameter sets created for 8 different quantum mechanical (QM) charge calculation schemes. Afterwards, we prepared a training set of 74 substituted phenols. Additionally, for each molecule we generated its dissociated form by removing the phenolic hydrogen. For all the molecules in the training set, we then calculated EEM charges using the 18 parameter sets, and the QM charges using the 8 above mentioned charge calculation schemes. For each type of QM and EEM charges, we created one QSPR model employing charges from the non-dissociated molecules (three descriptor QSPR models), and one QSPR model based on charges from both dissociated and non-dissociated molecules (QSPR models with five descriptors). Afterwards, we calculated the quality criteria and evaluated all the QSPR models obtained. We found that QSPR models employing the EEM charges proved as a good approach for the prediction of p Ka (63% of these models had R2 > 0.9, while the best had R2 = 0.924). As expected, QM QSPR models provided more accurate p Ka predictions than the EEM QSPR models but the differences were not significant. Furthermore, a big advantage of the EEM QSPR models is that their descriptors (i.e., EEM atomic charges) can be calculated markedly faster than the QM charge descriptors. Moreover, we found that the EEM QSPR models are not so strongly influenced by the selection of the charge calculation approach as the QM QSPR models. The robustness of the EEM QSPR models was subsequently confirmed by cross-validation. The applicability of EEM QSPR models for other chemical classes was illustrated by a case study focused on carboxylic acids. In summary, EEM QSPR models constitute a fast and accurate p Ka prediction approach that can be used in virtual screening. PMID:23574978

In silico quantitative structure-toxicity relationship study of aromatic nitro compounds.

PubMed

Pasha, Farhan Ahmad; Neaz, Mohammad Morshed; Cho, Seung Joo; Ansari, Mohiuddin; Mishra, Sunil Kumar; Tiwari, Sharvan

2009-05-01

Small molecules often have toxicities that are a function of molecular structural features. Minor variations in structural features can make large difference in such toxicity. Consequently, in silico techniques may be used to correlate such molecular toxicities with their structural features. Relative to nine different sets of aromatic nitro compounds having known observed toxicities against different targets, we developed ligand-based 2D quantitative structure-toxicity relationship models using 20 selected topological descriptors. The topological descriptors have several advantages such as conformational independency, facile and less time-consuming computation to yield good results. Multiple linear regression analysis was used to correlate variations of toxicity with molecular properties. The information index on molecular size, lopping centric index and Kier flexibility index were identified as fundamental descriptors for different kinds of toxicity, and further showed that molecular size, branching and molecular flexibility might be particularly important factors in quantitative structure-toxicity relationship analysis. This study revealed that topological descriptor-guided quantitative structure-toxicity relationship provided a very useful, cost and time-efficient, in silico tool for describing small-molecule toxicities.

Artificial neural networks and the study of the psychoactivity of cannabinoid compounds.

PubMed

Honório, Káthia M; de Lima, Emmanuela F; Quiles, Marcos G; Romero, Roseli A F; Molfetta, Fábio A; da Silva, Albérico B F

2010-06-01

Cannabinoid compounds have widely been employed because of its medicinal and psychotropic properties. These compounds are isolated from Cannabis sativa (or marijuana) and are used in several medical treatments, such as glaucoma, nausea associated to chemotherapy, pain and many other situations. More recently, its use as appetite stimulant has been indicated in patients with cachexia or AIDS. In this work, the influence of several molecular descriptors on the psychoactivity of 50 cannabinoid compounds is analyzed aiming one obtain a model able to predict the psychoactivity of new cannabinoids. For this purpose, initially, the selection of descriptors was carried out using the Fisher's weight, the correlation matrix among the calculated variables and principal component analysis. From these analyses, the following descriptors have been considered more relevant: E(LUMO) (energy of the lowest unoccupied molecular orbital), Log P (logarithm of the partition coefficient), VC4 (volume of the substituent at the C4 position) and LP1 (Lovasz-Pelikan index, a molecular branching index). To follow, two neural network models were used to construct a more adequate model for classifying new cannabinoid compounds. The first model employed was multi-layer perceptrons, with algorithm back-propagation, and the second model used was the Kohonen network. The results obtained from both networks were compared and showed that both techniques presented a high percentage of correctness to discriminate psychoactive and psychoinactive compounds. However, the Kohonen network was superior to multi-layer perceptrons.

Correlations between chromatographic parameters and bioactivity predictors of potential herbicides.

PubMed

Janicka, Małgorzata

2014-08-01

Different liquid chromatography techniques, including reversed-phase liquid chromatography on Purosphere RP-18e, IAM.PC.DD2 and Cosmosil Cholester columns and micellar liqud chromatography with a Purosphere RP-8e column and using buffered sodium dodecyl sulfate-acetonitrile as the mobile phase, were applied to study the lipophilic properties of 15 newly synthesized phenoxyacetic and carbamic acid derivatives, which are potential herbicides. Chromatographic lipophilicity descriptors were used to extrapolate log k parameters (log kw and log km) and log k values. Partitioning lipophilicity descriptors, i.e., log P coefficients in an n-octanol-water system, were computed from the molecular structures of the tested compounds. Bioactivity descriptors, including partition coefficients in a water-plant cuticle system and water-human serum albumin and coefficients for human skin partition and permeation were calculated in silico by ACD/ADME software using the linear solvation energy relationship of Abraham. Principal component analysis was applied to describe similarities between various chromatographic and partitioning lipophilicities. Highly significant, predictive linear relationships were found between chromatographic parameters and bioactivity descriptors. © The Author [2013]. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

An infrastructure to mine molecular descriptors for ligand selection on virtual screening.

PubMed

Seus, Vinicius Rosa; Perazzo, Giovanni Xavier; Winck, Ana T; Werhli, Adriano V; Machado, Karina S

2014-01-01

The receptor-ligand interaction evaluation is one important step in rational drug design. The databases that provide the structures of the ligands are growing on a daily basis. This makes it impossible to test all the ligands for a target receptor. Hence, a ligand selection before testing the ligands is needed. One possible approach is to evaluate a set of molecular descriptors. With the aim of describing the characteristics of promising compounds for a specific receptor we introduce a data warehouse-based infrastructure to mine molecular descriptors for virtual screening (VS). We performed experiments that consider as target the receptor HIV-1 protease and different compounds for this protein. A set of 9 molecular descriptors are taken as the predictive attributes and the free energy of binding is taken as a target attribute. By applying the J48 algorithm over the data we obtain decision tree models that achieved up to 84% of accuracy. The models indicate which molecular descriptors and their respective values are relevant to influence good FEB results. Using their rules we performed ligand selection on ZINC database. Our results show important reduction in ligands selection to be applied in VS experiments; for instance, the best selection model picked only 0.21% of the total amount of drug-like ligands.

Notes on quantitative structure-properties relationships (QSPR) (1): A discussion on a QSPR dimensionality paradox (QSPR DP) and its quantum resolution.

PubMed

Carbó-Dorca, Ramon; Gallegos, Ana; Sánchez, Angel J

2009-05-01

Classical quantitative structure-properties relationship (QSPR) statistical techniques unavoidably present an inherent paradoxical computational context. They rely on the definition of a Gram matrix in descriptor spaces, which is used afterwards to reduce the original dimension via several possible kinds of algebraic manipulations. From there, effective models for the computation of unknown properties of known molecular structures are obtained. However, the reduced descriptor dimension causes linear dependence within the set of discrete vector molecular representations, leading to positive semi-definite Gram matrices in molecular spaces. To resolve this QSPR dimensionality paradox (QSPR DP) here is proposed to adopt as starting point the quantum QSPR (QQSPR) computational framework perspective, where density functions act as infinite dimensional descriptors. The fundamental QQSPR equation, deduced from employing quantum expectation value numerical evaluation, can be approximately solved in order to obtain models exempt of the QSPR DP. The substitution of the quantum similarity matrix by an empirical Gram matrix in molecular spaces, build up with the original non manipulated discrete molecular descriptor vectors, permits to obtain classical QSPR models with the same characteristics as in QQSPR, that is: possessing a certain degree of causality and explicitly independent of the descriptor dimension. 2008 Wiley Periodicals, Inc.

QSPR modeling of octanol/water partition coefficient for vitamins by optimal descriptors calculated with SMILES.

PubMed

Toropov, A A; Toropova, A P; Raska, I

2008-04-01

Simplified molecular input line entry system (SMILES) has been utilized in constructing quantitative structure-property relationships (QSPR) for octanol/water partition coefficient of vitamins and organic compounds of different classes by optimal descriptors. Statistical characteristics of the best model (vitamins) are the following: n=17, R(2)=0.9841, s=0.634, F=931 (training set); n=7, R(2)=0.9928, s=0.773, F=690 (test set). Using this approach for modeling octanol/water partition coefficient for a set of organic compounds gives a model that is statistically characterized by n=69, R(2)=0.9872, s=0.156, F=5184 (training set) and n=70, R(2)=0.9841, s=0.179, F=4195 (test set).

Quantum chemical study of a derivative of 3-substituted dithiocarbamic flavanone

NASA Astrophysics Data System (ADS)

Gosav, Steluta; Paduraru, Nicoleta; Maftei, Dan; Birsa, Mihail Lucian; Praisler, Mirela

2017-02-01

The aim of this work is to characterize a quite novel 3-dithiocarbamic flavonoid by vibrational spectroscopy in conjunction with Density Functional Theory (DFT) calculations. Quantum mechanics calculations of energies, geometries and vibrational wavenumbers in the ground state were carried out by using hybrid functional B3LYP with 6-311G(d,p) as basis set. The results indicate a remarkable agreement between the calculated molecular geometries, as well as vibrational frequencies, and the corresponding experimental data. In addition, a complete assignment of all the absorption bands present in the vibrational spectrum has been performed. In order to assess its chemical potential, quantum molecular descriptors characterizing the interactions between the 3-dithiocarbamic flavonoid and its biological receptors have been computed. The frontier molecular orbitals and the HOMO-LUMO energy gap have been used in order to explain the way in which the new molecule can interact with other species and to characterize its molecular chemical stability/reactivity. The molecular electrostatic potential (MEP) map, computed in order to identify the sites of the studied flavonoid that are most likely to interact with electrophilic and nucleophilic species, is discussed.

Towards the chemometric dissection of peptide - HLA-A*0201 binding affinity: comparison of local and global QSAR models

NASA Astrophysics Data System (ADS)

Doytchinova, Irini A.; Walshe, Valerie; Borrow, Persephone; Flower, Darren R.

2005-03-01

The affinities of 177 nonameric peptides binding to the HLA-A*0201 molecule were measured using a FACS-based MHC stabilisation assay and analysed using chemometrics. Their structures were described by global and local descriptors, QSAR models were derived by genetic algorithm, stepwise regression and PLS. The global molecular descriptors included molecular connectivity χ indices, κ shape indices, E-state indices, molecular properties like molecular weight and log P, and three-dimensional descriptors like polarizability, surface area and volume. The local descriptors were of two types. The first used a binary string to indicate the presence of each amino acid type at each position of the peptide. The second was also position-dependent but used five z-scales to describe the main physicochemical properties of the amino acids forming the peptides. The models were developed using a representative training set of 131 peptides and validated using an independent test set of 46 peptides. It was found that the global descriptors could not explain the variance in the training set nor predict the affinities of the test set accurately. Both types of local descriptors gave QSAR models with better explained variance and predictive ability. The results suggest that, in their interactions with the MHC molecule, the peptide acts as a complicated ensemble of multiple amino acids mutually potentiating each other.

QSAR Analysis of 2-Amino or 2-Methyl-1-Substituted Benzimidazoles Against Pseudomonas aeruginosa

PubMed Central

Podunavac-Kuzmanović, Sanja O.; Cvetković, Dragoljub D.; Barna, Dijana J.

2009-01-01

A set of benzimidazole derivatives were tested for their inhibitory activities against the Gram-negative bacterium Pseudomonas aeruginosa and minimum inhibitory concentrations were determined for all the compounds. Quantitative structure activity relationship (QSAR) analysis was applied to fourteen of the abovementioned derivatives using a combination of various physicochemical, steric, electronic, and structural molecular descriptors. A multiple linear regression (MLR) procedure was used to model the relationships between molecular descriptors and the antibacterial activity of the benzimidazole derivatives. The stepwise regression method was used to derive the most significant models as a calibration model for predicting the inhibitory activity of this class of molecules. The best QSAR models were further validated by a leave one out technique as well as by the calculation of statistical parameters for the established theoretical models. To confirm the predictive power of the models, an external set of molecules was used. High agreement between experimental and predicted inhibitory values, obtained in the validation procedure, indicated the good quality of the derived QSAR models. PMID:19468332

Prediction of Partition Coefficients of Organic Compounds between SPME/PDMS and Aqueous Solution

PubMed Central

Chao, Keh-Ping; Lu, Yu-Ting; Yang, Hsiu-Wen

2014-01-01

Polydimethylsiloxane (PDMS) is commonly used as the coated polymer in the solid phase microextraction (SPME) technique. In this study, the partition coefficients of organic compounds between SPME/PDMS and the aqueous solution were compiled from the literature sources. The correlation analysis for partition coefficients was conducted to interpret the effect of their physicochemical properties and descriptors on the partitioning process. The PDMS-water partition coefficients were significantly correlated to the polarizability of organic compounds (r = 0.977, p < 0.05). An empirical model, consisting of the polarizability, the molecular connectivity index, and an indicator variable, was developed to appropriately predict the partition coefficients of 61 organic compounds for the training set. The predictive ability of the empirical model was demonstrated by using it on a test set of 26 chemicals not included in the training set. The empirical model, applying the straightforward calculated molecular descriptors, for estimating the PDMS-water partition coefficient will contribute to the practical applications of the SPME technique. PMID:24534804

QSAR Study for Carcinogenic Potency of Aromatic Amines Based on GEP and MLPs

PubMed Central

Song, Fucheng; Zhang, Anling; Liang, Hui; Cui, Lianhua; Li, Wenlian; Si, Hongzong; Duan, Yunbo; Zhai, Honglin

2016-01-01

A new analysis strategy was used to classify the carcinogenicity of aromatic amines. The physical-chemical parameters are closely related to the carcinogenicity of compounds. Quantitative structure activity relationship (QSAR) is a method of predicting the carcinogenicity of aromatic amine, which can reveal the relationship between carcinogenicity and physical-chemical parameters. This study accessed gene expression programming by APS software, the multilayer perceptrons by Weka software to predict the carcinogenicity of aromatic amines, respectively. All these methods relied on molecular descriptors calculated by CODESSA software and eight molecular descriptors were selected to build function equations. As a remarkable result, the accuracy of gene expression programming in training and test sets are 0.92 and 0.82, the accuracy of multilayer perceptrons in training and test sets are 0.84 and 0.74 respectively. The precision of the gene expression programming is obviously superior to multilayer perceptrons both in training set and test set. The QSAR application in the identification of carcinogenic compounds is a high efficiency method. PMID:27854309

Chemically Aware Model Builder (camb): an R package for property and bioactivity modelling of small molecules.

PubMed

Murrell, Daniel S; Cortes-Ciriano, Isidro; van Westen, Gerard J P; Stott, Ian P; Bender, Andreas; Malliavin, Thérèse E; Glen, Robert C

2015-01-01

In silico predictive models have proved to be valuable for the optimisation of compound potency, selectivity and safety profiles in the drug discovery process. camb is an R package that provides an environment for the rapid generation of quantitative Structure-Property and Structure-Activity models for small molecules (including QSAR, QSPR, QSAM, PCM) and is aimed at both advanced and beginner R users. camb's capabilities include the standardisation of chemical structure representation, computation of 905 one-dimensional and 14 fingerprint type descriptors for small molecules, 8 types of amino acid descriptors, 13 whole protein sequence descriptors, filtering methods for feature selection, generation of predictive models (using an interface to the R package caret), as well as techniques to create model ensembles using techniques from the R package caretEnsemble). Results can be visualised through high-quality, customisable plots (R package ggplot2). Overall, camb constitutes an open-source framework to perform the following steps: (1) compound standardisation, (2) molecular and protein descriptor calculation, (3) descriptor pre-processing and model training, visualisation and validation, and (4) bioactivity/property prediction for new molecules. camb aims to speed model generation, in order to provide reproducibility and tests of robustness. QSPR and proteochemometric case studies are included which demonstrate camb's application.Graphical abstractFrom compounds and data to models: a complete model building workflow in one package.

Predicting Subtype Selectivity for Adenosine Receptor Ligands with Three-Dimensional Biologically Relevant Spectrum (BRS-3D)

NASA Astrophysics Data System (ADS)

He, Song-Bing; Ben Hu; Kuang, Zheng-Kun; Wang, Dong; Kong, De-Xin

2016-11-01

Adenosine receptors (ARs) are potential therapeutic targets for Parkinson’s disease, diabetes, pain, stroke and cancers. Prediction of subtype selectivity is therefore important from both therapeutic and mechanistic perspectives. In this paper, we introduced a shape similarity profile as molecular descriptor, namely three-dimensional biologically relevant spectrum (BRS-3D), for AR selectivity prediction. Pairwise regression and discrimination models were built with the support vector machine methods. The average determination coefficient (r2) of the regression models was 0.664 (for test sets). The 2B-3 (A2B vs A3) model performed best with q2 = 0.769 for training sets (10-fold cross-validation), and r2 = 0.766, RMSE = 0.828 for test sets. The models’ robustness and stability were validated with 100 times resampling and 500 times Y-randomization. We compared the performance of BRS-3D with 3D descriptors calculated by MOE. BRS-3D performed as good as, or better than, MOE 3D descriptors. The performances of the discrimination models were also encouraging, with average accuracy (ACC) 0.912 and MCC 0.792 (test set). The 2A-3 (A2A vs A3) selectivity discrimination model (ACC = 0.882 and MCC = 0.715 for test set) outperformed an earlier reported one (ACC = 0.784). These results demonstrated that, through multiple conformation encoding, BRS-3D can be used as an effective molecular descriptor for AR subtype selectivity prediction.

Blue M2: an intermediate melanoidin studied via conceptual DFT.

PubMed

Frau, Juan; Glossman-Mitnik, Daniel

2018-05-31

In this computational study, ten density functionals, viz. CAM-B3LYP, LC-ω PBE, M11, M11L, MN12L, MN12SX, N12, N12SX, ω B97X, and ω B97XD, related to the Def2TZVP basis sets, are assessed together with the SMD solvation model for calculation of the molecular properties and structure of blue-M2 intermediate melanoidin pigment. All the chemical reactivity descriptors for the system are calculated via conceptual density functional theory (DFT). The active sites suitable for nucleophilic, electrophilic, and radical attacks are selected by linking them with the Fukui function indices, electrophilic Parr functions, and condensed dual descriptors Δf(r), respectively. The prediction of the maximum absorption wavelength is considerably accurate relative to its experimental value. The study reveals that the MN12SX and N12SX density functionals are the most appropriate density functionals for predicting the chemical reactivity of the molecule under study.

Spectroscopic, quantum chemical calculation and molecular docking of dipfluzine

NASA Astrophysics Data System (ADS)

Srivastava, Karnica; Srivastava, Anubha; Tandon, Poonam; Sinha, Kirti; Wang, Jing

2016-12-01

Molecular structure and vibrational analysis of dipfluzine (C27H29FN2O) were presented using FT-IR and FT-Raman spectroscopy and quantum chemical calculations. The theoretical ground state geometry and electronic structure of dipfluzine are optimized by the DFT/B3LYP/6-311++G (d,p) method and compared with those of the crystal data. The 1D potential energy scan was performed by varying the dihedral angle using B3LYP functional at 6-31G(d,p) level of theory and thus the most stable conformer of the compound were determined. Molecular electrostatic potential surface (MEPS), frontier orbital analysis and electronic reactivity descriptor were used to predict the chemical reactivity of molecule. Energies of intra- and inter-molecular hydrogen bonds in molecule and their electronic aspects were investigated by natural bond orbital (NBO). To find out the anti-apoptotic activity of the title compound molecular docking studies have been performed against protein Fas.

Conceptual DFT Study of the Local Chemical Reactivity of the Colored BISARG Melanoidin and Its Protonated Derivative

PubMed Central

Frau, Juan; Glossman-Mitnik, Daniel

2018-01-01

This computational study assessed eight fixed RSH (range-separated hybrid) density functionals that include CAM-B3LYP, LC-ωPBE, M11, MN12SX, N12SX, ωB97, ωB97X, and ωB97XD related to the Def2TZVP basis sets together with the SMD solvation model in the calculation the molecular structure and reactivity properties of the BISARG intermediate melanoidin pigment (5-(2-(E)-(Z)-5-[(2-furyl)methylidene]-3-(4-acetylamino-4-carboxybutyl)-2-imino-1,3-dihydroimidazol-4-ylideneamino(E)-4-[(2-furyl)methylidene]-5-oxo-1H-imidazol-1-yl)-2-acetylaminovaleric acid) and its protonated derivative, BISARG(p). The chemical reactivity descriptors for the systems were calculated via the Conceptual Density Functional Theory. The choice of active sites applicable to nucleophilic, electrophilic as well as radical attacks were made by linking them with Fukui functions indices, electrophilic and nucleophilic Parr functions, and the condensed Dual Descriptor Δf(r). The study found the MN12SX and N12SX density functionals to be the most appropriate in predicting the chemical reactivity of the molecular systems under study starting from the knowledge of the HOMO, LUMO, and HOMO-LUMO gap energies. PMID:29765937

Conceptual DFT Study of the Local Chemical Reactivity of the Colored BISARG Melanoidin and Its Protonated Derivative.

PubMed

Frau, Juan; Glossman-Mitnik, Daniel

2018-01-01

This computational study assessed eight fixed RSH (range-separated hybrid) density functionals that include CAM-B3LYP, LC-ωPBE, M11, MN12SX, N12SX, ωB97, ωB97X, and ωB97XD related to the Def2TZVP basis sets together with the SMD solvation model in the calculation the molecular structure and reactivity properties of the BISARG intermediate melanoidin pigment (5-(2-(E)-(Z)-5-[(2-furyl)methylidene]-3-(4-acetylamino-4-carboxybutyl)-2-imino-1,3-dihydroimidazol-4-ylideneamino(E)-4-[(2-furyl)methylidene]-5-oxo-1H-imidazol-1-yl)-2-acetylaminovaleric acid) and its protonated derivative, BISARG(p). The chemical reactivity descriptors for the systems were calculated via the Conceptual Density Functional Theory. The choice of active sites applicable to nucleophilic, electrophilic as well as radical attacks were made by linking them with Fukui functions indices, electrophilic and nucleophilic Parr functions, and the condensed Dual Descriptor Δf( r ). The study found the MN12SX and N12SX density functionals to be the most appropriate in predicting the chemical reactivity of the molecular systems under study starting from the knowledge of the HOMO, LUMO, and HOMO-LUMO gap energies.

Molecular basis of quantitative structure-properties relationships (QSPR): a quantum similarity approach.

PubMed

Ponec, R; Amat, L; Carbó-Dorca, R

1999-05-01

Since the dawn of quantitative structure-properties relationships (QSPR), empirical parameters related to structural, electronic and hydrophobic molecular properties have been used as molecular descriptors to determine such relationships. Among all these parameters, Hammett sigma constants and the logarithm of the octanol-water partition coefficient, log P, have been massively employed in QSPR studies. In the present paper, a new molecular descriptor, based on quantum similarity measures (QSM), is proposed as a general substitute of these empirical parameters. This work continues previous analyses related to the use of QSM to QSPR, introducing molecular quantum self-similarity measures (MQS-SM) as a single working parameter in some cases. The use of MQS-SM as a molecular descriptor is first confirmed from the correlation with the aforementioned empirical parameters. The Hammett equation has been examined using MQS-SM for a series of substituted carboxylic acids. Then, for a series of aliphatic alcohols and acetic acid esters, log P values have been correlated with the self-similarity measure between density functions in water and octanol of a given molecule. And finally, some examples and applications of MQS-SM to determine QSAR are presented. In all studied cases MQS-SM appeared to be excellent molecular descriptors usable in general QSPR applications of chemical interest.

Molecular basis of quantitative structure-properties relationships (QSPR): A quantum similarity approach

NASA Astrophysics Data System (ADS)

Ponec, Robert; Amat, Lluís; Carbó-dorca, Ramon

1999-05-01

Since the dawn of quantitative structure-properties relationships (QSPR), empirical parameters related to structural, electronic and hydrophobic molecular properties have been used as molecular descriptors to determine such relationships. Among all these parameters, Hammett σ constants and the logarithm of the octanol- water partition coefficient, log P, have been massively employed in QSPR studies. In the present paper, a new molecular descriptor, based on quantum similarity measures (QSM), is proposed as a general substitute of these empirical parameters. This work continues previous analyses related to the use of QSM to QSPR, introducing molecular quantum self-similarity measures (MQS-SM) as a single working parameter in some cases. The use of MQS-SM as a molecular descriptor is first confirmed from the correlation with the aforementioned empirical parameters. The Hammett equation has been examined using MQS-SM for a series of substituted carboxylic acids. Then, for a series of aliphatic alcohols and acetic acid esters, log P values have been correlated with the self-similarity measure between density functions in water and octanol of a given molecule. And finally, some examples and applications of MQS-SM to determine QSAR are presented. In all studied cases MQS-SM appeared to be excellent molecular descriptors usable in general QSPR applications of chemical interest.

A Novel/Old Modification of the First Zagreb Index.

PubMed

Ali, Akbar; Trinajstić, Nenad

2018-03-14

In the seminal paper [I. Gutman, N. Trinajstić, Chem. Phys. Lett. 1972, 17, 535-538], it was shown that total electron energy (Eπ ) of any alternant hydrocarbon depends on the sum of the squares of the degrees of the corresponding molecular graph. Nowadays, this sum is known as the first Zagreb index. In the same paper, another molecular descriptor was proved to influence Eπ , but that descriptor was never restudied explicitly. We call this descriptor as modified first Zagreb connection index and denote it by ZC1* . In this paper, chemical applicability of the molecular descriptor ZC1* is tested for the octane isomers. Some basic properties of ZC1* are also established here. Furthermore, the alkanes with maximum and minimum ZC1* values are determined from the class of all alkanes having fixed number of carbon atoms. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Calculating the dermal flux of chemicals with OELs based on their molecular structure: An attempt to assign the skin notation.

PubMed

Kupczewska-Dobecka, Małgorzata; Jakubowski, Marek; Czerczak, Sławomir

2010-09-01

Our objectives included calculating the permeability coefficient and dermal penetration rates (flux value) for 112 chemicals with occupational exposure limits (OELs) according to the LFER (linear free-energy relationship) model developed using published methods. We also attempted to assign skin notations based on each chemical's molecular structure. There are many studies available where formulae for coefficients of permeability from saturated aqueous solutions (K(p)) have been related to physicochemical characteristics of chemicals. The LFER model is based on the solvation equation, which contains five main descriptors predicted from chemical structure: solute excess molar refractivity, dipolarity/polarisability, summation hydrogen bond acidity and basicity, and the McGowan characteristic volume. Descriptor values, available for about 5000 compounds in the Pharma Algorithms Database were used to calculate permeability coefficients. Dermal penetration rate was estimated as a ratio of permeability coefficient and concentration of chemical in saturated aqueous solution. Finally, estimated dermal penetration rates were used to assign the skin notation to chemicals. Defined critical fluxes defined from the literature were recommended as reference values for skin notation. The application of Abraham descriptors predicted from chemical structure and LFER analysis in calculation of permeability coefficients and flux values for chemicals with OELs was successful. Comparison of calculated K(p) values with data obtained earlier from other models showed that LFER predictions were comparable to those obtained by some previously published models, but the differences were much more significant for others. It seems reasonable to conclude that skin should not be characterised as a simple lipophilic barrier alone. Both lipophilic and polar pathways of permeation exist across the stratum corneum. It is feasible to predict skin notation on the basis of the LFER and other published models; from among 112 chemicals 94 (84%) should have the skin notation in the OEL list based on the LFER calculations. The skin notation had been estimated by other published models for almost 94% of the chemicals. Twenty-nine (25.8%) chemicals were identified to have significant absorption and 65 (58%) the potential for dermal toxicity. We found major differences between alternative published analytical models and their ability to determine whether particular chemicals were potentially dermotoxic. Copyright © 2010 Elsevier B.V. All rights reserved.

Evaluation of Structural Isomers, Molecular Interactions, Reactivity Descriptors, and Vibrational Analysis of Tretinoin.

PubMed

Karthick, T; Tandon, Poonam; Singh, Swapnil

2017-01-01

Tretinoin is known to be a pharmaceutical drug for treating acne vulgaris, keratosis pilaris, and acute promyelocytic leukemia. In order to reveal the possible conformers of tretinoin, the energies of all the conformers through rotational bonds have been evaluated by systematic rotor search analysis. The intramolecular interactions ranging from strong hydrogen bonds to weak van der Waals forces present in tretinoin have been distinguished with the help of electron density mapping and wavefunction analysis. The global reactivity descriptors and Fukui functions of tretinoin have been calculated and discussed. The sites suitable for electrophilic attack and nucleophilic attack have been identified with the help of Hirshfeld partitioning. The vibrational spectroscopic signature of tretinoin and mixed mode band assignments have been elucidated with the help of experimental and simulated spectra.

Quantitative structure-retention relationship models for the prediction of the reversed-phase HPLC gradient retention based on the heuristic method and support vector machine.

PubMed

Du, Hongying; Wang, Jie; Yao, Xiaojun; Hu, Zhide

2009-01-01

The heuristic method (HM) and support vector machine (SVM) were used to construct quantitative structure-retention relationship models by a series of compounds to predict the gradient retention times of reversed-phase high-performance liquid chromatography (HPLC) in three different columns. The aims of this investigation were to predict the retention times of multifarious compounds, to find the main properties of the three columns, and to indicate the theory of separation procedures. In our method, we correlated the retention times of many diverse structural analytes in three columns (Symmetry C18, Chromolith, and SG-MIX) with their representative molecular descriptors, calculated from the molecular structures alone. HM was used to select the most important molecular descriptors and build linear regression models. Furthermore, non-linear regression models were built using the SVM method; the performance of the SVM models were better than that of the HM models, and the prediction results were in good agreement with the experimental values. This paper could give some insights into the factors that were likely to govern the gradient retention process of the three investigated HPLC columns, which could theoretically supervise the practical experiment.

Conceptual DFT Descriptors of Amino Acids with Potential Corrosion Inhibition Properties Calculated with the Latest Minnesota Density Functionals.

PubMed

Frau, Juan; Glossman-Mitnik, Daniel

2017-01-01

Amino acids and peptides have the potential to perform as corrosion inhibitors. The chemical reactivity descriptors that arise from Conceptual DFT for the twenty natural amino acids have been calculated by using the latest Minnesota family of density functionals. In order to verify the validity of the calculation of the descriptors directly from the HOMO and LUMO, a comparison has been performed with those obtained through ΔSCF results. Moreover, the active sites for nucleophilic and electrophilic attacks have been identified through Fukui function indices, the dual descriptor Δf( r ) and the electrophilic and nucleophilic Parr functions. The results could be of interest as a starting point for the study of large peptides where the calculation of the radical cation and anion of each system may be computationally harder and costly.

Blast and ballistic trajectories in combat casualties: a preliminary analysis using a cartesian positioning system with MDCT.

PubMed

Folio, Les R; Fischer, Tatjana; Shogan, Paul; Frew, Michael; Dwyer, Andrew; Provenzale, James M

2011-08-01

The purpose of this study is to determine the agreement with which radiologists identify wound paths in vivo on MDCT and calculate missile trajectories on the basis of Cartesian coordinates using a Cartesian positioning system (CPS). Three radiologists retrospectively identified 25 trajectories on MDCT in 19 casualties who sustained penetrating trauma in Iraq. Trajectories were described qualitatively in terms of directional path descriptors and quantitatively as trajectory vectors. Directional descriptors, trajectory angles, and angles between trajectories were calculated based on Cartesian coordinates of entrance and terminus or exit recorded in x, y image and table space (z) using a Trajectory Calculator created using spreadsheet software. The consistency of qualitative descriptor determinations was assessed in terms of frequency of observer agreement and multirater kappa statistics. Consistency of trajectory vectors was evaluated in terms of distribution of magnitude of the angles between vectors and the differences between their paraaxial and parasagittal angles. In 68% of trajectories, the observers' visual assessment of qualitative descriptors was congruent. Calculated descriptors agreed across observers in 60% of the trajectories. Estimated kappa also showed good agreement (0.65-0.79, p < 0.001); 70% of calculated paraaxial and parasagittal angles were within 20° across observers, and 61.3% of angles between trajectory vectors were within 20° across observers. Results show agreement of visually assessed and calculated qualitative descriptors and trajectory angles among observers. The Trajectory Calculator describes trajectories qualitatively similar to radiologists' visual assessment, showing the potential feasibility of automated trajectory analysis.

Implications for Metabolite Quantification by Mass Spectrometry in the Absence of Authentic Standards.

PubMed

Hatsis, Panos; Waters, Nigel J; Argikar, Upendra A

2017-05-01

Quantification of metabolites by mass spectrometry in the absence of authentic reference standards or without a radiolabel is often called "semiquantitative," which acknowledges that mass spectrometric responses are not truly quantitative. For many researchers, it is tempting to pursue this practice of semiquantification in early drug discovery and even preclinical development, when radiolabeled absorption, distribution, metabolism, and excretion studies are being deferred to later stages of drug development. The caveats of quantifying metabolites based on parent drug response are explored in this investigation. A set of 71 clinically relevant drugs/metabolites encompassing common biotransformation pathways was subjected to flow injection analysis coupled with electrospray ionization (ESI) mass spectrometry. The results revealed a large variation in ESI response even for structurally similar parent drug/metabolite pairs. The ESI response of each metabolite was normalized to that of the parent drug to generate an ESI relative response factor. Overall, relative response factors ranged from 0.014 (>70-fold lower response than parent) to 8.6 (8.6-fold higher response than parent). Various two-dimensional molecular descriptors were calculated that describe physicochemical, topological, and structural properties for each drug/metabolite. The molecular descriptors, along with the ESI response factors, were used in univariate analyses as well as a principal components analysis to ascertain which molecular descriptors best account for the observed discrepancies in drug/metabolite ESI response. This investigation has shown that the practice of using parent drug response to quantify metabolites should be used with caution. Copyright © 2017 by The American Society for Pharmacology and Experimental Therapeutics.

Global QSAR modeling of logP values of phenethylamines acting as adrenergic alpha-1 receptor agonists.

PubMed

Yadav, Mukesh; Joshi, Shobha; Nayarisseri, Anuraj; Jain, Anuja; Hussain, Aabid; Dubey, Tushar

2013-06-01

Global QSAR models predict biological response of molecular structures which are generic in particular class. A global QSAR dataset admits structural features derived from larger chemical space, intricate to model but more applicable in medicinal chemistry. The present work is global in either sense of structural diversity in QSAR dataset or large number of descriptor input. Forty phenethylamine structure derivatives were selected from a large pool (904) of similar phenethylamines available in Pubchem database. LogP values of selected candidates were collected from physical properties database (PHYSPROP) determined in identical set of conditions. Attempts to model logP value have produced significant QSAR models. MLR aided linear one-variable and two-variable QSAR models with their respective R(2) (0.866, 0.937), R(2)A (0.862, 0.932), F-stat (181.936, 199.812) and Standard Error (0.365, 0.255) are statistically fit and found predictive after internal validation and external validation. The descriptors chosen after improvisation and optimization reveal mechanistic part of work in terms of Verhaar model of Fish base-line toxicity from MLOGP, i.e. (BLTF96) and 3D-MoRSE -signal 15 /unweighted molecular descriptor calculated by summing atom weights viewed by a different angular scattering function (Mor15u) are crucial in regulation of logP values of phenethylamines.

Physicochemical properties/descriptors governing the solubility and partitioning of chemicals in water-solvent-gas systems. Part 2. Solubility in 1-octanol.

PubMed

Raevsky, O A; Perlovich, G L; Schaper, K-J

2007-01-01

On the basis of octanol solubility data (log S(o)) for 218 structurally diverse solid chemicals it was shown that the exclusive consideration of melting points did not provide satisfactory results in the quantitative prediction of this parameter (s = 0.92). The application of HYBOT physicochemical descriptors separately (s = 0.94) and together with melting points (s = 0.70) in the framework of a common regression model also was not successful, although contributions of volume-related and H-bond terms to solubility in octanol were identified. It was proposed that the main reason for such behaviour was the different crystal lattice interaction of different classes of chemicals. Successful calculations of the solubility in octanol of chemicals of interest were performed on the basis of the experimental solubility of structurally/physicochemically/numerically similar nearest neighbours with consideration of their difference in physicochemical parameters (molecular polarisability, H-bond acceptor and donor factors (s = 0.66)) and of these descriptors together with melting point differences (s = 0.38). Good results were obtained for all compounds having nearest neighbours with sufficient similarity, expressed by Tanimoto indexes, and by distances in the scaled 3D descriptor space. Obviously the success of this approach depends on the size of the database.

Analysis of A Drug Target-based Classification System using Molecular Descriptors.

PubMed

Lu, Jing; Zhang, Pin; Bi, Yi; Luo, Xiaomin

2016-01-01

Drug-target interaction is an important topic in drug discovery and drug repositioning. KEGG database offers a drug annotation and classification using a target-based classification system. In this study, we gave an investigation on five target-based classes: (I) G protein-coupled receptors; (II) Nuclear receptors; (III) Ion channels; (IV) Enzymes; (V) Pathogens, using molecular descriptors to represent each drug compound. Two popular feature selection methods, maximum relevance minimum redundancy and incremental feature selection, were adopted to extract the important descriptors. Meanwhile, an optimal prediction model based on nearest neighbor algorithm was constructed, which got the best result in identifying drug target-based classes. Finally, some key descriptors were discussed to uncover their important roles in the identification of drug-target classes.

Adjacent bin stability evaluating for feature description

NASA Astrophysics Data System (ADS)

Nie, Dongdong; Ma, Qinyong

2018-04-01

Recent study improves descriptor performance by accumulating stability votes for all scale pairs to compose the local descriptor. We argue that the stability of a bin depends on the differences across adjacent pairs more than the differences across all scale pairs, and a new local descriptor is composed based on the hypothesis. A series of SIFT descriptors are extracted from multiple scales firstly. Then the difference value of the bin across adjacent scales is calculated, and the stability value of a bin is calculated based on it and accumulated to compose the final descriptor. The performance of the proposed method is evaluated with two popular matching datasets, and compared with other state-of-the-art works. Experimental results show that the proposed method performs satisfactorily.

A 3D model retrieval approach based on Bayesian networks lightfield descriptor

NASA Astrophysics Data System (ADS)

Xiao, Qinhan; Li, Yanjun

2009-12-01

A new 3D model retrieval methodology is proposed by exploiting a novel Bayesian networks lightfield descriptor (BNLD). There are two key novelties in our approach: (1) a BN-based method for building lightfield descriptor; and (2) a 3D model retrieval scheme based on the proposed BNLD. To overcome the disadvantages of the existing 3D model retrieval methods, we explore BN for building a new lightfield descriptor. Firstly, 3D model is put into lightfield, about 300 binary-views can be obtained along a sphere, then Fourier descriptors and Zernike moments descriptors can be calculated out from binaryviews. Then shape feature sequence would be learned into a BN model based on BN learning algorithm; Secondly, we propose a new 3D model retrieval method by calculating Kullback-Leibler Divergence (KLD) between BNLDs. Beneficial from the statistical learning, our BNLD is noise robustness as compared to the existing methods. The comparison between our method and the lightfield descriptor-based approach is conducted to demonstrate the effectiveness of our proposed methodology.

In silico prediction of nematic transition temperature for liquid crystals using quantitative structure-property relationship approaches.

PubMed

Fatemi, Mohammad Hossein; Ghorbanzad'e, Mehdi

2009-11-01

Quantitative structure-property relationship models for the prediction of the nematic transition temperature (T (N)) were developed by using multilinear regression analysis and a feedforward artificial neural network (ANN). A collection of 42 thermotropic liquid crystals was chosen as the data set. The data set was divided into three sets: for training, and an internal and external test set. Training and internal test sets were used for ANN model development, and the external test set was used for evaluation of the predictive power of the model. In order to build the models, a set of six descriptors were selected by the best multilinear regression procedure of the CODESSA program. These descriptors were: atomic charge weighted partial negatively charged surface area, relative negative charged surface area, polarity parameter/square distance, minimum most negative atomic partial charge, molecular volume, and the A component of moment of inertia, which encode geometrical and electronic characteristics of molecules. These descriptors were used as inputs to ANN. The optimized ANN model had 6:6:1 topology. The standard errors in the calculation of T (N) for the training, internal, and external test sets using the ANN model were 1.012, 4.910, and 4.070, respectively. To further evaluate the ANN model, a crossvalidation test was performed, which produced the statistic Q (2) = 0.9796 and standard deviation of 2.67 based on predicted residual sum of square. Also, the diversity test was performed to ensure the model's stability and prove its predictive capability. The obtained results reveal the suitability of ANN for the prediction of T (N) for liquid crystals using molecular structural descriptors.

Conceptual DFT Descriptors of Amino Acids with Potential Corrosion Inhibition Properties Calculated with the Latest Minnesota Density Functionals

PubMed Central

Frau, Juan; Glossman-Mitnik, Daniel

2017-01-01

Amino acids and peptides have the potential to perform as corrosion inhibitors. The chemical reactivity descriptors that arise from Conceptual DFT for the twenty natural amino acids have been calculated by using the latest Minnesota family of density functionals. In order to verify the validity of the calculation of the descriptors directly from the HOMO and LUMO, a comparison has been performed with those obtained through ΔSCF results. Moreover, the active sites for nucleophilic and electrophilic attacks have been identified through Fukui function indices, the dual descriptor Δf(r) and the electrophilic and nucleophilic Parr functions. The results could be of interest as a starting point for the study of large peptides where the calculation of the radical cation and anion of each system may be computationally harder and costly. PMID:28361050

Synthesis, XRD crystal structure, spectroscopic characterization, local reactive properties using DFT and molecular dynamics simulations and molecular docking study of (E)-1-(4-bromophenyl)-3-(4-(trifluoromethoxy)phenyl)prop-2-en-1-one

NASA Astrophysics Data System (ADS)

Arshad, Suhana; Raveendran Pillai, Renjith; Zainuri, Dian Alwani; Khalib, Nuridayanti Che; Razak, Ibrahim Abdul; Armaković, Stevan; Armaković, Sanja J.; Renjith, Rishikesh; Panicker, C. Yohannan; Van Alsenoy, C.

2017-06-01

In the present study, the title compound named as (E)-1-(4-bromophenyl)-3-(4-(trifluoromethoxy)phenyl)prop-2-en-1-one was synthesized and structurally characterized by single-crystal X-ray diffraction. The FT-IR spectrum was recorded and interpreted in details with the aid of Density Functional Theory (DFT) calculations and Potential Energy Distribution (PED) analysis. Average local ionization energies (ALIE) and Fukui functions have been used as quantum-molecular descriptors to locate the molecule sites that could be of importance from the aspect of reactivity. Degradation properties have been assessed by calculations of bond dissociation energies (BDE) for hydrogen abstraction and the rest of the single acyclic bonds, while molecular dynamics (MD) simulations were used in order to calculate radial distribution functions and determine the atoms with significant interactions with water. In order to understand how the title molecule inhibits and hence increases the catalytic efficiency of MOA-B enzyme, molecular docking study was performed to fit the title compound into the binding site of MOA-B enzyme.

Structure-Activity Relationships for Rates of Aromatic Amine Oxidation by Manganese Dioxide.

PubMed

Salter-Blanc, Alexandra J; Bylaska, Eric J; Lyon, Molly A; Ness, Stuart C; Tratnyek, Paul G

2016-05-17

New energetic compounds are designed to minimize their potential environmental impacts, which includes their transformation and the fate and effects of their transformation products. The nitro groups of energetic compounds are readily reduced to amines, and the resulting aromatic amines are subject to oxidation and coupling reactions. Manganese dioxide (MnO2) is a common environmental oxidant and model system for kinetic studies of aromatic amine oxidation. In this study, a training set of new and previously reported kinetic data for the oxidation of model and energetic-derived aromatic amines was assembled and subjected to correlation analysis against descriptor variables that ranged from general purpose [Hammett σ constants (σ(-)), pKas of the amines, and energies of the highest occupied molecular orbital (EHOMO)] to specific for the likely rate-limiting step [one-electron oxidation potentials (Eox)]. The selection of calculated descriptors (pKa, EHOMO, and Eox) was based on validation with experimental data. All of the correlations gave satisfactory quantitative structure-activity relationships (QSARs), but they improved with the specificity of the descriptor. The scope of correlation analysis was extended beyond MnO2 to include literature data on aromatic amine oxidation by other environmentally relevant oxidants (ozone, chlorine dioxide, and phosphate and carbonate radicals) by correlating relative rate constants (normalized to 4-chloroaniline) to EHOMO (calculated with a modest level of theory).

Insights into the structural basis of N2 and O6 substituted guanine derivatives as cyclin-dependent kinase 2 (CDK2) inhibitors: prediction of the binding modes and potency of the inhibitors by docking and ONIOM calculations.

PubMed

Alzate-Morales, Jans H; Caballero, Julio; Vergara Jague, Ariela; González Nilo, Fernando D

2009-04-01

N2 and O6 substituted guanine derivatives are well-known as potent and selective CDK2 inhibitors. The ability of molecular docking using the program AutoDock3 and the hybrid method ONIOM, to obtain some quantum chemical descriptors with the aim to successfully rank these inhibitors, was assessed. The quantum chemical descriptors were used to explain the affinity, of the series studied, by a model of the CDK2 binding site. The initial structures were obtained from docking studies and the ONIOM method was applied with only a single point energy calculation on the protein-ligand structure. We obtained a good correlation model between the ONIOM derived quantum chemical descriptor "H-bond interaction energy" and the experimental biological activity, with a correlation coefficient value of R = 0.80 for 75 compounds. To the best of our knowledge, this is the first time that both methodologies are used in conjunction in order to obtain a correlation model. The model suggests that electrostatic interactions are the principal driving force in this protein-ligand interaction. Overall, the approach was successful for the cases considered, and it suggests that could be useful for the design of inhibitors in the lead optimization phase of drug discovery.

Chemical Sensor Array Response Modeling Using Quantitative Structure-Activity Relationships Technique

NASA Astrophysics Data System (ADS)

Shevade, Abhijit V.; Ryan, Margaret A.; Homer, Margie L.; Zhou, Hanying; Manfreda, Allison M.; Lara, Liana M.; Yen, Shiao-Pin S.; Jewell, April D.; Manatt, Kenneth S.; Kisor, Adam K.

We have developed a Quantitative Structure-Activity Relationships (QSAR) based approach to correlate the response of chemical sensors in an array with molecular descriptors. A novel molecular descriptor set has been developed; this set combines descriptors of sensing film-analyte interactions, representing sensor response, with a basic analyte descriptor set commonly used in QSAR studies. The descriptors are obtained using a combination of molecular modeling tools and empirical and semi-empirical Quantitative Structure-Property Relationships (QSPR) methods. The sensors under investigation are polymer-carbon sensing films which have been exposed to analyte vapors at parts-per-million (ppm) concentrations; response is measured as change in film resistance. Statistically validated QSAR models have been developed using Genetic Function Approximations (GFA) for a sensor array for a given training data set. The applicability of the sensor response models has been tested by using it to predict the sensor activities for test analytes not considered in the training set for the model development. The validated QSAR sensor response models show good predictive ability. The QSAR approach is a promising computational tool for sensing materials evaluation and selection. It can also be used to predict response of an existing sensing film to new target analytes.

3D molecular descriptors important for clinical success.

PubMed

Kombo, David C; Tallapragada, Kartik; Jain, Rachit; Chewning, Joseph; Mazurov, Anatoly A; Speake, Jason D; Hauser, Terry A; Toler, Steve

2013-02-25

The pharmacokinetic and safety profiles of clinical drug candidates are greatly influenced by their requisite physicochemical properties. In particular, it has been shown that 2D molecular descriptors such as fraction of Sp3 carbon atoms (Fsp3) and number of stereo centers correlate with clinical success. Using the proteomic off-target hit rate of nicotinic ligands, we found that shape-based 3D descriptors such as the radius of gyration and shadow indices discriminate off-target promiscuity better than do Fsp3 and the number of stereo centers. We have deduced the relevant descriptor values required for a ligand to be nonpromiscuous. Investigating the MDL Drug Data Report (MDDR) database as compounds move from the preclinical stage toward the market, we have found that these shape-based 3D descriptors predict clinical success of compounds at preclinical and phase1 stages vs compounds withdrawn from the market better than do Fsp3 and LogD. Further, these computed 3D molecular descriptors correlate well with experimentally observed solubility, which is among well-known physicochemical properties that drive clinical success. We also found that about 84% of launched drugs satisfy either Shadow index or Fsp3 criteria, whereas withdrawn and discontinued compounds fail to meet the same criteria. Our studies suggest that spherical compounds (rather than their elongated counterparts) with a minimal number of aromatic rings may exhibit a high propensity to advance from clinical trials to market.

The molecular structure of 5-X-isatines where (X = F, Cl, and Br) determined by gas-phase electron diffraction with theoretical calculations

NASA Astrophysics Data System (ADS)

Belyakov, Alexander V.; Nikolaenko, Kirill O.; Davidovich, Pavel B.; Ivanov, Anatolii D.; Ponyaev, Alexander I.; Rykov, Anatolii N.; Shishkov, Igor F.

2018-01-01

The molecular structures of 5-X-isatines where X = F (1), Cl (2), and Br (3) were studied by gas-phase electron diffraction (GED) and theoretical calculations at M062X/aug-cc-pVTZ and MP2/aug-cc-pVTZ levels. The best fit of the experimental scattering intensities was obtained for a molecular model of Cs symmetry. The small differences between similar geometric parameters were constrained at the values calculated at the M062X level. The bond distances in the benzene ring are in agreement with their standard values. The (Odbnd)Csbnd C(dbnd O) carbon-carbon bonds of the pyrrole moiety in title compounds (1.581(11), 1.578(8), 1.574(12) Å, respectively) are remarkably lengthened in comparison with standard C(sp2)-C(sp2) value, 1.425(11) Å for N-methylpyrrole. According to NBO analysis this lengthening cannot be attributed to the electrostatic repulsion of oxygen lone pairs alone and is, mainly, due to the hyperconjugation, that is delocalization of oxygen lone pairs of π-type into the corresponding carbon-carbon antibonding orbital, nπ(O)→σ*(Csbnd C). Deletion of σ*(Csbnd C) orbital followed by subsequent geometry optimization led to shortening of the corresponding Csbnd C bond by 0.05-0.06 Å. Electronegative halogen atoms led to increase of Csbnd CXsbnd C endocyclic bond angles at ipso carbon atom as compared with the value of 120° in regular hexagon. According to different aromaticity descriptors, aromaticity of benzene moiety of title compounds is smaller in comparison with benzene molecule. External magnetic field induces diatropic ring current in benzene moiety. Local reactivity descriptors that indicate sites in a molecule that are susceptible to nucleophilic, electrophilic and radical attack are calculated.

Prediction of atmospheric degradation data for POPs by gene expression programming.

PubMed

Luan, F; Si, H Z; Liu, H T; Wen, Y Y; Zhang, X Y

2008-01-01

Quantitative structure-activity relationship models for the prediction of the mean and the maximum atmospheric degradation half-life values of persistent organic pollutants were developed based on the linear heuristic method (HM) and non-linear gene expression programming (GEP). Molecular descriptors, calculated from the structures alone, were used to represent the characteristics of the compounds. HM was used both to pre-select the whole descriptor sets and to build the linear model. GEP yielded satisfactory prediction results: the square of the correlation coefficient r(2) was 0.80 and 0.81 for the mean and maximum half-life values of the test set, and the root mean square errors were 0.448 and 0.426, respectively. The results of this work indicate that the GEP is a very promising tool for non-linear approximations.

A Short Review of the Generation of Molecular Descriptors and Their Applications in Quantitative Structure Property/Activity Relationships.

PubMed

Sahoo, Sagarika; Adhikari, Chandana; Kuanar, Minati; Mishra, Bijay K

2016-01-01

Synthesis of organic compounds with specific biological activity or physicochemical characteristics needs a thorough analysis of the enumerable data set obtained from literature. Quantitative structure property/activity relationships have made it simple by predicting the structure of the compound with any optimized activity. For that there is a paramount data set of molecular descriptors (MD). This review is a survey on the generation of the molecular descriptors and its probable applications in QSP/AR. Literatures have been collected from a wide class of research journals, citable web reports, seminar proceedings and books. The MDs were classified according to their generation. The applications of the MDs on the QSP/AR have also been reported in this review. The MDs can be classified into experimental and theoretical types, having a sub classification of the later into structural and quantum chemical descriptors. The structural parameters are derived from molecular graphs or topology of the molecules. Even the pixel of the molecular image can be used as molecular descriptor. In QSPR studies the physicochemical properties include boiling point, heat capacity, density, refractive index, molar volume, surface tension, heat of formation, octanol-water partition coefficient, solubility, chromatographic retention indices etc. Among biological activities toxicity, antimalarial activity, sensory irritant, potencies of local anesthetic, tadpole narcosis, antifungal activity, enzyme inhibiting activity are some important parameters in the QSAR studies. The classification of the MDs is mostly generic in nature. The application of the MDs in QSP/AR also has a generic link. Experimental MDs are more suitable in correlation analysis than the theoretical ones but are more expensive for generation. In advent of sophisticated computational tools and experimental design proliferation of MDs is inevitable, but for a highly optimized MD, studies on generation of MD is an unending process.

Systems Biological Approach of Molecular Descriptors Connectivity: Optimal Descriptors for Oral Bioavailability Prediction

PubMed Central

Ahmed, Shiek S. S. J.; Ramakrishnan, V.

2012-01-01

Background Poor oral bioavailability is an important parameter accounting for the failure of the drug candidates. Approximately, 50% of developing drugs fail because of unfavorable oral bioavailability. In silico prediction of oral bioavailability (%F) based on physiochemical properties are highly needed. Although many computational models have been developed to predict oral bioavailability, their accuracy remains low with a significant number of false positives. In this study, we present an oral bioavailability model based on systems biological approach, using a machine learning algorithm coupled with an optimal discriminative set of physiochemical properties. Results The models were developed based on computationally derived 247 physicochemical descriptors from 2279 molecules, among which 969, 605 and 705 molecules were corresponds to oral bioavailability, intestinal absorption (HIA) and caco-2 permeability data set, respectively. The partial least squares discriminate analysis showed 49 descriptors of HIA and 50 descriptors of caco-2 are the major contributing descriptors in classifying into groups. Of these descriptors, 47 descriptors were commonly associated to HIA and caco-2, which suggests to play a vital role in classifying oral bioavailability. To determine the best machine learning algorithm, 21 classifiers were compared using a bioavailability data set of 969 molecules with 47 descriptors. Each molecule in the data set was represented by a set of 47 physiochemical properties with the functional relevance labeled as (+bioavailability/−bioavailability) to indicate good-bioavailability/poor-bioavailability molecules. The best-performing algorithm was the logistic algorithm. The correlation based feature selection (CFS) algorithm was implemented, which confirms that these 47 descriptors are the fundamental descriptors for oral bioavailability prediction. Conclusion The logistic algorithm with 47 selected descriptors correctly predicted the oral bioavailability, with a predictive accuracy of more than 71%. Overall, the method captures the fundamental molecular descriptors, that can be used as an entity to facilitate prediction of oral bioavailability. PMID:22815781

Systems biological approach of molecular descriptors connectivity: optimal descriptors for oral bioavailability prediction.

PubMed

Ahmed, Shiek S S J; Ramakrishnan, V

2012-01-01

Poor oral bioavailability is an important parameter accounting for the failure of the drug candidates. Approximately, 50% of developing drugs fail because of unfavorable oral bioavailability. In silico prediction of oral bioavailability (%F) based on physiochemical properties are highly needed. Although many computational models have been developed to predict oral bioavailability, their accuracy remains low with a significant number of false positives. In this study, we present an oral bioavailability model based on systems biological approach, using a machine learning algorithm coupled with an optimal discriminative set of physiochemical properties. The models were developed based on computationally derived 247 physicochemical descriptors from 2279 molecules, among which 969, 605 and 705 molecules were corresponds to oral bioavailability, intestinal absorption (HIA) and caco-2 permeability data set, respectively. The partial least squares discriminate analysis showed 49 descriptors of HIA and 50 descriptors of caco-2 are the major contributing descriptors in classifying into groups. Of these descriptors, 47 descriptors were commonly associated to HIA and caco-2, which suggests to play a vital role in classifying oral bioavailability. To determine the best machine learning algorithm, 21 classifiers were compared using a bioavailability data set of 969 molecules with 47 descriptors. Each molecule in the data set was represented by a set of 47 physiochemical properties with the functional relevance labeled as (+bioavailability/-bioavailability) to indicate good-bioavailability/poor-bioavailability molecules. The best-performing algorithm was the logistic algorithm. The correlation based feature selection (CFS) algorithm was implemented, which confirms that these 47 descriptors are the fundamental descriptors for oral bioavailability prediction. The logistic algorithm with 47 selected descriptors correctly predicted the oral bioavailability, with a predictive accuracy of more than 71%. Overall, the method captures the fundamental molecular descriptors, that can be used as an entity to facilitate prediction of oral bioavailability.

Graph Theoretical Representation of Atomic Asymmetry and Molecular Chirality of Benzenoids in Two-Dimensional Space

PubMed Central

Zhao, Tanfeng; Zhang, Qingyou; Long, Hailin; Xu, Lu

2014-01-01

In order to explore atomic asymmetry and molecular chirality in 2D space, benzenoids composed of 3 to 11 hexagons in 2D space were enumerated in our laboratory. These benzenoids are regarded as planar connected polyhexes and have no internal holes; that is, their internal regions are filled with hexagons. The produced dataset was composed of 357,968 benzenoids, including more than 14 million atoms. Rather than simply labeling the huge number of atoms as being either symmetric or asymmetric, this investigation aims at exploring a quantitative graph theoretical descriptor of atomic asymmetry. Based on the particular characteristics in the 2D plane, we suggested the weighted atomic sum as the descriptor of atomic asymmetry. This descriptor is measured by circulating around the molecule going in opposite directions. The investigation demonstrates that the weighted atomic sums are superior to the previously reported quantitative descriptor, atomic sums. The investigation of quantitative descriptors also reveals that the most asymmetric atom is in a structure with a spiral ring with the convex shape going in clockwise direction and concave shape going in anticlockwise direction from the atom. Based on weighted atomic sums, a weighted F index is introduced to quantitatively represent molecular chirality in the plane, rather than merely regarding benzenoids as being either chiral or achiral. By validating with enumerated benzenoids, the results indicate that the weighted F indexes were in accordance with their chiral classification (achiral or chiral) over the whole benzenoids dataset. Furthermore, weighted F indexes were superior to previously available descriptors. Benzenoids possess a variety of shapes and can be extended to practically represent any shape in 2D space—our proposed descriptor has thus the potential to be a general method to represent 2D molecular chirality based on the difference between clockwise and anticlockwise sums around a molecule. PMID:25032832

A Quantitative Structure-Property Relationship (QSPR) Study of Aliphatic Alcohols by the Method of Dividing the Molecular Structure into Substructure

PubMed Central

Liu, Fengping; Cao, Chenzhong; Cheng, Bin

2011-01-01

A quantitative structure–property relationship (QSPR) analysis of aliphatic alcohols is presented. Four physicochemical properties were studied: boiling point (BP), n-octanol–water partition coefficient (lg POW), water solubility (lg W) and the chromatographic retention indices (RI) on different polar stationary phases. In order to investigate the quantitative structure–property relationship of aliphatic alcohols, the molecular structure ROH is divided into two parts, R and OH to generate structural parameter. It was proposed that the property is affected by three main factors for aliphatic alcohols, alkyl group R, substituted group OH, and interaction between R and OH. On the basis of the polarizability effect index (PEI), previously developed by Cao, the novel molecular polarizability effect index (MPEI) combined with odd-even index (OEI), the sum eigenvalues of bond-connecting matrix (SX1CH) previously developed in our team, were used to predict the property of aliphatic alcohols. The sets of molecular descriptors were derived directly from the structure of the compounds based on graph theory. QSPR models were generated using only calculated descriptors and multiple linear regression techniques. These QSPR models showed high values of multiple correlation coefficient (R > 0.99) and Fisher-ratio statistics. The leave-one-out cross-validation demonstrated the final models to be statistically significant and reliable. PMID:21731451

In silico prediction of ROCK II inhibitors by different classification approaches.

PubMed

Cai, Chuipu; Wu, Qihui; Luo, Yunxia; Ma, Huili; Shen, Jiangang; Zhang, Yongbin; Yang, Lei; Chen, Yunbo; Wen, Zehuai; Wang, Qi

2017-11-01

ROCK II is an important pharmacological target linked to central nervous system disorders such as Alzheimer's disease. The purpose of this research is to generate ROCK II inhibitor prediction models by machine learning approaches. Firstly, four sets of descriptors were calculated with MOE 2010 and PaDEL-Descriptor, and optimized by F-score and linear forward selection methods. In addition, four classification algorithms were used to initially build 16 classifiers with k-nearest neighbors [Formula: see text], naïve Bayes, Random forest, and support vector machine. Furthermore, three sets of structural fingerprint descriptors were introduced to enhance the predictive capacity of classifiers, which were assessed with fivefold cross-validation, test set validation and external test set validation. The best two models, MFK + MACCS and MLR + SubFP, have both MCC values of 0.925 for external test set. After that, a privileged substructure analysis was performed to reveal common chemical features of ROCK II inhibitors. Finally, binding modes were analyzed to identify relationships between molecular descriptors and activity, while main interactions were revealed by comparing the docking interaction of the most potent and the weakest ROCK II inhibitors. To the best of our knowledge, this is the first report on ROCK II inhibitors utilizing machine learning approaches that provides a new method for discovering novel ROCK II inhibitors.

Molecular structure, spectral studies, NBO, HOMO-LUMO profile, MEP and Mulliken analysis of 3β,6β-dichloro-5α-hydroxy-5α-cholestane

NASA Astrophysics Data System (ADS)

Alam, Mahboob; Park, Soonheum

2018-05-01

The synthesis of 3β,6β-dichloro-5α-hydroxy-5α-cholestane (in general, steroidal chlorohydrin or steroidal halohydrin) and theoretical study of the structure are reported in this paper. The individuality of chlorohydrin was confirmed by FT-IR, NMR, MS, CHN microanalysis and X-ray crystallography. DFT calculations on the titled molecule have been performed. The molecular structure and spectra explained by Gaussian hybrid computational analysis theory (B3LYP) are found to be in correlation with the experimental data obtained from the various spectrophotometric techniques. The theoretical geometry optimization data were compared with the X-ray data. The vibrational bands appearing in the FT-IR are assigned with accuracy using harmonic frequencies along with intensities and animated modes. Molecular properties like NBO, HOMO-LUMO analysis, chemical reactivity descriptors, MEP mapping and dipole moment have been dealt at same level of theory. The calculated electronic spectrum of chlorohydrin is interpreted on the basis of TD-DFT calculations.

DFT approach to (benzylthio)acetic acid: Conformational search, molecular (monomer and dimer) structure, vibrational spectroscopy and some electronic properties

NASA Astrophysics Data System (ADS)

Sienkiewicz-Gromiuk, Justyna

2018-01-01

The DFT studies were carried out with the B3LYP method utilizing the 6-31G and 6-311++G(d,p) basis sets depending on whether the aim of calculations was to gain the geometry at equilibrium, or to calculate the optimized molecular structure of (benzylthio)acetic acid (Hbta) in the forms of monomer and dimer. The minimum conformational energy search was followed by the potential energy surface (PES) scan of all rotary bonds existing in the acid molecule. The optimized geometrical monomeric and dimeric structures of the title compound were compared with the experimental structural data in the solid state. The detailed vibrational interpretation of experimental infrared and Raman bands was performed on the basis of theoretically simulated ESFF-scaled wavenumbers calculated for the monomer and dimer structures of Hbta. The electronic characteristics of Hbta is also presented in terms of Mulliken atomic charges, frontier molecular orbitals and global reactivity descriptors. Additionally, the MEP and ESP surfaces were computed to predict coordination sites for potential metal complex formation.

Effect of number of probes and their orientation on the calculation of several compressor face distortion descriptors

NASA Technical Reports Server (NTRS)

Stoll, F.; Tremback, J. W.; Arnaiz, H. H.

1979-01-01

A study was performed to determine the effects of the number and position of total pressure probes on the calculation of five compressor face distortion descriptors. This study used three sets of 320 steady state total pressure measurements that were obtained with a special rotating rake apparatus in wind tunnel tests of a mixed-compression inlet. The inlet was a one third scale model of the inlet on a YF-12 airplane, and it was tested in the wind tunnel at representative flight conditions at Mach numbers above 2.0. The study shows that large errors resulted in the calculation of the distortion descriptors even with a number of probes that were considered adequate in the past. There were errors as large as 30 and -50 percent in several distortion descriptors for a configuration consisting of eight rakes with five equal-area-weighted probes on each rake.

QuBiLS-MAS, open source multi-platform software for atom- and bond-based topological (2D) and chiral (2.5D) algebraic molecular descriptors computations.

PubMed

Valdés-Martiní, José R; Marrero-Ponce, Yovani; García-Jacas, César R; Martinez-Mayorga, Karina; Barigye, Stephen J; Vaz d'Almeida, Yasser Silveira; Pham-The, Hai; Pérez-Giménez, Facundo; Morell, Carlos A

2017-06-07

In previous reports, Marrero-Ponce et al. proposed algebraic formalisms for characterizing topological (2D) and chiral (2.5D) molecular features through atom- and bond-based ToMoCoMD-CARDD (acronym for Topological Molecular Computational Design-Computer Aided Rational Drug Design) molecular descriptors. These MDs codify molecular information based on the bilinear, quadratic and linear algebraic forms and the graph-theoretical electronic-density and edge-adjacency matrices in order to consider atom- and bond-based relations, respectively. These MDs have been successfully applied in the screening of chemical compounds of different therapeutic applications ranging from antimalarials, antibacterials, tyrosinase inhibitors and so on. To compute these MDs, a computational program with the same name was initially developed. However, this in house software barely offered the functionalities required in contemporary molecular modeling tasks, in addition to the inherent limitations that made its usability impractical. Therefore, the present manuscript introduces the QuBiLS-MAS (acronym for Quadratic, Bilinear and N-Linear mapS based on graph-theoretic electronic-density Matrices and Atomic weightingS) software designed to compute topological (0-2.5D) molecular descriptors based on bilinear, quadratic and linear algebraic forms for atom- and bond-based relations. The QuBiLS-MAS module was designed as standalone software, in which extensions and generalizations of the former ToMoCoMD-CARDD 2D-algebraic indices are implemented, considering the following aspects: (a) two new matrix normalization approaches based on double-stochastic and mutual probability formalisms; (b) topological constraints (cut-offs) to take into account particular inter-atomic relations; (c) six additional atomic properties to be used as weighting schemes in the calculation of the molecular vectors; (d) four new local-fragments to consider molecular regions of interest; (e) number of lone-pair electrons in chemical structure defined by diagonal coefficients in matrix representations; and (f) several aggregation operators (invariants) applied over atom/bond-level descriptors in order to compute global indices. This software permits the parallel computation of the indices, contains a batch processing module and data curation functionalities. This program was developed in Java v1.7 using the Chemistry Development Kit library (version 1.4.19). The QuBiLS-MAS software consists of two components: a desktop interface (GUI) and an API library allowing for the easy integration of the latter in chemoinformatics applications. The relevance of the novel extensions and generalizations implemented in this software is demonstrated through three studies. Firstly, a comparative Shannon's entropy based variability study for the proposed QuBiLS-MAS and the DRAGON indices demonstrates superior performance for the former. A principal component analysis reveals that the QuBiLS-MAS approach captures chemical information orthogonal to that codified by the DRAGON descriptors. Lastly, a QSAR study for the binding affinity to the corticosteroid-binding globulin using Cramer's steroid dataset is carried out. From these analyses, it is revealed that the QuBiLS-MAS approach for atom-pair relations yields similar-to-superior performance with regard to other QSAR methodologies reported in the literature. Therefore, the QuBiLS-MAS approach constitutes a useful tool for the diversity analysis of chemical compound datasets and high-throughput screening of structure-activity data.

Simple idea to generate fragment and pharmacophore descriptors and their implications in chemical informatics.

PubMed

Catana, Cornel

2009-03-01

Using a well-defined set of fragments/pharmacophores, a new methodology to calculate fragment/ pharmacophore descriptors for any molecule onto which at least one fragment/pharmacophore can be mapped is presented. To each fragment/pharmacophore present in a molecule, we attach a descriptor that is calculated by identifying the molecule's atoms onto which it maps and summing over its constituent atomic descriptors. The attached descriptors are named C-fragment/pharmacophore descriptors, and this methodology can be applied to any descriptors defined at the atomic level, such as the partition coefficient, molar refractivity, electrotopological state, etc. By using this methodology, the same fragment/pharmacophore can be shown to have different values in different molecules resulting in better discrimination power. As we know, fragment and pharmacophore fingerprints have a lot of applications in chemical informatics. This study has attempted to find the impact of replacing the traditional value of "1" in a fingerprint with real numbers derived form C-fragment/pharmacophore descriptors. One way to do this is to assess the utility of C-fragment/ pharmacophore descriptors in modeling different end points. Here, we exemplify with data from CYP and hERG. The fact that, in many cases, the obtained models were fairly successful and C-fragment descriptors were ranked among the top ones supports the idea that they play an important role in correlation. When we modeled hERG with C-pharmacophore descriptors, however, the model performances decreased slightly, and we attribute this, mainly to the fact that there is no technique capable of handling multiple instances (states). We hope this will open new research, especially in the emerging field of machine learning. Further research is needed to see the impact of C-fragment/pharmacophore descriptors in similarity/dissimilarity applications.

Computational Prediction of the Protonation Sites of Ac-Lys-(Ala)n-Lys-NH2 Peptides through Conceptual DFT Descriptors.

PubMed

Sastre, Sebastián; Frau, Juan; Glossman-Mitnik, Daniel

2017-03-13

Six density functionals (M11, M11L, MN12L, MN12SX, N12, and N12SX) in connection with the Def2TZVP basis set and the SMD solvation model (water as a solvent) have been assessed for the calculation of the molecular structure and properties of several peptides with the general formulaAc-Lys-(Ala)n-Lys-NH2,withn=0to5  [...].

Linear and nonlinear models for predicting fish bioconcentration factors for pesticides.

PubMed

Yuan, Jintao; Xie, Chun; Zhang, Ting; Sun, Jinfang; Yuan, Xuejie; Yu, Shuling; Zhang, Yingbiao; Cao, Yunyuan; Yu, Xingchen; Yang, Xuan; Yao, Wu

2016-08-01

This work is devoted to the applications of the multiple linear regression (MLR), multilayer perceptron neural network (MLP NN) and projection pursuit regression (PPR) to quantitative structure-property relationship analysis of bioconcentration factors (BCFs) of pesticides tested on Bluegill (Lepomis macrochirus). Molecular descriptors of a total of 107 pesticides were calculated with the DRAGON Software and selected by inverse enhanced replacement method. Based on the selected DRAGON descriptors, a linear model was built by MLR, nonlinear models were developed using MLP NN and PPR. The robustness of the obtained models was assessed by cross-validation and external validation using test set. Outliers were also examined and deleted to improve predictive power. Comparative results revealed that PPR achieved the most accurate predictions. This study offers useful models and information for BCF prediction, risk assessment, and pesticide formulation. Copyright © 2016 Elsevier Ltd. All rights reserved.

Structure-Activity Relationships for Rates of Aromatic Amine Oxidation by Manganese Dioxide

DOE PAGES

Salter-Blanc, Alexandra J.; Bylaska, Eric J.; Lyon, Molly A.; ...

2016-04-13

New energetic compounds are designed to minimize their potential environmental impacts, which includes their transformation and the fate and effects of their transformation products. The nitro groups of energetic compounds are readily reduced to amines, and the resulting aromatic amines are subject to oxidation and coupling reactions. Manganese dioxide (MnO 2) is a common environmental oxidant and model system for kinetic studies of aromatic amine oxidation. Here in this study, a training set of new and previously reported kinetic data for the oxidation of model and energetic-derived aromatic amines was assembled and subjected to correlation analysis against descriptor variables that ranged from general purpose [Hammettmore » $$\\sigma$$ constants ($$\\sigma^-$$), pK as of the amines, and energies of the highest occupied molecular orbital (E HOMO)] to specific for the likely rate-limiting step [one-electron oxidation potentials (E ox)]. The selection of calculated descriptors (pK a), E HOMO, and E ox) was based on validation with experimental data. All of the correlations gave satisfactory quantitative structure-activity relationships (QSARs), but they improved with the specificity of the descriptor. The scope of correlation analysis was extended beyond MnO 2 to include literature data on aromatic amine oxidation by other environmentally relevant oxidants (ozone, chlorine dioxide, and phosphate and carbonate radicals) by correlating relative rate constants (normalized to 4-chloroaniline) to E HOMO (calculated with a modest level of theory).« less

Structure-Activity Relationships for Rates of Aromatic Amine Oxidation by Manganese Dioxide

DOE Office of Scientific and Technical Information (OSTI.GOV)

Salter-Blanc, Alexandra J.; Bylaska, Eric J.; Lyon, Molly A.

New energetic compounds are designed to minimize their potential environmental impacts, which includes their transformation and the fate and effects of their transformation products. The nitro groups of energetic compounds are readily reduced to amines, and the resulting aromatic amines are subject to oxidation and coupling reactions. Manganese dioxide (MnO 2) is a common environmental oxidant and model system for kinetic studies of aromatic amine oxidation. Here in this study, a training set of new and previously reported kinetic data for the oxidation of model and energetic-derived aromatic amines was assembled and subjected to correlation analysis against descriptor variables that ranged from general purpose [Hammettmore » $$\\sigma$$ constants ($$\\sigma^-$$), pK as of the amines, and energies of the highest occupied molecular orbital (E HOMO)] to specific for the likely rate-limiting step [one-electron oxidation potentials (E ox)]. The selection of calculated descriptors (pK a), E HOMO, and E ox) was based on validation with experimental data. All of the correlations gave satisfactory quantitative structure-activity relationships (QSARs), but they improved with the specificity of the descriptor. The scope of correlation analysis was extended beyond MnO 2 to include literature data on aromatic amine oxidation by other environmentally relevant oxidants (ozone, chlorine dioxide, and phosphate and carbonate radicals) by correlating relative rate constants (normalized to 4-chloroaniline) to E HOMO (calculated with a modest level of theory).« less

A rotation-translation invariant molecular descriptor of partial charges and its use in ligand-based virtual screening

PubMed Central

2014-01-01

Background Measures of similarity for chemical molecules have been developed since the dawn of chemoinformatics. Molecular similarity has been measured by a variety of methods including molecular descriptor based similarity, common molecular fragments, graph matching and 3D methods such as shape matching. Similarity measures are widespread in practice and have proven to be useful in drug discovery. Because of our interest in electrostatics and high throughput ligand-based virtual screening, we sought to exploit the information contained in atomic coordinates and partial charges of a molecule. Results A new molecular descriptor based on partial charges is proposed. It uses the autocorrelation function and linear binning to encode all atoms of a molecule into two rotation-translation invariant vectors. Combined with a scoring function, the descriptor allows to rank-order a database of compounds versus a query molecule. The proposed implementation is called ACPC (AutoCorrelation of Partial Charges) and released in open source. Extensive retrospective ligand-based virtual screening experiments were performed and other methods were compared with in order to validate the method and associated protocol. Conclusions While it is a simple method, it performed remarkably well in experiments. At an average speed of 1649 molecules per second, it reached an average median area under the curve of 0.81 on 40 different targets; hence validating the proposed protocol and implementation. PMID:24887178

On the history of the connectivity index: from the connectivity index to the exact solution of the protein alignment problem.

PubMed

Randić, M

2015-01-01

We briefly review the history of the connectivity index from 1975 to date. We hope to throw some light on why this unique, by its design, graph theoretical molecular descriptor continues to be of interest in QSAR, having wide use in applications in structure-property and structure-activity studies. We will elaborate on its generalizations and the insights it offered on applications in Multiple Regression Analysis (MRA). Going beyond the connectivity index we will outline several related developments in the development of molecular descriptors used in MRA, including molecular ID numbers (1986), the variable connectivity index (1991), orthogonal regression (1991), irrelevance of co-linearity of descriptors (1997), anti-connectivity (2006), and high discriminatory descriptors characterizing molecular similarity (2015). We will comment on beauty in QSAR and recent progress in searching for similarity of DNA, proteins and the proteome. This review reports on several results which are little known to the structure-property-activity community, the significance of which may surprise those unfamiliar with the application of discrete mathematics to chemistry. It tells the reader many unknown stories about the connectivity index, which may help the reader to better understand the meaning of this index. Readers are not required to be familiar with graph theory.

Quantum descriptors for predictive toxicology of halogenated aliphatic hydrocarbons.

PubMed

Trohalaki, S; Pachter, R

2003-04-01

In order to improve Quantitative Structure-Activity Relationships (QSARs) for halogenated aliphatics (HA) and to better understand the biophysical mechanism of toxic response to these ubiquitous chemicals, we employ improved quantum-mechanical descriptors to account for HA electrophilicity. We demonstrate that, unlike the lowest unoccupied molecular orbital energy, ELUMO, which was previously used as a descriptor, the electron affinity can be systematically improved by application of higher levels of theory. We also show that employing the reciprocal of ELUMO, which is more consistent with frontier molecular orbital (FMO) theory, improves the correlations with in vitro toxicity data. We offer explanations based on FMO theory for a result from our previous work, in which the LUMO energies of HA anions correlated surprisingly well with in vitro toxicity data. Additional descriptors are also suggested and interpreted in terms of the accepted biophysical mechanism of toxic response to HAs and new QSARs are derived for various chemical categories that compose the data set employed. These alternate descriptors provide important insight and could benefit other classes of compounds where the biophysical mechanism of toxic response involves dissociative attachment.

A machine learning approach for ranking clusters of docked protein‐protein complexes by pairwise cluster comparison

PubMed Central

Pfeiffenberger, Erik; Chaleil, Raphael A.G.; Moal, Iain H.

2017-01-01

ABSTRACT Reliable identification of near‐native poses of docked protein–protein complexes is still an unsolved problem. The intrinsic heterogeneity of protein–protein interactions is challenging for traditional biophysical or knowledge based potentials and the identification of many false positive binding sites is not unusual. Often, ranking protocols are based on initial clustering of docked poses followed by the application of an energy function to rank each cluster according to its lowest energy member. Here, we present an approach of cluster ranking based not only on one molecular descriptor (e.g., an energy function) but also employing a large number of descriptors that are integrated in a machine learning model, whereby, an extremely randomized tree classifier based on 109 molecular descriptors is trained. The protocol is based on first locally enriching clusters with additional poses, the clusters are then characterized using features describing the distribution of molecular descriptors within the cluster, which are combined into a pairwise cluster comparison model to discriminate near‐native from incorrect clusters. The results show that our approach is able to identify clusters containing near‐native protein–protein complexes. In addition, we present an analysis of the descriptors with respect to their power to discriminate near native from incorrect clusters and how data transformations and recursive feature elimination can improve the ranking performance. Proteins 2017; 85:528–543. © 2016 Wiley Periodicals, Inc. PMID:27935158

Improving virtual screening predictive accuracy of Human kallikrein 5 inhibitors using machine learning models.

PubMed

Fang, Xingang; Bagui, Sikha; Bagui, Subhash

2017-08-01

The readily available high throughput screening (HTS) data from the PubChem database provides an opportunity for mining of small molecules in a variety of biological systems using machine learning techniques. From the thousands of available molecular descriptors developed to encode useful chemical information representing the characteristics of molecules, descriptor selection is an essential step in building an optimal quantitative structural-activity relationship (QSAR) model. For the development of a systematic descriptor selection strategy, we need the understanding of the relationship between: (i) the descriptor selection; (ii) the choice of the machine learning model; and (iii) the characteristics of the target bio-molecule. In this work, we employed the Signature descriptor to generate a dataset on the Human kallikrein 5 (hK 5) inhibition confirmatory assay data and compared multiple classification models including logistic regression, support vector machine, random forest and k-nearest neighbor. Under optimal conditions, the logistic regression model provided extremely high overall accuracy (98%) and precision (90%), with good sensitivity (65%) in the cross validation test. In testing the primary HTS screening data with more than 200K molecular structures, the logistic regression model exhibited the capability of eliminating more than 99.9% of the inactive structures. As part of our exploration of the descriptor-model-target relationship, the excellent predictive performance of the combination of the Signature descriptor and the logistic regression model on the assay data of the Human kallikrein 5 (hK 5) target suggested a feasible descriptor/model selection strategy on similar targets. Copyright © 2017 Elsevier Ltd. All rights reserved.

Uniting Cheminformatics and Chemical Theory To Predict the Intrinsic Aqueous Solubility of Crystalline Druglike Molecules

PubMed Central

2014-01-01

We present four models of solution free-energy prediction for druglike molecules utilizing cheminformatics descriptors and theoretically calculated thermodynamic values. We make predictions of solution free energy using physics-based theory alone and using machine learning/quantitative structure–property relationship (QSPR) models. We also develop machine learning models where the theoretical energies and cheminformatics descriptors are used as combined input. These models are used to predict solvation free energy. While direct theoretical calculation does not give accurate results in this approach, machine learning is able to give predictions with a root mean squared error (RMSE) of ∼1.1 log S units in a 10-fold cross-validation for our Drug-Like-Solubility-100 (DLS-100) dataset of 100 druglike molecules. We find that a model built using energy terms from our theoretical methodology as descriptors is marginally less predictive than one built on Chemistry Development Kit (CDK) descriptors. Combining both sets of descriptors allows a further but very modest improvement in the predictions. However, in some cases, this is a statistically significant enhancement. These results suggest that there is little complementarity between the chemical information provided by these two sets of descriptors, despite their different sources and methods of calculation. Our machine learning models are also able to predict the well-known Solubility Challenge dataset with an RMSE value of 0.9–1.0 log S units. PMID:24564264

An Analysis of Descriptors of Volatile Organic Compounds and Their Impact on Rate Constant for Reaction with Hydroxyl Radicals

DTIC Science & Technology

2018-05-01

the descriptors were correlated to experimental rate constants. The five descriptors fell into one of two categories: whole molecule descriptors or...model based on these correlations . Although that goal was not achieved in full, considerable progress has been made, and there is potential for a...readme.txt) and compiled. We then searched for correlations between the calculated properties from theory and the experimental measurements of reaction rate

Evaluation of a novel electronic eigenvalue (EEVA) molecular descriptor for QSAR/QSPR studies: validation using a benchmark steroid data set.

PubMed

Tuppurainen, Kari; Viisas, Marja; Laatikainen, Reino; Peräkylä, Mikael

2002-01-01

A novel electronic eigenvalue (EEVA) descriptor of molecular structure for use in the derivation of predictive QSAR/QSPR models is described. Like other spectroscopic QSAR/QSPR descriptors, EEVA is also invariant as to the alignment of the structures concerned. Its performance was tested with respect to the CBG (corticosteroid binding globulin) affinity of 31 benchmark steroids. It appeared that the electronic structure of the steroids, i.e., the "spectra" derived from molecular orbital energies, is directly related to the CBG binding affinities. The predictive ability of EEVA is compared to other QSAR approaches, and its performance is discussed in the context of the Hammett equation. The good performance of EEVA is an indication of the essential quantum mechanical nature of QSAR. The EEVA method is a supplement to conventional 3D QSAR methods, which employ fields or surface properties derived from Coulombic and van der Waals interactions.

Quantitative structure-retention relationships applied to development of liquid chromatography gradient-elution method for the separation of sartans.

PubMed

Golubović, Jelena; Protić, Ana; Otašević, Biljana; Zečević, Mira

2016-04-01

QSRR are mathematically derived relationships between the chromatographic parameters determined for a representative series of analytes in given separation systems and the molecular descriptors accounting for the structural differences among the investigated analytes. Artificial neural network is a technique of data analysis, which sets out to emulate the human brain's way of working. The aim of the present work was to optimize separation of six angiotensin receptor antagonists, so-called sartans: losartan, valsartan, irbesartan, telmisartan, candesartan cilexetil and eprosartan in a gradient-elution HPLC method. For this purpose, ANN as a mathematical tool was used for establishing a QSRR model based on molecular descriptors of sartans and varied instrumental conditions. The optimized model can be further used for prediction of an external congener of sartans and analysis of the influence of the analyte structure, represented through molecular descriptors, on retention behaviour. Molecular descriptors included in modelling were electrostatic, geometrical and quantum-chemical descriptors: connolly solvent excluded volume non-1,4 van der Waals energy, octanol/water distribution coefficient, polarizability, number of proton-donor sites and number of proton-acceptor sites. Varied instrumental conditions were gradient time, buffer pH and buffer molarity. High prediction ability of the optimized network enabled complete separation of the analytes within the run time of 15.5 min under following conditions: gradient time of 12.5 min, buffer pH of 3.95 and buffer molarity of 25 mM. Applied methodology showed the potential to predict retention behaviour of an external analyte with the properties within the training space. Connolly solvent excluded volume, polarizability and number of proton-acceptor sites appeared to be most influential paramateres on retention behaviour of the sartans. Copyright © 2015 Elsevier B.V. All rights reserved.

Prediction of boiling points of organic compounds by QSPR tools.

PubMed

Dai, Yi-min; Zhu, Zhi-ping; Cao, Zhong; Zhang, Yue-fei; Zeng, Ju-lan; Li, Xun

2013-07-01

The novel electro-negativity topological descriptors of YC, WC were derived from molecular structure by equilibrium electro-negativity of atom and relative bond length of molecule. The quantitative structure-property relationships (QSPR) between descriptors of YC, WC as well as path number parameter P3 and the normal boiling points of 80 alkanes, 65 unsaturated hydrocarbons and 70 alcohols were obtained separately. The high-quality prediction models were evidenced by coefficient of determination (R(2)), the standard error (S), average absolute errors (AAE) and predictive parameters (Qext(2),RCV(2),Rm(2)). According to the regression equations, the influences of the length of carbon backbone, the size, the degree of branching of a molecule and the role of functional groups on the normal boiling point were analyzed. Comparison results with reference models demonstrated that novel topological descriptors based on the equilibrium electro-negativity of atom and the relative bond length were useful molecular descriptors for predicting the normal boiling points of organic compounds. Copyright © 2013 Elsevier Inc. All rights reserved.

QSPR study of polychlorinated diphenyl ethers by molecular electronegativity distance vector (MEDV-4).

PubMed

Sun, Lili; Zhou, Liping; Yu, Yu; Lan, Yukun; Li, Zhiliang

2007-01-01

Polychlorinated diphenyl ethers (PCDEs) have received more and more concerns as a group of ubiquitous potential persistent organic pollutants (POPs). By using molecular electronegativity distance vector (MEDV-4), multiple linear regression (MLR) models are developed for sub-cooled liquid vapor pressures (P(L)), n-octanol/water partition coefficients (K(OW)) and sub-cooled liquid water solubilities (S(W,L)) of 209 PCDEs and diphenyl ether. The correlation coefficients (R) and the leave-one-out cross-validation (LOO) correlation coefficients (R(CV)) of all the 6-descriptor models for logP(L), logK(OW) and logS(W,L) are more than 0.98. By using stepwise multiple regression (SMR), the descriptors are selected and the resulting models are 5-descriptor model for logP(L), 4-descriptor model for logK(OW), and 6-descriptor model for logS(W,L), respectively. All these models exhibit excellent estimate capabilities for internal sample set and good predictive capabilities for external samples set. The consistency between observed and estimated/predicted values for logP(L) is the best (R=0.996, R(CV)=0.996), followed by logK(OW) (R=0.992, R(CV)=0.992) and logS(W,L) (R=0.983, R(CV)=0.980). By using MEDV-4 descriptors, the QSPR models can be used for prediction and the model predictions can hence extend the current database of experimental values.

Density functional theory fragment descriptors to quantify the reactivity of a molecular family: application to amino acids.

PubMed

Senet, P; Aparicio, F

2007-04-14

By using the exact density functional theory, one demonstrates that the value of the local electronic softness of a molecular fragment is directly related to the polarization charge (Coulomb hole) induced by a test electron removed (or added) from (at) the fragment. Our finding generalizes to a chemical group a formal relation between these molecular descriptors recently obtained for an atom in a molecule using an approximate atomistic model [P. Senet and M. Yang, J. Chem. Sci. 117, 411 (2005)]. In addition, a practical ab initio computational scheme of the Coulomb hole and related local descriptors of reactivity of a molecular family having in common a similar fragment is presented. As a blind test, the method is applied to the lateral chains of the 20 isolated amino acids. One demonstrates that the local softness of the lateral chain is a quantitative measure of the similarity of the amino acids. It predicts the separation of amino acids in different biochemical groups (aliphatic, basic, acidic, sulfur contained, and aromatic). The present approach may find applications in quantitative structure activity relationship methodology.

Approaching Pharmacological Space: Events and Components.

PubMed

Vistoli, Giulio; Pedretti, Alessandro; Mazzolari, Angelica; Testa, Bernard

2018-01-01

With a view to introducing the concept of pharmacological space and its potential applications in investigating and predicting the toxic mechanisms of xenobiotics, this opening chapter describes the logical relations between conformational behavior, physicochemical properties and binding spaces, which are seen as the three key elements composing the pharmacological space. While the concept of conformational space is routinely used to encode molecular flexibility, the concepts of property spaces and, particularly, of binding spaces are more innovative. Indeed, their descriptors can find fruitful applications (a) in describing the dynamic adaptability a given ligand experiences when inserted into a specific environment, and (b) in parameterizing the flexibility a ligand retains when bound to a biological target. Overall, these descriptors can conveniently account for the often disregarded entropic factors and as such they prove successful when inserted in ligand- or structure-based predictive models. Notably, and although binding space parameters can clearly be derived from MD simulations, the chapter will illustrate how docking calculations, despite their static nature, are able to evaluate ligand's flexibility by analyzing several poses for each ligand. Such an approach, which represents the founding core of the binding space concept, can find various applications in which the related descriptors show an impressive enhancing effect on the statistical performances of the resulting predictive models.

Discrimination of Active and Weakly Active Human BACE1 Inhibitors Using Self-Organizing Map and Support Vector Machine.

PubMed

Li, Hang; Wang, Maolin; Gong, Ya-Nan; Yan, Aixia

2016-01-01

β-secretase (BACE1) is an aspartyl protease, which is considered as a novel vital target in Alzheimer`s disease therapy. We collected a data set of 294 BACE1 inhibitors, and built six classification models to discriminate active and weakly active inhibitors using Kohonen's Self-Organizing Map (SOM) method and Support Vector Machine (SVM) method. Each molecular descriptor was calculated using the program ADRIANA.Code. We adopted two different methods: random method and Self-Organizing Map method, for training/test set split. The descriptors were selected by F-score and stepwise linear regression analysis. The best SVM model Model2C has a good prediction performance on test set with prediction accuracy, sensitivity (SE), specificity (SP) and Matthews correlation coefficient (MCC) of 89.02%, 90%, 88%, 0.78, respectively. Model 1A is the best SOM model, whose accuracy and MCC of the test set were 94.57% and 0.98, respectively. The lone pair electronegativity and polarizability related descriptors importantly contributed to bioactivity of BACE1 inhibitor. The Extended-Connectivity Finger-Prints_4 (ECFP_4) analysis found some vitally key substructural features, which could be helpful for further drug design research. The SOM and SVM models built in this study can be obtained from the authors by email or other contacts.

Utilizing Ion-Mobility Data to Estimate Molecular Masses

NASA Technical Reports Server (NTRS)

Duong, Tuan; Kanik, Isik

2008-01-01

A method is being developed for utilizing readings of an ion-mobility spectrometer (IMS) to estimate molecular masses of ions that have passed through the spectrometer. The method involves the use of (1) some feature-based descriptors of structures of molecules of interest and (2) reduced ion mobilities calculated from IMS readings as inputs to (3) a neural network. This development is part of a larger effort to enable the use of IMSs as relatively inexpensive, robust, lightweight instruments to identify, via molecular masses, individual compounds or groups of compounds (especially organic compounds) that may be present in specific environments or samples. Potential applications include detection of organic molecules as signs of life on remote planets, modeling and detection of biochemicals of interest in the pharmaceutical and agricultural industries, and detection of chemical and biological hazards in industrial, homeland-security, and industrial settings.

PyGlobal: A toolkit for automated compilation of DFT-based descriptors.

PubMed

Nath, Shilpa R; Kurup, Sudheer S; Joshi, Kaustubh A

2016-06-15

Density Functional Theory (DFT)-based Global reactivity descriptor calculations have emerged as powerful tools for studying the reactivity, selectivity, and stability of chemical and biological systems. A Python-based module, PyGlobal has been developed for systematically parsing a typical Gaussian outfile and extracting the relevant energies of the HOMO and LUMO. Corresponding global reactivity descriptors are further calculated and the data is saved into a spreadsheet compatible with applications like Microsoft Excel and LibreOffice. The efficiency of the module has been accounted by measuring the time interval for randomly selected Gaussian outfiles for 1000 molecules. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Surrogate data--a secure way to share corporate data.

PubMed

Tetko, Igor V; Abagyan, Ruben; Oprea, Tudor I

2005-01-01

The privacy of chemical structure is of paramount importance for the industrial sector, in particular for the pharmaceutical industry. At the same time, companies handle large amounts of physico-chemical and biological data that could be shared in order to improve our molecular understanding of pharmacokinetic and toxicological properties, which could lead to improved predictivity and shorten the development time for drugs, in particular in the early phases of drug discovery. The current study provides some theoretical limits on the information required to produce reverse engineering of molecules from generated descriptors and demonstrates that the information content of molecules can be as low as less than one bit per atom. Thus theoretically just one descriptor can be used to completely disclose the molecular structure. Instead of sharing descriptors, we propose to share surrogate data. The sharing of surrogate data is nothing else but sharing of reliably predicted molecules. The use of surrogate data can provide the same information as the original set. We consider the practical application of this idea to predict lipophilicity of chemical compounds and we demonstrate that surrogate and real (original) data provides similar prediction ability. Thus, our proposed strategy makes it possible not only to share descriptors, but also complete collections of surrogate molecules without the danger of disclosing the underlying molecular structures.

Prediction of blood-brain partitioning: a model based on molecular electronegativity distance vector descriptors.

PubMed

Zhang, Yong-Hong; Xia, Zhi-Ning; Qin, Li-Tang; Liu, Shu-Shen

2010-09-01

The objective of this paper is to build a reliable model based on the molecular electronegativity distance vector (MEDV) descriptors for predicting the blood-brain barrier (BBB) permeability and to reveal the effects of the molecular structural segments on the BBB permeability. Using 70 structurally diverse compounds, the partial least squares regression (PLSR) models between the BBB permeability and the MEDV descriptors were developed and validated by the variable selection and modeling based on prediction (VSMP) technique. The estimation ability, stability, and predictive power of a model are evaluated by the estimated correlation coefficient (r), leave-one-out (LOO) cross-validation correlation coefficient (q), and predictive correlation coefficient (R(p)). It has been found that PLSR model has good quality, r=0.9202, q=0.7956, and R(p)=0.6649 for M1 model based on the training set of 57 samples. To search the most important structural factors affecting the BBB permeability of compounds, we performed the values of the variable importance in projection (VIP) analysis for MEDV descriptors. It was found that some structural fragments in compounds, such as -CH(3), -CH(2)-, =CH-, =C, triple bond C-, -CH<, =C<, =N-, -NH-, =O, and -OH, are the most important factors affecting the BBB permeability. (c) 2010. Published by Elsevier Inc.

On the Preferred Active Sites of Promoted MoS 2 for Hydrodesulfurization with Minimal Organonitrogen Inhibition

DOE PAGES

Rangarajan, Srinivas; Mavrikakis, Manos

2016-12-14

Hydrodesulfurization is a process to produce ultralow-sulfur diesel fuel. Although promoted molybdenum sulfide (MoS 2) catalysts have been used industrially for several decades, the active site requirements for selective hydrodesulfurization of organosulfur compounds with minimal inhibition by organonitrogen constituents of a real gasoil feed has not been resolved. By using molecular binding energy descriptors derived from plane wave density functional theory calculations for comparative adsorption of organosulfur and organonitrogen compounds, we analyzed more than 20 potential sites on unpromoted and Ni- and Co-promoted MoS 2. We also found that hydrogen sulfide and ammonia are simple descriptors of adsorption of stericallymore » unhindered organosulfur and organonitrogen compounds such as dibenzothiophene and acridine, respectively. Further, organonitrogen compounds in gasoil bind more strongly than organosulfur compounds on all sites except on sites with exposed metal atoms on the corner and sulfur edges of promoted MoS 2. Consequently, these sites are proposed as required for maximum-hydrodesulfurization minimum-inhibition catalysis.« less

Novel coumarins and related copper complexes with biological activity: DNA binding, molecular docking and in vitro antiproliferative activity.

PubMed

Pivetta, Tiziana; Valletta, Elisa; Ferino, Giulio; Isaia, Francesco; Pani, Alessandra; Vascellari, Sarah; Castellano, Carlo; Demartin, Francesco; Cabiddu, Maria Grazia; Cadoni, Enzo

2017-12-01

Coumarins show biological activity and are widely exploited for their therapeutic effects. Although a great number of coumarins substituted by heterocyclic moieties have been prepared, few studies have been carried out on coumarins containing pyridine heterocycle, which is known to modulate their physiological activities. We prepared and characterized three novel 3-(pyridin-2-yl)coumarins and their corresponding copper(II) complexes. We extended our investigations also to three known similar coumarins, since no data about their biochemical activity was previously been reported. The antiproliferative activity of the studied compounds was tested against human derived tumor cell lines and one human normal cell line. The DNA binding constants were determined and docking studies with DNA carried out. Selected Quantitative Structure-Activity Relationship (QSAR) descriptors were calculated in order to relate a set of structural and topological descriptors of the studied compounds to their DNA interaction and cytotoxic activity. Copyright © 2017 Elsevier Inc. All rights reserved.

Calculation of Five Thermodynamic Molecular Descriptors by Means of a General Computer Algorithm Based on the Group-Additivity Method: Standard Enthalpies of Vaporization, Sublimation and Solvation, and Entropy of Fusion of Ordinary Organic Molecules and Total Phase-Change Entropy of Liquid Crystals.

PubMed

Naef, Rudolf; Acree, William E

2017-06-25

The calculation of the standard enthalpies of vaporization, sublimation and solvation of organic molecules is presented using a common computer algorithm on the basis of a group-additivity method. The same algorithm is also shown to enable the calculation of their entropy of fusion as well as the total phase-change entropy of liquid crystals. The present method is based on the complete breakdown of the molecules into their constituting atoms and their immediate neighbourhood; the respective calculations of the contribution of the atomic groups by means of the Gauss-Seidel fitting method is based on experimental data collected from literature. The feasibility of the calculations for each of the mentioned descriptors was verified by means of a 10-fold cross-validation procedure proving the good to high quality of the predicted values for the three mentioned enthalpies and for the entropy of fusion, whereas the predictive quality for the total phase-change entropy of liquid crystals was poor. The goodness of fit ( Q ²) and the standard deviation (σ) of the cross-validation calculations for the five descriptors was as follows: 0.9641 and 4.56 kJ/mol ( N = 3386 test molecules) for the enthalpy of vaporization, 0.8657 and 11.39 kJ/mol ( N = 1791) for the enthalpy of sublimation, 0.9546 and 4.34 kJ/mol ( N = 373) for the enthalpy of solvation, 0.8727 and 17.93 J/mol/K ( N = 2637) for the entropy of fusion and 0.5804 and 32.79 J/mol/K ( N = 2643) for the total phase-change entropy of liquid crystals. The large discrepancy between the results of the two closely related entropies is discussed in detail. Molecules for which both the standard enthalpies of vaporization and sublimation were calculable, enabled the estimation of their standard enthalpy of fusion by simple subtraction of the former from the latter enthalpy. For 990 of them the experimental enthalpy-of-fusion values are also known, allowing their comparison with predictions, yielding a correlation coefficient R ² of 0.6066.

Molecular structure, vibrational analysis (IR and Raman) and quantum chemical investigations of 1-aminoisoquinoline

NASA Astrophysics Data System (ADS)

Sivaprakash, S.; Prakash, S.; Mohan, S.; Jose, Sujin P.

2017-12-01

Quantum chemical calculations of energy and geometrical parameters of 1-aminoisoquinoline [1-AIQ] were carried out by using DFT/B3LYP method using 6-311G (d,p), 6-311G++(d,p) and cc-pVTZ basis sets. The vibrational wavenumbers were computed for the energetically most stable, optimized geometry. The vibrational assignments were performed on the basis of potential energy distribution (PED) using VEDA program. The NBO analysis was done to investigate the intra molecular charge transfer of the molecule. The frontier molecular orbital (FMO) analysis was carried out and the chemical reactivity descriptors of the molecule were studied. The Mulliken charge analysis, molecular electrostatic potential (MEP), HOMO-LUMO energy gap and the related properties were also investigated at B3LYP level. The absorption spectrum of the molecule was studied from UV-Visible analysis by using time-dependent density functional theory (TD-DFT). Fourier Transform Infrared spectrum (FT-IR) and Raman spectrum of 1-AIQ compound were analyzed and recorded in the range 4000-400 cm-1 and 3500-100 cm-1 respectively. The experimentally determined wavenumbers were compared with those calculated theoretically and they complement each other.

In silico design of anti-atherogenic biomaterials.

PubMed

Lewis, Daniel R; Kholodovych, Vladyslav; Tomasini, Michael D; Abdelhamid, Dalia; Petersen, Latrisha K; Welsh, William J; Uhrich, Kathryn E; Moghe, Prabhas V

2013-10-01

Atherogenesis, the uncontrolled deposition of modified lipoproteins in inflamed arteries, serves as a focal trigger of cardiovascular disease (CVD). Polymeric biomaterials have been envisioned to counteract atherogenesis based on their ability to repress scavenger mediated uptake of oxidized lipoprotein (oxLDL) in macrophages. Following the conceptualization in our laboratories of a new library of amphiphilic macromolecules (AMs), assembled from sugar backbones, aliphatic chains and poly(ethylene glycol) tails, a more rational approach is necessary to parse the diverse features such as charge, hydrophobicity, sugar composition and stereochemistry. In this study, we advance a computational biomaterials design approach to screen and elucidate anti-atherogenic biomaterials with high efficacy. AMs were quantified in terms of not only 1D (molecular formula) and 2D (molecular connectivity) descriptors, but also new 3D (molecular geometry) descriptors of AMs modeled by coarse-grained molecular dynamics (MD) followed by all-atom MD simulations. Quantitative structure-activity relationship (QSAR) models for anti-atherogenic activity were then constructed by screening a total of 1164 descriptors against the corresponding, experimentally measured potency of AM inhibition of oxLDL uptake in human monocyte-derived macrophages. Five key descriptors were identified to provide a strong linear correlation between the predicted and observed anti-atherogenic activity values, and were then used to correctly forecast the efficacy of three newly designed AMs. Thus, a new ligand-based drug design framework was successfully adapted to computationally screen and design biomaterials with cardiovascular therapeutic properties. Copyright © 2013 Elsevier Ltd. All rights reserved.

Selection of effective cocrystals former for dissolution rate improvement of active pharmaceutical ingredients based on lipoaffinity index.

PubMed

Cysewski, Piotr; Przybyłek, Maciej

2017-09-30

New theoretical screening procedure was proposed for appropriate selection of potential cocrystal formers possessing the ability of enhancing dissolution rates of drugs. The procedure relies on the training set comprising 102 positive and 17 negative cases of cocrystals found in the literature. Despite the fact that the only available data were of qualitative character, performed statistical analysis using binary classification allowed to formulate quantitative criterions. Among considered 3679 molecular descriptors the relative value of lipoaffinity index, expressed as the difference between values calculated for active compound and excipient, has been found as the most appropriate measure suited for discrimination of positive and negative cases. Assuming 5% precision, the applied classification criterion led to inclusion of 70% positive cases in the final prediction. Since lipoaffinity index is a molecular descriptor computed using only 2D information about a chemical structure, its estimation is straightforward and computationally inexpensive. The inclusion of an additional criterion quantifying the cocrystallization probability leads to the following conjunction criterions H mix <-0.18 and ΔLA>3.61, allowing for identification of dissolution rate enhancers. The screening procedure was applied for finding the most promising coformers of such drugs as Iloperidone, Ritonavir, Carbamazepine and Enthenzamide. Copyright © 2017 Elsevier B.V. All rights reserved.

Molecular descriptor subset selection in theoretical peptide quantitative structure-retention relationship model development using nature-inspired optimization algorithms.

PubMed

Žuvela, Petar; Liu, J Jay; Macur, Katarzyna; Bączek, Tomasz

2015-10-06

In this work, performance of five nature-inspired optimization algorithms, genetic algorithm (GA), particle swarm optimization (PSO), artificial bee colony (ABC), firefly algorithm (FA), and flower pollination algorithm (FPA), was compared in molecular descriptor selection for development of quantitative structure-retention relationship (QSRR) models for 83 peptides that originate from eight model proteins. The matrix with 423 descriptors was used as input, and QSRR models based on selected descriptors were built using partial least squares (PLS), whereas root mean square error of prediction (RMSEP) was used as a fitness function for their selection. Three performance criteria, prediction accuracy, computational cost, and the number of selected descriptors, were used to evaluate the developed QSRR models. The results show that all five variable selection methods outperform interval PLS (iPLS), sparse PLS (sPLS), and the full PLS model, whereas GA is superior because of its lowest computational cost and higher accuracy (RMSEP of 5.534%) with a smaller number of variables (nine descriptors). The GA-QSRR model was validated initially through Y-randomization. In addition, it was successfully validated with an external testing set out of 102 peptides originating from Bacillus subtilis proteomes (RMSEP of 22.030%). Its applicability domain was defined, from which it was evident that the developed GA-QSRR exhibited strong robustness. All the sources of the model's error were identified, thus allowing for further application of the developed methodology in proteomics.

Stocking, Forest Type, and Stand Size Class - The Southern Forest Inventory and Analysis Unit's Calculation of Three Important Stand Descriptors

Treesearch

Dennis M. May

1990-01-01

The procedures by which the Southern Forest Inventory and Analysis unit calculates stocking from tree data collected on inventory sample plots are described in this report. Stocking is then used to ascertain two other important stand descriptors: forest type and stand size class. Inventory data for three plots from the recently completed 1989 Tennessee survey are used...

Calculation of the octanol-water partition coefficient of armchair polyhex BN nanotubes

NASA Astrophysics Data System (ADS)

Mohammadinasab, E.; Pérez-Sánchez, H.; Goodarzi, M.

2017-12-01

A predictive model for determination partition coefficient (log P) of armchair polyhex BN nanotubes by using simple descriptors was built. The relationship between the octanol-water log P and quantum chemical descriptors, electric moments, and topological indices of some armchair polyhex BN nanotubes with various lengths and fixed circumference are represented. Based on density functional theory electric moments and physico-chemical properties of those nanotubes are calculated.

Mechanistic Details and Reactivity Descriptors in Oxidation and Acid Catalysis of Methanol

DOE Office of Scientific and Technical Information (OSTI.GOV)

Deshlahra, Prashant; Carr, Robert T.; Chai, Song-Hai

2015-02-06

Acid and redox reaction rates of CH₃OH-O₂ mixtures on polyoxometalate (POM) clusters, together with isotopic, spectroscopic, and theoretical assessments of catalyst properties and reaction pathways, were used to define rigorous descriptors of reactivity and to probe the compositional effects for oxidative dehydrogenation (ODH) and dehydration reactions. ³¹P-MAS NMR, transmission electron microscopy and titrations of protons with di-tert-butylpyridine during catalysis showed that POM clusters retained their Keggin structure upon dispersion on SiO₂ and after use in CH₃OH reactions. The effects of CH₃OH and O₂ pressures and of D-substitution on ODH rates show that C-H activation in molecularly adsorbed CH₃OH is themore » sole kinetically relevant step and leads to reduced centers as intermediates present at low coverages; their concentrations, measured from UV-vis spectra obtained during catalysis, are consistent with the effects of CH₃OH/O₂ ratios predicted from the elementary steps proposed. First-order ODH rate constants depend strongly on the addenda atoms (Mo vs W) but weakly on the central atom (P vs Si) in POM clusters, because C-H activation steps inject electrons into the lowest unoccupied molecular orbitals (LUMO) of the clusters, which are the d-orbitals at Mo⁶⁺ and W⁶⁺ centers. H-atom addition energies (HAE) at O-atoms in POM clusters represent the relevant theoretical probe of the LUMO energies and of ODH reactivity. The calculated energies of ODH transition states at each O-atom depend linearly on their HAE values with slopes near unity, as predicted for late transition states in which electron transfer and C-H cleavage are essentially complete. HAE values averaged over all accessible O-atoms in POM clusters provide the appropriate reactivity descriptor for oxides whose known structures allow accurate HAE calculations. CH₃OH dehydration proceeds via parallel pathways mediated by late carbenium-ion transition states; effects of composition on dehydration reactivity reflect changes in charge reorganizations and electrostatic forces that stabilize protons at Brønsted acid sites.« less

QSAR and docking studies on xanthone derivatives for anticancer activity targeting DNA topoisomerase IIα

PubMed Central

Alam, Sarfaraz; Khan, Feroz

2014-01-01

Due to the high mortality rate in India, the identification of novel molecules is important in the development of novel and potent anticancer drugs. Xanthones are natural constituents of plants in the families Bonnetiaceae and Clusiaceae, and comprise oxygenated heterocycles with a variety of biological activities along with an anticancer effect. To explore the anticancer compounds from xanthone derivatives, a quantitative structure activity relationship (QSAR) model was developed by the multiple linear regression method. The structure–activity relationship represented by the QSAR model yielded a high activity–descriptors relationship accuracy (84%) referred by regression coefficient (r2=0.84) and a high activity prediction accuracy (82%). Five molecular descriptors – dielectric energy, group count (hydroxyl), LogP (the logarithm of the partition coefficient between n-octanol and water), shape index basic (order 3), and the solvent-accessible surface area – were significantly correlated with anticancer activity. Using this QSAR model, a set of virtually designed xanthone derivatives was screened out. A molecular docking study was also carried out to predict the molecular interaction between proposed compounds and deoxyribonucleic acid (DNA) topoisomerase IIα. The pharmacokinetics parameters, such as absorption, distribution, metabolism, excretion, and toxicity, were also calculated, and later an appraisal of synthetic accessibility of organic compounds was carried out. The strategy used in this study may provide understanding in designing novel DNA topoisomerase IIα inhibitors, as well as for other cancer targets. PMID:24516330

Modeling Biophysical and Biological Properties From the Characteristics of the Molecular Electron Density, Electron Localization and Delocalization Matrices, and the Electrostatic Potential

PubMed Central

Matta*, Chérif F

2014-01-01

The electron density and the electrostatic potential are fundamentally related to the molecular hamiltonian, and hence are the ultimate source of all properties in the ground- and excited-states. The advantages of using molecular descriptors derived from these fundamental scalar fields, both accessible from theory and from experiment, in the formulation of quantitative structure-to-activity and structure-to-property relationships, collectively abbreviated as QSAR, are discussed. A few such descriptors encode for a wide variety of properties including, for example, electronic transition energies, pKa's, rates of ester hydrolysis, NMR chemical shifts, DNA dimers binding energies, π-stacking energies, toxicological indices, cytotoxicities, hepatotoxicities, carcinogenicities, partial molar volumes, partition coefficients (log P), hydrogen bond donor capacities, enzyme–substrate complementarities, bioisosterism, and regularities in the genetic code. Electronic fingerprinting from the topological analysis of the electron density is shown to be comparable and possibly superior to Hammett constants and can be used in conjunction with traditional bulk and liposolubility descriptors to accurately predict biological activities. A new class of descriptors obtained from the quantum theory of atoms in molecules' (QTAIM) localization and delocalization indices and bond properties, cast in matrix format, is shown to quantify transferability and molecular similarity meaningfully. Properties such as “interacting quantum atoms (IQA)” energies which are expressible into an interaction matrix of two body terms (and diagonal one body “self” terms, as IQA energies) can be used in the same manner. The proposed QSAR-type studies based on similarity distances derived from such matrix representatives of molecular structure necessitate extensive investigation before their utility is unequivocally established. © 2014 The Author and the Journal of Computational Chemistry Published by Wiley Periodicals, Inc. PMID:24777743

Role of physicochemical properties in the activation of peroxisome proliferator-activated receptor δ.

PubMed

Maltarollo, Vinícius G; Homem-de-Mello, Paula; Honorio, Káthia M

2011-10-01

Current researches on treatments for metabolic diseases involve a class of biological receptors called peroxisome proliferator-activated receptors (PPARs), which control the metabolism of carbohydrates and lipids. A subclass of these receptors, PPARδ, regulates several metabolic processes, and the substances that activate them are being studied as new drug candidates for the treatment of diabetes mellitus and metabolic syndrome. In this study, several PPARδ agonists with experimental biological activity were selected for a structural and chemical study. Electronic, stereochemical, lipophilic and topological descriptors were calculated for the selected compounds using various theoretical methods, such as density functional theory (DFT). Fisher's weight and principal components analysis (PCA) methods were employed to select the most relevant variables for this study. The partial least squares (PLS) method was used to construct the multivariate statistical model, and the best model obtained had 4 PCs, q ( 2 ) = 0.80 and r ( 2 ) = 0.90, indicating a good internal consistency. The prediction residues calculated for the compounds in the test set had low values, indicating the good predictive capability of our PLS model. The model obtained in this study is reliable and can be used to predict the biological activity of new untested compounds. Docking studies have also confirmed the importance of the molecular descriptors selected for this system.

New formulae for Zagreb indices

NASA Astrophysics Data System (ADS)

Cangul, Ismail Naci; Yurttas, Aysun; Togan, Muge; Cevik, Ahmet Sinan

2017-07-01

In this paper, we study with some graph descriptors also called topological indices. These descriptors are useful in determination of some properties of chemical structures and preferred to some earlier descriptors as they are more practical. Especially the first and second Zagreb indices together with the first and second multiplicative Zagreb indices are considered and they are calculated in terms of the smallest and largest vertex degrees and vertex number for some well-known classes of graphs.

Synthesis of a novel methyl(2E)-2-{[N-(2-formylphenyl)(4-methylbenzene) sulfonamido]methyl}-3-(2-methoxyphenyl)prop-2-enoate: Molecular structure, spectral, antimicrobial, molecular docking and DFT computational approaches

NASA Astrophysics Data System (ADS)

Murugavel, S.; Vetri velan, V.; Kannan, Damodharan; Bakthadoss, Manickam

2017-01-01

The title compound methyl(2E)-2-{[N-(2-formylphenyl)(4-methylbenzene)sulfonamido] methyl}-3-(2-methoxyphenyl)prop-2-enoate (MFMSM) has been synthesized and single crystals were grown by slow evaporation solution growth technique at room temperature. XRD, FT-IR and NMR spectra of MFMSM in the solid phase were recorded and analyzed. The optimized geometry and vibrational wave numbers were computed using DFT method. The NLO, Mulliken, MEP, HOMO-LUMO energy gap and thermodynamic properties were theoretically predicted. The NBO analysis explained the intramolecular hydrogen bonding. The global chemical reactivity descriptors are calculated for MFMSM and used to predict their relative stability and reactivity. All the calculations were carried out by B3LYP/6-311G (d,p) method. MFMSM has been screened for its antimicrobial activity and found to exhibit antifungal and antibacterial effects. Docking simulation has been performed.

Synthesis, crystal structure, Hirshfeld surface analysis, spectroscopic characterization, reactivity study by DFT and MD approaches and molecular docking study of a novel chalcone derivative

NASA Astrophysics Data System (ADS)

Arshad, Suhana; Pillai, Renjith Raveendran; Zainuri, Dian Alwani; Khalib, Nuridayanti Che; Razak, Ibrahim Abdul; Armaković, Stevan; Armaković, Sanja J.; Panicker, C. Yohannan; Van Alsenoy, C.

2017-05-01

In the present study, the title compound named as (E)-1-(4-bromophenyl)-3-(4-(trifluoromethyl)phenyl)prop-2-en-1-one was synthesized and structurally characterized by single-crystal X-ray diffraction. The compound crystallizes in monoclinic crystal system in P21/c space group, unit cell parameters a = 16.7629 (12) Å, b = 13.9681 (10) Å, c = 5.8740 (4) Å, β = 96.3860 (12)° and Z = 4. Hirshfeld surface analysis revealed that the molecular structure is dominated by H⋯H, C⋯H/H⋯C, Br⋯F/F⋯Br and F⋯F contacts. The FT-IR spectrum was recorded and interpreted in details with the aid of Density Functional Theory (DFT) calculations and Potential Energy Distribution (PED) analysis. Average local ionization energies (ALIE) and Fukui functions have been used as quantum-molecular descriptors to locate the molecule sites that could be of importance from the aspect of reactivity. Degradation properties have been assessed by calculations of bond dissociation energies (BDE) for hydrogen abstraction and the rest of the single acyclic bonds, while molecular dynamics (MD) simulations were used in order to calculate radial distribution functions and determine the atoms with significant interactions with water. In order to understand how the title molecule inhibits and hence increases the catalytic efficiency of MOA-B enzyme, molecular docking study was performed.

Molecular structure, vibrational, electronic and thermal properties of 4-vinylcyclohexene by quantum chemical calculations

NASA Astrophysics Data System (ADS)

Nagabalasubramanian, P. B.; Periandy, S.; Karabacak, Mehmet; Govindarajan, M.

2015-06-01

The solid phase FT-IR and FT-Raman spectra of 4-vinylcyclohexene (abbreviated as 4-VCH) have been recorded in the region 4000-100 cm-1. The optimized molecular geometry and vibrational frequencies of the fundamental modes of 4-VCH have been precisely assigned and analyzed with the aid of structure optimizations and normal coordinate force field calculations based on density functional theory (DFT) method at 6-311++G(d,p) level basis set. The theoretical frequencies were properly scaled and compared with experimentally obtained FT-IR and FT-Raman spectra. Also, the effect due the substitution of vinyl group on the ring vibrational frequencies was analyzed and a detailed interpretation of the vibrational spectra of this compound has been made on the basis of the calculated total energy distribution (TED). The time dependent DFT (TD-DFT) method was employed to predict its electronic properties, such as electronic transitions by UV-Visible analysis, HOMO and LUMO energies, molecular electrostatic potential (MEP) and various global reactivity and selectivity descriptors (chemical hardness, chemical potential, softness, electrophilicity index). Stability of the molecule arising from hyper conjugative interaction, charge delocalization has been analyzed using natural bond orbital (NBO) analysis. Atomic charges obtained by Mulliken population analysis and NBO analysis are compared. Thermodynamic properties (heat capacity, entropy and enthalpy) of the title compound at different temperatures are also calculated.

Quantitative structure-property relationships for predicting sorption of pharmaceuticals to sewage sludge during waste water treatment processes.

PubMed

Berthod, L; Whitley, D C; Roberts, G; Sharpe, A; Greenwood, R; Mills, G A

2017-02-01

Understanding the sorption of pharmaceuticals to sewage sludge during waste water treatment processes is important for understanding their environmental fate and in risk assessments. The degree of sorption is defined by the sludge/water partition coefficient (K d ). Experimental K d values (n=297) for active pharmaceutical ingredients (n=148) in primary and activated sludge were collected from literature. The compounds were classified by their charge at pH7.4 (44 uncharged, 60 positively and 28 negatively charged, and 16 zwitterions). Univariate models relating log K d to log K ow for each charge class showed weak correlations (maximum R 2 =0.51 for positively charged) with no overall correlation for the combined dataset (R 2 =0.04). Weaker correlations were found when relating log K d to log D ow . Three sets of molecular descriptors (Molecular Operating Environment, VolSurf and ParaSurf) encoding a range of physico-chemical properties were used to derive multivariate models using stepwise regression, partial least squares and Bayesian artificial neural networks (ANN). The best predictive performance was obtained with ANN, with R 2 =0.62-0.69 for these descriptors using the complete dataset. Use of more complex Vsurf and ParaSurf descriptors showed little improvement over Molecular Operating Environment descriptors. The most influential descriptors in the ANN models, identified by automatic relevance determination, highlighted the importance of hydrophobicity, charge and molecular shape effects in these sorbate-sorbent interactions. The heterogeneous nature of the different sewage sludges used to measure K d limited the predictability of sorption from physico-chemical properties of the pharmaceuticals alone. Standardization of test materials for the measurement of K d would improve comparability of data from different studies, in the long-term leading to better quality environmental risk assessments. Copyright © 2016 British Geological Survey, NERC. Published by Elsevier B.V. All rights reserved.

Predicting volume of distribution with decision tree-based regression methods using predicted tissue:plasma partition coefficients.

PubMed

Freitas, Alex A; Limbu, Kriti; Ghafourian, Taravat

2015-01-01

Volume of distribution is an important pharmacokinetic property that indicates the extent of a drug's distribution in the body tissues. This paper addresses the problem of how to estimate the apparent volume of distribution at steady state (Vss) of chemical compounds in the human body using decision tree-based regression methods from the area of data mining (or machine learning). Hence, the pros and cons of several different types of decision tree-based regression methods have been discussed. The regression methods predict Vss using, as predictive features, both the compounds' molecular descriptors and the compounds' tissue:plasma partition coefficients (Kt:p) - often used in physiologically-based pharmacokinetics. Therefore, this work has assessed whether the data mining-based prediction of Vss can be made more accurate by using as input not only the compounds' molecular descriptors but also (a subset of) their predicted Kt:p values. Comparison of the models that used only molecular descriptors, in particular, the Bagging decision tree (mean fold error of 2.33), with those employing predicted Kt:p values in addition to the molecular descriptors, such as the Bagging decision tree using adipose Kt:p (mean fold error of 2.29), indicated that the use of predicted Kt:p values as descriptors may be beneficial for accurate prediction of Vss using decision trees if prior feature selection is applied. Decision tree based models presented in this work have an accuracy that is reasonable and similar to the accuracy of reported Vss inter-species extrapolations in the literature. The estimation of Vss for new compounds in drug discovery will benefit from methods that are able to integrate large and varied sources of data and flexible non-linear data mining methods such as decision trees, which can produce interpretable models. Graphical AbstractDecision trees for the prediction of tissue partition coefficient and volume of distribution of drugs.

Lattice enumeration for inverse molecular design using the signature descriptor.

PubMed

Martin, Shawn

2012-07-23

We describe an inverse quantitative structure-activity relationship (QSAR) framework developed for the design of molecular structures with desired properties. This framework uses chemical fragments encoded with a molecular descriptor known as a signature. It solves a system of linear constrained Diophantine equations to reorganize the fragments into novel molecular structures. The method has been previously applied to problems in drug and materials design but has inherent computational limitations due to the necessity of solving the Diophantine constraints. We propose a new approach to overcome these limitations using the Fincke-Pohst algorithm for lattice enumeration. We benchmark the new approach against previous results on LFA-1/ICAM-1 inhibitory peptides, linear homopolymers, and hydrofluoroether foam blowing agents. Software implementing the new approach is available at www.cs.otago.ac.nz/homepages/smartin.

Chemodiversity and molecular plasticity: recognition processes as explored by property spaces.

PubMed

Vistoli, Giulio; Pedretti, Alessandro; Testa, Bernard

2011-06-01

In the last few years, a need to account for molecular flexibility in drug-design methodologies has emerged, even if the dynamic behavior of molecular properties is seldom made explicit. For a flexible molecule, it is indeed possible to compute different values for a given conformation-dependent property and the ensemble of such values defines a property space that can be used to describe its molecular variability; a most representative case is the lipophilicity space. In this review, a number of applications of lipophilicity space and other property spaces are presented, showing that this concept can be fruitfully exploited: to investigate the constraints exerted by media of different levels of structural organization, to examine processes of molecular recognition and binding at an atomic level, to derive informative descriptors to be included in quantitative structure--activity relationships and to analyze protein simulations extracting the relevant information. Much molecular information is neglected in the descriptors used by medicinal chemists, while the concept of property space can fill this gap by accounting for the often-disregarded dynamic behavior of both small ligands and biomacromolecules. Property space also introduces some innovative concepts such as molecular sensitivity and plasticity, which appear best suited to explore the ability of a molecule to adapt itself to the environment variously modulating its property and conformational profiles. Globally, such concepts can enhance our understanding of biological phenomena providing fruitful descriptors in drug-design and pharmaceutical sciences.

Can the electronegativity equalization method predict spectroscopic properties?

PubMed

Verstraelen, T; Bultinck, P

2015-02-05

The electronegativity equalization method is classically used as a method allowing the fast generation of atomic charges using a set of calibrated parameters and provided knowledge of the molecular structure. Recently, it has started being used for the calculation of other reactivity descriptors and for the development of polarizable and reactive force fields. For such applications, it is of interest to know whether the method, through the inclusion of the molecular geometry in the Taylor expansion of the energy, would also allow sufficiently accurate predictions of spectroscopic data. In this work, relevant quantities for IR spectroscopy are considered, namely the dipole derivatives and the Cartesian Hessian. Despite careful calibration of parameters for this specific task, it is shown that the current models yield insufficiently accurate results. Copyright © 2013 Elsevier B.V. All rights reserved.

Prediction of chromatographic relative retention time of polychlorinated biphenyls from the molecular electronegativity distance vector.

PubMed

Liu, Shu-Shen; Liu, Yan; Yin, Da-Qian; Wang, Xiao-Dong; Wang, Lian-Sheng

2006-02-01

Using the molecular electronegativity distance vector (MEDV) descriptors derived directly from the molecular topological structures, the gas chromatographic relative retention times (RRTs) of 209 polychlorinated biphenyls (PCBs) on the SE-54 stationary phase were predicted. A five-variable regression equation with the correlation coefficient of 0.9964 and the root mean square errors of 0.0152 was developed. The descriptors included in the equation represent degree of chlorination (nCl), nonortho index (Ino), and interactions between three pairs of atom types, i.e., atom groups -C= and -C=, -C= and >C=, -C= and -Cl. It has been proved that the retention times of all 209 PCB congeners can be accurately predicted as long as there are more than 50 calibration compounds. In the same way, the MEDV descriptors are also used to develop the five- or six-variable models of RRTs of PCBs on other 18 stationary phases and the correlation coefficients in both modeling stage and LOO cross-validation step are not lower than 0.99 except two models.

Prediction and Dissection of Protein-RNA Interactions by Molecular Descriptors.

PubMed

Liu, Zhi-Ping; Chen, Luonan

2016-01-01

Protein-RNA interactions play crucial roles in numerous biological processes. However, detecting the interactions and binding sites between protein and RNA by traditional experiments is still time consuming and labor costing. Thus, it is of importance to develop bioinformatics methods for predicting protein-RNA interactions and binding sites. Accurate prediction of protein-RNA interactions and recognitions will highly benefit to decipher the interaction mechanisms between protein and RNA, as well as to improve the RNA-related protein engineering and drug design. In this work, we summarize the current bioinformatics strategies of predicting protein-RNA interactions and dissecting protein-RNA interaction mechanisms from local structure binding motifs. In particular, we focus on the feature-based machine learning methods, in which the molecular descriptors of protein and RNA are extracted and integrated as feature vectors of representing the interaction events and recognition residues. In addition, the available methods are classified and compared comprehensively. The molecular descriptors are expected to elucidate the binding mechanisms of protein-RNA interaction and reveal the functional implications from structural complementary perspective.

Sensory shelf life of dulce de leche.

PubMed

Garitta, L; Hough, G; Sánchez, R

2004-06-01

The objectives of this research were to determine the sensory cutoff points for dulce de leche (DL) critical descriptors, both for defective off-flavors and for storage changes in desirable attributes, and to estimate the shelf life of DL as a function of storage temperature. The critical descriptors used to determine the cutoff points were plastic flavor, burnt flavor, dark color, and spreadability. Linear correlations between sensory acceptability and trained panel scores were used to determine the sensory failure cutoff point for each descriptor. To estimate shelf life, DL samples were stored at 25, 37, and 45 degrees C. Plastic flavor was the first descriptor to reach its cutoff point at 25 degrees C and was used for shelf-life calculations. Plastic flavor vs. storage time followed zero-order reaction rate. Shelf-life estimations at different temperatures were 109 d at 25 degrees C, 53 d at 37 degrees C, and 9 d at 45 degrees C. The activation energy, necessary to calculate shelf lives at different temperatures, was 14,370 +/- 2080 cal/mol.

Prediction of drug transport processes using simple parameters and PLS statistics. The use of ACD/logP and ACD/ChemSketch descriptors.

PubMed

Osterberg, T; Norinder, U

2001-01-01

A method of modelling and predicting biopharmaceutical properties using simple theoretically computed molecular descriptors and multivariate statistics has been investigated for several data sets related to solubility, IAM chromatography, permeability across Caco-2 cell monolayers, human intestinal perfusion, brain-blood partitioning, and P-glycoprotein ATPase activity. The molecular descriptors (e.g. molar refractivity, molar volume, index of refraction, surface tension and density) and logP were computed with ACD/ChemSketch and ACD/logP, respectively. Good statistical models were derived that permit simple computational prediction of biopharmaceutical properties. All final models derived had R(2) values ranging from 0.73 to 0.95 and Q(2) values ranging from 0.69 to 0.86. The RMSEP values for the external test sets ranged from 0.24 to 0.85 (log scale).

SVM Based Descriptor Selection and Classification of Neurodegenerative Disease Drugs for Pharmacological Modeling.

PubMed

Shahid, Mohammad; Shahzad Cheema, Muhammad; Klenner, Alexander; Younesi, Erfan; Hofmann-Apitius, Martin

2013-03-01

Systems pharmacological modeling of drug mode of action for the next generation of multitarget drugs may open new routes for drug design and discovery. Computational methods are widely used in this context amongst which support vector machines (SVM) have proven successful in addressing the challenge of classifying drugs with similar features. We have applied a variety of such SVM-based approaches, namely SVM-based recursive feature elimination (SVM-RFE). We use the approach to predict the pharmacological properties of drugs widely used against complex neurodegenerative disorders (NDD) and to build an in-silico computational model for the binary classification of NDD drugs from other drugs. Application of an SVM-RFE model to a set of drugs successfully classified NDD drugs from non-NDD drugs and resulted in overall accuracy of ∼80 % with 10 fold cross validation using 40 top ranked molecular descriptors selected out of total 314 descriptors. Moreover, SVM-RFE method outperformed linear discriminant analysis (LDA) based feature selection and classification. The model reduced the multidimensional descriptors space of drugs dramatically and predicted NDD drugs with high accuracy, while avoiding over fitting. Based on these results, NDD-specific focused libraries of drug-like compounds can be designed and existing NDD-specific drugs can be characterized by a well-characterized set of molecular descriptors. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Quantitative structure-retention relationship studies with immobilized artificial membrane chromatography II: partial least squares regression.

PubMed

Li, Jie; Sun, Jin; He, Zhonggui

2007-01-26

We aimed to establish quantitative structure-retention relationship (QSRR) with immobilized artificial membrane (IAM) chromatography using easily understood and obtained physicochemical molecular descriptors and to elucidate which descriptors are critical to affect the interaction process between solutes and immobilized phospholipid membranes. The retention indices (logk(IAM)) of 55 structurally diverse drugs were determined on an immobilized artificial membrane column (IAM.PC.DD2) directly or obtained by extrapolation method for highly hydrophobic compounds. Ten simple physicochemical property descriptors (clogP, rings, rotatory bond, hydro-bond counting, etc.) of these drugs were collected and used to establish QSRR and predict the retention data by partial least squares regression (PLSR). Five descriptors, clogP, rotatory bond (RotB), rings, molecular weight (MW) and total surface area (TSA), were reserved by using the Variable Importance for Projection (VIP) values as criterion to build the final PLSR model. An external test set was employed to verify the QSRR based on the training set with the five variables, and QSRR by PLSR exhibited a satisfying predictive ability with R(p)=0.902 and RMSE(p)=0.400. Comparison of coefficients of centered and scaled variables by PLSR demonstrated that, for the descriptors studied, clogP and TSA have the most significant positive effect but the rotatable bond has significant negative effect on drug IAM chromatographic retention.

Adversarial Threshold Neural Computer for Molecular de Novo Design.

PubMed

Putin, Evgeny; Asadulaev, Arip; Vanhaelen, Quentin; Ivanenkov, Yan; Aladinskaya, Anastasia V; Aliper, Alex; Zhavoronkov, Alex

2018-03-30

In this article, we propose the deep neural network Adversarial Threshold Neural Computer (ATNC). The ATNC model is intended for the de novo design of novel small-molecule organic structures. The model is based on generative adversarial network architecture and reinforcement learning. ATNC uses a Differentiable Neural Computer as a generator and has a new specific block, called adversarial threshold (AT). AT acts as a filter between the agent (generator) and the environment (discriminator + objective reward functions). Furthermore, to generate more diverse molecules we introduce a new objective reward function named Internal Diversity Clustering (IDC). In this work, ATNC is tested and compared with the ORGANIC model. Both models were trained on the SMILES string representation of the molecules, using four objective functions (internal similarity, Muegge druglikeness filter, presence or absence of sp 3 -rich fragments, and IDC). The SMILES representations of 15K druglike molecules from the ChemDiv collection were used as a training data set. For the different functions, ATNC outperforms ORGANIC. Combined with the IDC, ATNC generates 72% of valid and 77% of unique SMILES strings, while ORGANIC generates only 7% of valid and 86% of unique SMILES strings. For each set of molecules generated by ATNC and ORGANIC, we analyzed distributions of four molecular descriptors (number of atoms, molecular weight, logP, and tpsa) and calculated five chemical statistical features (internal diversity, number of unique heterocycles, number of clusters, number of singletons, and number of compounds that have not been passed through medicinal chemistry filters). Analysis of key molecular descriptors and chemical statistical features demonstrated that the molecules generated by ATNC elicited better druglikeness properties. We also performed in vitro validation of the molecules generated by ATNC; results indicated that ATNC is an effective method for producing hit compounds.

TyPol - a new methodology for organic compounds clustering based on their molecular characteristics and environmental behavior.

PubMed

Servien, Rémi; Mamy, Laure; Li, Ziang; Rossard, Virginie; Latrille, Eric; Bessac, Fabienne; Patureau, Dominique; Benoit, Pierre

2014-09-01

Following legislation, the assessment of the environmental risks of 30000-100000 chemical substances is required for their registration dossiers. However, their behavior in the environment and their transfer to environmental components such as water or atmosphere are studied for only a very small proportion of the chemical in laboratory tests or monitoring studies because it is time-consuming and/or cost prohibitive. Therefore, the objective of this work was to develop a new methodology, TyPol, to classify organic compounds, and their degradation products, according to both their behavior in the environment and their molecular properties. The strategy relies on partial least squares analysis and hierarchical clustering. The calculation of molecular descriptors is based on an in silico approach, and the environmental endpoints (i.e. environmental parameters) are extracted from several available databases and literature. The classification of 215 organic compounds inputted in TyPol for this proof-of-concept study showed that the combination of some specific molecular descriptors could be related to a particular behavior in the environment. TyPol also provided an analysis of similarities (or dissimilarities) between organic compounds and their degradation products. Among the 24 degradation products that were inputted, 58% were found in the same cluster as their parents. The robustness of the method was tested and shown to be good. TyPol could help to predict the environmental behavior of a "new" compound (parent compound or degradation product) from its affiliation to one cluster, but also to select representative substances from a large data set in order to answer some specific questions regarding their behavior in the environment. Copyright © 2014 Elsevier Ltd. All rights reserved.

OPERA models for predicting physicochemical properties and environmental fate endpoints.

PubMed

Mansouri, Kamel; Grulke, Chris M; Judson, Richard S; Williams, Antony J

2018-03-08

The collection of chemical structure information and associated experimental data for quantitative structure-activity/property relationship (QSAR/QSPR) modeling is facilitated by an increasing number of public databases containing large amounts of useful data. However, the performance of QSAR models highly depends on the quality of the data and modeling methodology used. This study aims to develop robust QSAR/QSPR models for chemical properties of environmental interest that can be used for regulatory purposes. This study primarily uses data from the publicly available PHYSPROP database consisting of a set of 13 common physicochemical and environmental fate properties. These datasets have undergone extensive curation using an automated workflow to select only high-quality data, and the chemical structures were standardized prior to calculation of the molecular descriptors. The modeling procedure was developed based on the five Organization for Economic Cooperation and Development (OECD) principles for QSAR models. A weighted k-nearest neighbor approach was adopted using a minimum number of required descriptors calculated using PaDEL, an open-source software. The genetic algorithms selected only the most pertinent and mechanistically interpretable descriptors (2-15, with an average of 11 descriptors). The sizes of the modeled datasets varied from 150 chemicals for biodegradability half-life to 14,050 chemicals for logP, with an average of 3222 chemicals across all endpoints. The optimal models were built on randomly selected training sets (75%) and validated using fivefold cross-validation (CV) and test sets (25%). The CV Q 2 of the models varied from 0.72 to 0.95, with an average of 0.86 and an R 2 test value from 0.71 to 0.96, with an average of 0.82. Modeling and performance details are described in QSAR model reporting format and were validated by the European Commission's Joint Research Center to be OECD compliant. All models are freely available as an open-source, command-line application called OPEn structure-activity/property Relationship App (OPERA). OPERA models were applied to more than 750,000 chemicals to produce freely available predicted data on the U.S. Environmental Protection Agency's CompTox Chemistry Dashboard.

Application of the QSPR approach to the boiling points of azeotropes.

PubMed

Katritzky, Alan R; Stoyanova-Slavova, Iva B; Tämm, Kaido; Tamm, Tarmo; Karelson, Mati

2011-04-21

CODESSA Pro derivative descriptors were calculated for a data set of 426 azeotropic mixtures by the centroid approximation and the weighted-contribution-factor approximation. The two approximations produced almost identical four-descriptor QSPR models relating the structural characteristic of the individual components of azeotropes to the azeotropic boiling points. These models were supported by internal and external validations. The descriptors contributing to the QSPR models are directly related to the three components of the enthalpy (heat) of vaporization.

Synthesis, spectroscopic characterization, DFT studies and antifungal activity of (E)-4-amino-5-[N'-(2-nitro-benzylidene)-hydrazino]-2,4-dihydro-[1,2,4]triazole-3-thione

NASA Astrophysics Data System (ADS)

Joshi, Rachana; Pandey, Nidhi; Yadav, Swatantra Kumar; Tilak, Ragini; Mishra, Hirdyesh; Pokharia, Sandeep

2018-07-01

The hydrazino Schiff base (E)-4-amino-5-[N'-(2-nitro-benzylidene)-hydrazino]-2,4-dihydro-[1,2,4]triazole-3-thione was synthesized and structurally characterized by elemental analysis, FT-IR, Raman, 1H and 13C-NMR and UV-Vis studies. A density functional theory (DFT) based electronic structure calculations were accomplished at B3LYP/6-311++G(d,p) level of theory. A comparative analysis of calculated vibrational frequencies with experimental vibrational frequencies was carried out and significant bands were assigned. The results indicate a good correlation (R2 = 0.9974) between experimental and theoretical IR frequencies. The experimental 1H and 13C-NMR resonance signals were also compared to the calculated values. The theoretical UV-Vis spectral studies were carried out using time dependent-DFT method in gas phase and IEFPCM model in solvent field calculation. The geometrical parameters were calculated in the gas phase. Atomic charges at selected atoms were calculated by Mulliken population analysis (MPA), Hirshfeld population analysis (HPA) and Natural population analysis (NPA) schemes. The molecular electrostatic potential (MEP) map was calculated to assign reactive site on the surface of the molecule. The conceptual-DFT based global and local reactivity descriptors were calculated to obtain an insight into the reactivity behaviour. The frontier molecular orbital analysis was carried out to study the charge transfer within the molecule. The detailed natural bond orbital (NBO) analysis was performed to obtain an insight into the intramolecular conjugative electronic interactions. The titled compound was screened for in vitro antifungal activity against four fungal strains and the results obtained are explained through in silico molecular docking studies.

Determination of descriptors for polycyclic aromatic hydrocarbons and related compounds by chromatographic methods and liquid-liquid partition in totally organic biphasic systems.

PubMed

Ariyasena, Thiloka C; Poole, Colin F

2014-09-26

Retention factors on several columns and at various temperatures using gas chromatography and from reversed-phase liquid chromatography on a SunFire C18 column with various mobile phase compositions containing acetonitrile, methanol and tetrahydrofuran as strength adjusting solvents are combined with liquid-liquid partition coefficients in totally organic biphasic systems to calculate descriptors for 23 polycyclic aromatic hydrocarbons and eighteen related compounds of environmental interest. The use of a consistent protocol for the above measurements provides descriptors that are more self consistent for the estimation of physicochemical properties (octanol-water, air-octanol, air-water, aqueous solubility, and subcooled liquid vapor pressure). The descriptor in this report tend to have smaller values for the L and E descriptors and random differences in the B and S descriptors compared with literature sources. A simple atom fragment constant model is proposed for the estimation of descriptors from structure for polycyclic aromatic hydrocarbons. The new descriptors show no bias in the prediction of the air-water partition coefficient for polycyclic aromatic hydrocarbons unlike the literature values. Copyright © 2014 Elsevier B.V. All rights reserved.

Hybrid Histogram Descriptor: A Fusion Feature Representation for Image Retrieval.

PubMed

Feng, Qinghe; Hao, Qiaohong; Chen, Yuqi; Yi, Yugen; Wei, Ying; Dai, Jiangyan

2018-06-15

Currently, visual sensors are becoming increasingly affordable and fashionable, acceleratingly the increasing number of image data. Image retrieval has attracted increasing interest due to space exploration, industrial, and biomedical applications. Nevertheless, designing effective feature representation is acknowledged as a hard yet fundamental issue. This paper presents a fusion feature representation called a hybrid histogram descriptor (HHD) for image retrieval. The proposed descriptor comprises two histograms jointly: a perceptually uniform histogram which is extracted by exploiting the color and edge orientation information in perceptually uniform regions; and a motif co-occurrence histogram which is acquired by calculating the probability of a pair of motif patterns. To evaluate the performance, we benchmarked the proposed descriptor on RSSCN7, AID, Outex-00013, Outex-00014 and ETHZ-53 datasets. Experimental results suggest that the proposed descriptor is more effective and robust than ten recent fusion-based descriptors under the content-based image retrieval framework. The computational complexity was also analyzed to give an in-depth evaluation. Furthermore, compared with the state-of-the-art convolutional neural network (CNN)-based descriptors, the proposed descriptor also achieves comparable performance, but does not require any training process.

Fourier series of atomic radial distribution functions: A molecular fingerprint for machine learning models of quantum chemical properties

DOE Office of Scientific and Technical Information (OSTI.GOV)

von Lilienfeld, O. Anatole; Ramakrishnan, Raghunathan; Rupp, Matthias

We introduce a fingerprint representation of molecules based on a Fourier series of atomic radial distribution functions. This fingerprint is unique (except for chirality), continuous, and differentiable with respect to atomic coordinates and nuclear charges. It is invariant with respect to translation, rotation, and nuclear permutation, and requires no preconceived knowledge about chemical bonding, topology, or electronic orbitals. As such, it meets many important criteria for a good molecular representation, suggesting its usefulness for machine learning models of molecular properties trained across chemical compound space. To assess the performance of this new descriptor, we have trained machine learning models ofmore » molecular enthalpies of atomization for training sets with up to 10 k organic molecules, drawn at random from a published set of 134 k organic molecules with an average atomization enthalpy of over 1770 kcal/mol. We validate the descriptor on all remaining molecules of the 134 k set. For a training set of 10 k molecules, the fingerprint descriptor achieves a mean absolute error of 8.0 kcal/mol. This is slightly worse than the performance attained using the Coulomb matrix, another popular alternative, reaching 6.2 kcal/mol for the same training and test sets. (c) 2015 Wiley Periodicals, Inc.« less

Descriptors of sensation confirm the multidimensional nature of desire to void.

PubMed

Das, Rebekah; Buckley, Jonathan D; Williams, Marie T

2015-02-01

To collect and categorize descriptors of "desire to void" sensation, determine the reliability of descriptor categories and assess whether descriptor categories discriminate between people with and without symptoms of overactive bladder. This observational, repeated measures study involved 64 Australian volunteers (47 female), aged 50 years or more, with and without symptoms of overactive bladder. Descriptors of desire to void sensation were derived from a structured interview (conducted on two occasions, 1 week apart). Descriptors were recorded verbatim and categorized in a three-stage process. Overactive bladder status was determined by the Overactive Bladder Awareness Tool and the Overactive Bladder Symptom Score. McNemar's test assessed the reliability of descriptors volunteered between two occasions and Partial Least Squares Regression determined whether language categories discriminated according to overactive bladder status. Post hoc Chi squared analysis and relative risk calculation determined the size and direction of overactive bladder prediction. Thirteen language categories (Urgency, Fullness, Pressure, Tickle/tingle, Pain/ache, Heavy, Normal, Intense, Sudden, Annoying, Uncomfortable, Anxiety, and Unique somatic) encapsulated 344 descriptors of sensation. Descriptor categories were stable between two interviews. The categories "Urgency" and "Fullness" predicted overactive bladder status. Participants who volunteered "Urgency" descriptors were twice as likely to have overactive bladder and participants who volunteered "Fullness" descriptors were almost three times as likely not to have overactive bladder. The sensation of desire to void is reliably described over sessions separated by a week, the language used reflects multiple dimensions of sensation, and can predict overactive bladder status. © 2013 Wiley Periodicals, Inc.

Spherical harmonics based descriptor for neural network potentials: Structure and dynamics of Au147 nanocluster.

PubMed

Jindal, Shweta; Chiriki, Siva; Bulusu, Satya S

2017-05-28

We propose a highly efficient method for fitting the potential energy surface of a nanocluster using a spherical harmonics based descriptor integrated with an artificial neural network. Our method achieves the accuracy of quantum mechanics and speed of empirical potentials. For large sized gold clusters (Au 147 ), the computational time for accurate calculation of energy and forces is about 1.7 s, which is faster by several orders of magnitude compared to density functional theory (DFT). This method is used to perform the global minimum optimizations and molecular dynamics simulations for Au 147 , and it is found that its global minimum is not an icosahedron. The isomer that can be regarded as the global minimum is found to be 4 eV lower in energy than the icosahedron and is confirmed from DFT. The geometry of the obtained global minimum contains 105 atoms on the surface and 42 atoms in the core. A brief study on the fluxionality in Au 147 is performed, and it is concluded that Au 147 has a dynamic surface, thus opening a new window for studying its reaction dynamics.

Spherical harmonics based descriptor for neural network potentials: Structure and dynamics of Au147 nanocluster

NASA Astrophysics Data System (ADS)

Jindal, Shweta; Chiriki, Siva; Bulusu, Satya S.

2017-05-01

We propose a highly efficient method for fitting the potential energy surface of a nanocluster using a spherical harmonics based descriptor integrated with an artificial neural network. Our method achieves the accuracy of quantum mechanics and speed of empirical potentials. For large sized gold clusters (Au147), the computational time for accurate calculation of energy and forces is about 1.7 s, which is faster by several orders of magnitude compared to density functional theory (DFT). This method is used to perform the global minimum optimizations and molecular dynamics simulations for Au147, and it is found that its global minimum is not an icosahedron. The isomer that can be regarded as the global minimum is found to be 4 eV lower in energy than the icosahedron and is confirmed from DFT. The geometry of the obtained global minimum contains 105 atoms on the surface and 42 atoms in the core. A brief study on the fluxionality in Au147 is performed, and it is concluded that Au147 has a dynamic surface, thus opening a new window for studying its reaction dynamics.

NSAIDs as potential treatment option for preventing amyloid β toxicity in Alzheimer's disease: an investigation by docking, molecular dynamics, and DFT studies.

PubMed

Azam, Faizul; Alabdullah, Nada Hussin; Ehmedat, Hadeel Mohammed; Abulifa, Abdullah Ramadan; Taban, Ismail; Upadhyayula, Sreedevi

2018-06-01

Aggregation of amyloid beta (Aβ) protein considered as one of contributors in development of Alzheimer's disease (AD). Several investigations have identified the importance of non-steroidal anti-inflammatory drugs (NSAIDs) as Aβ aggregation inhibitors. Here, we have examined the binding interactions of 24 NSAIDs belonging to eight different classes, with Aβ fibrils by exploiting docking and molecular dynamics studies. Minimum energy conformation of the docked NSAIDs were further optimized by density functional theory (DFT) employing Becke's three-parameter hybrid model, Lee-Yang-Parr (B3LYP) correlation functional method. DFT-based global reactivity descriptors, such as electron affinity, hardness, softness, chemical potential, electronegativity, and electrophilicity index were calculated to inspect the expediency of these descriptors for understanding the reactive nature and sites of the molecules. Few selected NSAID-Aβ fibrils complexes were subjected to molecular dynamics simulation to illustrate the stability of these complexes and the most prominent interactions during the simulated trajectory. All of the NSAIDs exhibited potential activity against Aβ fibrils in terms of predicted binding affinity. Sulindac was found to be the most active compound underscoring the contribution of indene methylene substitution, whereas acetaminophen was observed as least active NSAID. General structural requirements for interaction of NSAIDs with Aβ fibril include: aryl/heteroaryl aromatic moiety connected through a linker of 1-2 atoms to a distal aromatic group. Considering these structural requirements and electronic features, new potent agents can be designed and developed as potential Aβ fibril inhibitors for the treatment of AD.

Temperature sensitivity of organic compound destruction in SCWO process.

PubMed

Tan, Yaqin; Shen, Zhemin; Guo, Weimin; Ouyang, Chuang; Jia, Jinping; Jiang, Weili; Zhou, Haiyun

2014-03-01

To study the temperature sensitivity of the destruction of organic compounds in supercritical water oxidation process (SCWO), oxidation effects of twelve chemicals in supercritical water were investigated. The SCWO reaction rates of different compounds improved to varying degrees with the increase of temperature, so the highest slope of the temperature-effect curve (imax) was defined as the maximum ratio of removal ratio to working temperature. It is an important index to stand for the temperature sensitivity effect in SCWO. It was proven that the higher imax is, the more significant the effect of temperature on the SCWO effect is. Since the high-temperature area of SCWO equipment is subject to considerable damage from fatigue, the temperature is of great significance in SCWO equipment operation. Generally, most compounds (imax > 0.25) can be completely oxidized when the reactor temperature reaches 500°C. However, some compounds (imax > 0.25) need a higher temperature for complete oxidation, up to 560°C. To analyze the correlation coefficients between imax and various molecular descriptors, a quantum chemical method was used in this study. The structures of the twelve organic compounds were optimized by the Density Functional Theory B3LYP/6-311G method, as well as their quantum properties. It was shown that six molecular descriptors were negatively correlated to imax while other three descriptors were positively correlated to imax. Among them, dipole moment had the greatest effect on the oxidation thermodynamics of the twelve organic compounds. Once a correlation between molecular descriptors and imax is established, SCWO can be run at an appropriate temperature according to molecular structure. Copyright © 2014 The Research Centre for Eco-Environmental Sciences, Chinese Academy of Sciences. Published by Elsevier B.V. All rights reserved.

Three-Dimensional Biologically Relevant Spectrum (BRS-3D): Shape Similarity Profile Based on PDB Ligands as Molecular Descriptors.

PubMed

Hu, Ben; Kuang, Zheng-Kun; Feng, Shi-Yu; Wang, Dong; He, Song-Bing; Kong, De-Xin

2016-11-17

The crystallized ligands in the Protein Data Bank (PDB) can be treated as the inverse shapes of the active sites of corresponding proteins. Therefore, the shape similarity between a molecule and PDB ligands indicated the possibility of the molecule to bind with the targets. In this paper, we proposed a shape similarity profile that can be used as a molecular descriptor for ligand-based virtual screening. First, through three-dimensional (3D) structural clustering, 300 diverse ligands were extracted from the druggable protein-ligand database, sc-PDB. Then, each of the molecules under scrutiny was flexibly superimposed onto the 300 ligands. Superimpositions were scored by shape overlap and property similarity, producing a 300 dimensional similarity array termed the "Three-Dimensional Biologically Relevant Spectrum (BRS-3D)". Finally, quantitative or discriminant models were developed with the 300 dimensional descriptor using machine learning methods (support vector machine). The effectiveness of this approach was evaluated using 42 benchmark data sets from the G protein-coupled receptor (GPCR) ligand library and the GPCR decoy database (GLL/GDD). We compared the performance of BRS-3D with other 2D and 3D state-of-the-art molecular descriptors. The results showed that models built with BRS-3D performed best for most GLL/GDD data sets. We also applied BRS-3D in histone deacetylase 1 inhibitors screening and GPCR subtype selectivity prediction. The advantages and disadvantages of this approach are discussed.

Exploring the potential energy surface for the interaction of sterically hindered trichloro(diethylenetriamine)gold(III) complexes with water.

PubMed

Dos Santos, Hélio F; Paschoal, Diego; Burda, Jaroslav V

2012-11-15

The reactivity of gold(III) complexes is analyzed for a series of derivatives of 3-azapentane-1,5-diamine (dien) tridentate ligand that can contain some bulky substituents. Two distinct series of compounds are considered where the dien ligand is either deprotonated (R-dien-H) or protonated (R-dien) at the secondary amine where R = ethyl (Et) or methyl (Me). While the deprotonated species will occur in neutral and basic solutions, the protonated forms are likely to be present in acidic environment. Hydration reaction (water/Cl(-) ligand exchange) of 14 complexes is modeled with quantum chemical calculations. Our calculations predict that the reactivity decreases with the increase in the molecular volume of the substituted dien ligand, and the calculated rate constants are in satisfactory agreement with experimental results. In addition, quantitative structure/reactivity models are proposed where the angle between the entering and leaving groups in the transition state structure (the reactivity angle) is used as a molecular descriptor. These models explain the trend of the relative reactivity of these complexes and can be used to design new ligands for gold(III) complexes aiming to adjust the reactivity of the complex.

Prioritization of anti-malarial hits from nature: chemo-informatic profiling of natural products with in vitro antiplasmodial activities and currently registered anti-malarial drugs.

PubMed

Egieyeh, Samuel Ayodele; Syce, James; Malan, Sarel F; Christoffels, Alan

2016-01-29

A large number of natural products have shown in vitro antiplasmodial activities. Early identification and prioritization of these natural products with potential for novel mechanism of action, desirable pharmacokinetics and likelihood for development into drugs is advantageous. Chemo-informatic profiling of these natural products were conducted and compared to currently registered anti-malarial drugs (CRAD). Natural products with in vitro antiplasmodial activities (NAA) were compiled from various sources. These natural products were sub-divided into four groups based on inhibitory concentration (IC50). Key molecular descriptors and physicochemical properties were computed for these compounds and analysis of variance used to assess statistical significance amongst the sets of compounds. Molecular similarity analysis, estimation of drug-likeness, in silico pharmacokinetic profiling, and exploration of structure-activity landscape were also carried out on these sets of compounds. A total of 1040 natural products were selected and a total of 13 molecular descriptors were analysed. Significant differences were observed among the sub-groups of NAA and CRAD for at least 11 of the molecular descriptors, including number of hydrogen bond donors and acceptors, molecular weight, polar and hydrophobic surface areas, chiral centres, oxygen and nitrogen atoms, and shape index. The remaining molecular descriptors, including clogP, number of rotatable bonds and number of aromatic rings, did not show any significant difference when comparing the two compound sets. Molecular similarity and chemical space analysis identified natural products that were structurally diverse from CRAD. Prediction of the pharmacokinetic properties and drug-likeness of these natural products identified over 50% with desirable drug-like properties. Nearly 70% of all natural products were identified as potentially promiscuous compounds. Structure-activity landscape analysis highlighted compound pairs that form 'activity cliffs'. In all, prioritization strategies for the NAA were proposed. Chemo-informatic profiling of NAA and CRAD have produced a wealth of information that may guide decisions and facilitate anti-malarial drug development from natural products. Articulation of the information provided within an interactive data-mining environment led to a prioritized list of NAA.

Self-organizing maps of molecular descriptors for sesquiterpene lactones and their application to the chemotaxonomy of the Asteraceae family.

PubMed

Scotti, Marcus T; Emerenciano, Vicente; Ferreira, Marcelo J P; Scotti, Luciana; Stefani, Ricardo; da Silva, Marcelo S; Mendonça Junior, Francisco Jaime B

2012-04-20

The Asteraceae, one of the largest families among angiosperms, is chemically characterised by the production of sesquiterpene lactones (SLs). A total of 1,111 SLs, which were extracted from 658 species, 161 genera, 63 subtribes and 15 tribes of Asteraceae, were represented and registered in two dimensions in the SISTEMATX, an in-house software system, and were associated with their botanical sources. The respective 11 block of descriptors: Constitutional, Functional groups, BCUT, Atom-centred, 2D autocorrelations, Topological, Geometrical, RDF, 3D-MoRSE, GETAWAY and WHIM were used as input data to separate the botanical occurrences through self-organising maps. Maps that were generated with each descriptor divided the Asteraceae tribes, with total index values between 66.7% and 83.6%. The analysis of the results shows evident similarities among the Heliantheae, Helenieae and Eupatorieae tribes as well as between the Anthemideae and Inuleae tribes. Those observations are in agreement with systematic classifications that were proposed by Bremer, which use mainly morphological and molecular data, therefore chemical markers partially corroborate with these classifications. The results demonstrate that the atom-centred and RDF descriptors can be used as a tool for taxonomic classification in low hierarchical levels, such as tribes. Descriptors obtained through fragments or by the two-dimensional representation of the SL structures were sufficient to obtain significant results, and better results were not achieved by using descriptors derived from three-dimensional representations of SLs. Such models based on physico-chemical properties can project new design SLs, similar structures from literature or even unreported structures in two-dimensional chemical space. Therefore, the generated SOMs can predict the most probable tribe where a biologically active molecule can be found according Bremer classification.

RPBS: Rotational Projected Binary Structure for point cloud representation

NASA Astrophysics Data System (ADS)

Fang, Bin; Zhou, Zhiwei; Ma, Tao; Hu, Fangyu; Quan, Siwen; Ma, Jie

2018-03-01

In this paper, we proposed a novel three-dimension local surface descriptor named RPBS for point cloud representation. First, points cropped form the query point within a predefined radius is regard as a local surface patch. Then pose normalization is done to the local surface to equip our descriptor with the invariance to rotation transformation. To obtain more information about the cropped surface, multi-view representation is formed by successively rotating it along the coordinate axis. Further, orthogonal projections to the three coordinate plane are adopted to construct two-dimension distribution matrixes, and binarization is applied to each matrix by following the rule that whether the grid is occupied, if yes, set the grid one, otherwise zero. We calculate the binary maps from all the viewpoints and concatenate them together as the final descriptor. Comparative experiments for evaluating our proposed descriptor is conducted on the standard dataset named Bologna with several state-of-the-art 3D descriptors, and results show that our descriptor achieves the best performance on feature matching experiments.

Automatized Assessment of Protective Group Reactivity: A Step Toward Big Reaction Data Analysis.

PubMed

Lin, Arkadii I; Madzhidov, Timur I; Klimchuk, Olga; Nugmanov, Ramil I; Antipin, Igor S; Varnek, Alexandre

2016-11-28

We report a new method to assess protective groups (PGs) reactivity as a function of reaction conditions (catalyst, solvent) using raw reaction data. It is based on an intuitive similarity principle for chemical reactions: similar reactions proceed under similar conditions. Technically, reaction similarity can be assessed using the Condensed Graph of Reaction (CGR) approach representing an ensemble of reactants and products as a single molecular graph, i.e., as a pseudomolecule for which molecular descriptors or fingerprints can be calculated. CGR-based in-house tools were used to process data for 142,111 catalytic hydrogenation reactions extracted from the Reaxys database. Our results reveal some contradictions with famous Greene's Reactivity Charts based on manual expert analysis. Models developed in this study show high accuracy (ca. 90%) for predicting optimal experimental conditions of protective group deprotection.

Prediction of Radical Scavenging Activities of Anthocyanins Applying Adaptive Neuro-Fuzzy Inference System (ANFIS) with Quantum Chemical Descriptors

PubMed Central

Jhin, Changho; Hwang, Keum Taek

2014-01-01

Radical scavenging activity of anthocyanins is well known, but only a few studies have been conducted by quantum chemical approach. The adaptive neuro-fuzzy inference system (ANFIS) is an effective technique for solving problems with uncertainty. The purpose of this study was to construct and evaluate quantitative structure-activity relationship (QSAR) models for predicting radical scavenging activities of anthocyanins with good prediction efficiency. ANFIS-applied QSAR models were developed by using quantum chemical descriptors of anthocyanins calculated by semi-empirical PM6 and PM7 methods. Electron affinity (A) and electronegativity (χ) of flavylium cation, and ionization potential (I) of quinoidal base were significantly correlated with radical scavenging activities of anthocyanins. These descriptors were used as independent variables for QSAR models. ANFIS models with two triangular-shaped input fuzzy functions for each independent variable were constructed and optimized by 100 learning epochs. The constructed models using descriptors calculated by both PM6 and PM7 had good prediction efficiency with Q-square of 0.82 and 0.86, respectively. PMID:25153627

Neural network approach to quantum-chemistry data: accurate prediction of density functional theory energies.

PubMed

Balabin, Roman M; Lomakina, Ekaterina I

2009-08-21

Artificial neural network (ANN) approach has been applied to estimate the density functional theory (DFT) energy with large basis set using lower-level energy values and molecular descriptors. A total of 208 different molecules were used for the ANN training, cross validation, and testing by applying BLYP, B3LYP, and BMK density functionals. Hartree-Fock results were reported for comparison. Furthermore, constitutional molecular descriptor (CD) and quantum-chemical molecular descriptor (QD) were used for building the calibration model. The neural network structure optimization, leading to four to five hidden neurons, was also carried out. The usage of several low-level energy values was found to greatly reduce the prediction error. An expected error, mean absolute deviation, for ANN approximation to DFT energies was 0.6+/-0.2 kcal mol(-1). In addition, the comparison of the different density functionals with the basis sets and the comparison of multiple linear regression results were also provided. The CDs were found to overcome limitation of the QD. Furthermore, the effective ANN model for DFT/6-311G(3df,3pd) and DFT/6-311G(2df,2pd) energy estimation was developed, and the benchmark results were provided.

Towards better modelling of drug-loading in solid lipid nanoparticles: Molecular dynamics, docking experiments and Gaussian Processes machine learning.

PubMed

Hathout, Rania M; Metwally, Abdelkader A

2016-11-01

This study represents one of the series applying computer-oriented processes and tools in digging for information, analysing data and finally extracting correlations and meaningful outcomes. In this context, binding energies could be used to model and predict the mass of loaded drugs in solid lipid nanoparticles after molecular docking of literature-gathered drugs using MOE® software package on molecularly simulated tripalmitin matrices using GROMACS®. Consequently, Gaussian processes as a supervised machine learning artificial intelligence technique were used to correlate the drugs' descriptors (e.g. M.W., xLogP, TPSA and fragment complexity) with their molecular docking binding energies. Lower percentage bias was obtained compared to previous studies which allows the accurate estimation of the loaded mass of any drug in the investigated solid lipid nanoparticles by just projecting its chemical structure to its main features (descriptors). Copyright © 2016 Elsevier B.V. All rights reserved.

Modular Chemical Descriptor Language (MCDL): Stereochemical modules

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gakh, Andrei A; Burnett, Michael N; Trepalin, Sergei V.

2011-01-01

In our previous papers we introduced the Modular Chemical Descriptor Language (MCDL) for providing a linear representation of chemical information. A subsequent development was the MCDL Java Chemical Structure Editor which is capable of drawing chemical structures from linear representations and generating MCDL descriptors from structures. In this paper we present MCDL modules and accompanying software that incorporate unique representation of molecular stereochemistry based on Cahn-Ingold-Prelog and Fischer ideas in constructing stereoisomer descriptors. The paper also contains additional discussions regarding canonical representation of stereochemical isomers, and brief algorithm descriptions of the open source LINDES, Java applet, and Open Babel MCDLmore » processing module software packages. Testing of the upgraded MCDL Java Chemical Structure Editor on compounds taken from several large and diverse chemical databases demonstrated satisfactory performance for storage and processing of stereochemical information in MCDL format.« less

Synthesis, spectroscopic characterization, theoretical study and anti-hepatic cancer activity study of 4-(1E,3Z,6E)-3-hydroxy-7-(4-hydroxy-3-methoxyphenyl)-5-oxohepta-1,3,6-trien-1-yl)-2-methoxyphenyl 4-nitrobenzoate, a novel curcumin congener

NASA Astrophysics Data System (ADS)

Srivastava, Sangeeta; Gupta, Preeti; Singh, Ranvijay Pratap; Jafri, Asif; Arshad, M.; Banerjee, Monisha

2017-08-01

In the present work 4-(1E,3Z,6E)-3-hydroxy-7-(4-hydroxy-3-methoxyphenyl)-5-oxohepta-1,3,6-trien-1-yl)-2-methoxyphenyl 4-nitrobenzoate (2), a novel curcumin ester was synthesized. The molecular structure and spectroscopic analysis were performed using experimental techniques like FT-IR, 1H,13C NMR, mass and UV-visible as well as theoretical calculations. The theoretical calculations were done by DFT level of theory using B3LYP/6-31G (d,p) basis set. The vibrational wavenumbers were calculated using DFT method and assigned with the help of potential energy distribution (PED). The electronic properties such as frontier orbitals and band gap energies have been calculated using time dependent density functional theory (TD-DFT). The strength and nature of weak intramolecular interactions have been studied by AIM approach. Global and local reactivity descriptors have been computed to predict reactivity and reactive sites in the molecule. First hyperpolarizability values have been calculated to describe the nonlinear optical (NLO) property of the synthesized compounds. Molecular electrostatic potential (MEP) analysis has also been carried out. The anti-hepatic cancer activity of compound 2 was also carried out.

Synthesis, crystal growth, single crystal X-ray analysis and vibrational spectral studies of (2E)-3-(2-chloro-4-fluorophenyl)-1-(3,4-dimethoxyphenyl)prop-2-en-1-one: A combined DFT study

NASA Astrophysics Data System (ADS)

Chidan Kumar, C. S.; Balachandran, V.; Fun, Hoong-Kun; Chandraju, Siddegowda; Quah, Ching Kheng

2015-11-01

A new chalcone derivative, (2E)-3-(2-chloro-4-fluorophenyl)-1-(3,4-dimethoxyphenyl)prop-2-en-1-one (a) was synthesized and single crystals were grown by slow evaporation technique. The FT-Raman and FT-IR spectra of the sample were recorded in the region 3500-100 cm-1 and 4000-400 cm-1 respectively. The spectra were interpreted with the aid of normal coordinate analysis, following structure optimizations and force field calculations based on B3LYP/6-31G (d) level of theory. Normal coordinate calculations were performed using the DFT force field corrected by a recommended set of scaling factors yielding fairly good agreement between the observed and calculated wavenumbers. The total electron density and molecular electrostatic potential surfaces of the molecule were constructed using B3LYP/6-31G (d) method to display electrostatic potential (electron + nuclei) distribution, molecular shape, size, and dipole moments of the molecule. HOMO and LUMO energies were also calculated. Stability of the molecule arising from hyperconjugative interactions and charge delocalization has been analyzed using natural bond orbital (NBO) analysis. Global and local reactivity descriptors and dipole moment (μ), static polarizability (α), first order hyperpolarizability (β) and optical gap (ΔE) were also calculated to study the NLO property of our title compound.

Yoink: An interaction-based partitioning API.

PubMed

Zheng, Min; Waller, Mark P

2018-05-15

Herein, we describe the implementation details of our interaction-based partitioning API (application programming interface) called Yoink for QM/MM modeling and fragment-based quantum chemistry studies. Interactions are detected by computing density descriptors such as reduced density gradient, density overlap regions indicator, and single exponential decay detector. Only molecules having an interaction with a user-definable QM core are added to the QM region of a hybrid QM/MM calculation. Moreover, a set of molecule pairs having density-based interactions within a molecular system can be computed in Yoink, and an interaction graph can then be constructed. Standard graph clustering methods can then be applied to construct fragments for further quantum chemical calculations. The Yoink API is licensed under Apache 2.0 and can be accessed via yoink.wallerlab.org. © 2018 Wiley Periodicals, Inc. © 2018 Wiley Periodicals, Inc.

A new descriptor via bio-mimetic chromatography and modeling for the blood brain barrier (Part II).

PubMed

Kouskoura, Maria G; Piteni, Aikaterini I; Markopoulou, Catherine K

2018-05-25

Within the context of drug design methodology for the central nervous system (CNS), a predictive model which can shorten the process of finding new candidate drugs was developed. Therefore, the retention time of 51 molecules which are clinically established to enter the blood brain barrier (BBB), were recorded on two HPLC columns. For this purpose, a lipophilic butyl (C 4 ) stationary phase was used to simulate the behavior of a drug regarding BBB permeability and a zwitterionic-HILIC to simulate blood. The results were plotted as Y variables on two Partial Least Squares (PLS) models, while 25 specific physicochemical properties (significant for lipid bilayers BBB permeation or blood) were used as X descriptors. Both models can be utilized to predict the drugability of a new molecule avoiding needless animal experiments, as well as time and material consuming syntheses. The developed models were validated (R 2  ≥ 0.90, Q 2  ≥ 0.83), and based on the results specific variables were proved to be significant for the studied phenomenon. Additionally, a new factor symbolized as MT was introduced. MT incorporated the experimental results and it was calculated by the fraction of the sum of the retention time of the drug on the two columns (t r(butyl)  + t r(HILIC) ) divided by the molecular volume (V m ) of each analyte. This new descriptor was used as an equivalent to the logarithm of BBB permeability (logBB) and may indicate the ability of a new molecule to act as a candidate drug able to enter the BBB. Comprehending the extend of contribution of several molecular attributes to the in vivo distribution of a drug may enlighten the knowledge on pharmacokinetics and clinical variation, and enable scientists to design more efficient drug molecules. Copyright © 2018 Elsevier B.V. All rights reserved.

wACSF—Weighted atom-centered symmetry functions as descriptors in machine learning potentials

NASA Astrophysics Data System (ADS)

Gastegger, M.; Schwiedrzik, L.; Bittermann, M.; Berzsenyi, F.; Marquetand, P.

2018-06-01

We introduce weighted atom-centered symmetry functions (wACSFs) as descriptors of a chemical system's geometry for use in the prediction of chemical properties such as enthalpies or potential energies via machine learning. The wACSFs are based on conventional atom-centered symmetry functions (ACSFs) but overcome the undesirable scaling of the latter with an increasing number of different elements in a chemical system. The performance of these two descriptors is compared using them as inputs in high-dimensional neural network potentials (HDNNPs), employing the molecular structures and associated enthalpies of the 133 855 molecules containing up to five different elements reported in the QM9 database as reference data. A substantially smaller number of wACSFs than ACSFs is needed to obtain a comparable spatial resolution of the molecular structures. At the same time, this smaller set of wACSFs leads to a significantly better generalization performance in the machine learning potential than the large set of conventional ACSFs. Furthermore, we show that the intrinsic parameters of the descriptors can in principle be optimized with a genetic algorithm in a highly automated manner. For the wACSFs employed here, we find however that using a simple empirical parametrization scheme is sufficient in order to obtain HDNNPs with high accuracy.

DFT calculations on molecular structure, spectral analysis, multiple interactions, reactivity, NLO property and molecular docking study of flavanol-2,4-dinitrophenylhydrazone

NASA Astrophysics Data System (ADS)

Singh, Ravindra Kumar; Singh, Ashok Kumar

2017-02-01

A new flavanol-2,4-dinitrophenylhydrazone (FDNP) was synthesized and its structure was confirmed by FT-IR, FT-Raman, 1H NMR, mass spectrometry and elemental analysis. All quantum chemical calculations were carried out at level of density functional theory (DFT) with B3LYP functional using 6-311++ G (d,p) basis atomic set. UV-Vis absorption spectra for the singlet-singlet transition computed for fully optimized ground state geometry using Time-Dependent-Density Functional Theory (TD-DFT) with CAM-B3LYP functional was found to be in consistent with that of experimental findings. Analysis of vibrational (FT-IR and FT-Raman) spectrum and their assignments has been done by computing Potential Energy Distribution (PED) using Gar2ped. HOMO-LUMO analysis was performed and reactivity descriptors were calculated. Calculated global electrophilicity index (ω = 7.986 eV) shows molecule to be a strong electrophile. 1H NMR chemical shift calculated with the help of gauge-including atomic orbital (GIAO) approach shows agreement with experimental data. Various intramolecular interactions were analysed by AIM approach. DFT computed total first static hyperpolarizability (β0 = 189.03 × 10-30 esu) indicates that title molecule can be used as attractive future NLO material. Solvent induced effects on the NLO properties studied by using self-consistent reaction field (SCRF) method shows that β0 value increases with increase in solvent polarity. To study the thermal behaviour of title molecule, thermodynamic properties such as heat capacity, entropy and enthalpy change at various temperatures have been calculated and reported. Molecular docking results suggests title molecule to be a potential kinase inhibitor and might be used in future for designing of new anticancer drug.

Modeling and Prediction of Drug Dispersability in Polyvinylpyrrolidone-Vinyl Acetate Copolymer Using a Molecular Descriptor.

PubMed

DeBoyace, Kevin; Buckner, Ira S; Gong, Yuchuan; Ju, Tzu-Chi Rob; Wildfong, Peter L D

2018-01-01

The expansion of a novel in silico model for the prediction of the dispersability of 18 model compounds with polyvinylpyrrolidone-vinyl acetate copolymer is described. The molecular descriptor R3m (atomic mass weighted 3rd-order autocorrelation index) is shown to be predictive of the formation of amorphous solid dispersions at 2 drug loadings (15% and 75% w/w) using 2 preparation methods (melt quenching and solvent evaporation using a rotary evaporator). Cosolidified samples were characterized using a suite of analytical techniques, which included differential scanning calorimetry, powder X-ray diffraction, pair distribution function analysis, polarized light microscopy, and hot stage microscopy. Logistic regression was applied, where appropriate, to model the success and failure of compound dispersability in polyvinylpyrrolidone-vinyl acetate copolymer. R3m had combined prediction accuracy greater than 90% for tested samples. The usefulness of this descriptor appears to be associated with the presence of heavy atoms in the molecular structure of the active pharmaceutical ingredient, and their location with respect to the geometric center of the molecule. Given the higher electronegativity and atomic volume of these types of atoms, it is hypothesized that they may impact the molecular mobility of the active pharmaceutical ingredient, or increase the likelihood of forming nonbonding interactions with the carrier polymer. Copyright © 2018 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

Synthesis of 4-((1E, 6E)-7-(4-hydroxy-3-methoxyphenyl)-3, 5-dioxohepta-1, 6-dienyl)-2-methoxyphenyl 4-fluorobenzoate, a novel monoester derivative of curcumin, its experimental and theoretical (DFT) studies

NASA Astrophysics Data System (ADS)

Srivastava, Sangeeta; Gupta, Preeti; Amandeep; Singh, Ranvijay Pratap

2016-04-01

Curcumin (1), isolated as a major component from the chloroform extract of Curcuma longa was converted to its ester derivative 4-((1E, 6E)-7-(4-hydroxy-3-methoxyphenyl)-3,5-dioxohepta-1,6-dienyl)-2-methoxyphenyl 4-fluorobenzoate (2). The compound has been characterized with the help of 1H, 13C NMR, UV, IR and mass spectrometry. The molecular geometry of synthesized compound was calculated in ground state by Density functional theory (DFT/B3LYP) using 6-31G (d,p) basis set. 1H and 13C NMR chemical shifts were calculated in ground state by using Gauge-Including Atomic Orbital (GIAO) approach and these values were correlated with experimental observations. The electronic properties such as HOMO and LUMO energies were calculated using time dependent Density Functional Theory (TD-DFT). Stability of the molecule as a result of hyper conjugative interactions and electron delocalization were analysed using Natural bond orbital (NBO) analysis. Intramolecular interactions were analysed by AIM (Atom in molecule) approach. Global reactivity descriptors were calculated to study the reactive site within molecule. The vibrational wavenumbers were calculated using DFT method and assigned with the help of potential energy distribution (PED). First hyperpolarizability values have been calculated to describe the nonlinear optical (NLO) property of the synthesized compounds. Molecular electrostatic potential (MEP) analysis has also been carried out.

Genetic divergence among accessions of melon from traditional agriculture of the Brazilian Northeast.

PubMed

Aragão, F A S; Torres Filho, J; Nunes, G H S; Queiróz, M A; Bordallo, P N; Buso, G S C; Ferreira, M A; Costa, Z P; Bezerra Neto, F

2013-12-06

The genetic divergence of 38 melon accessions from traditional agriculture of the Brazilian Northeast and three commercial hybrids were evaluated using fruit descriptors and microsatellite markers. The melon germplasm belongs to the botanic varieties cantalupensis (19), momordica (7), conomon (4), and inodorus (3), and to eight genotypes that were identified only at the species level. The fruit descriptors evaluated were: number of fruits per plant (NPF), fruit mass (FM; kg), fruit longitudinal diameter (LD; cm), fruit transversal diameter (TD; cm), shape index based on the LD/TD ratio, flesh pulp thickness, cavity thickness (CT; cm), firmness fruit pulp (N), and soluble solids (SS; °Brix). The results showed high variability for all descriptors, especially for NPF, LD, and FM. The grouping analysis based on fruit descriptors produced eight groups without taxonomic criteria. The LD (22.52%), NPF (19.70%), CT (16.13%), and SS (9.57%) characteristics were the descriptors that contributed the most to genotype dissimilarity. The 17 simple sequence repeat polymorphic markers amplified 41 alleles with an average of 2.41 alleles and three genotypes per locus. Some markers presented a high frequency for the main allele. The genetic diversity ranged from 0.07 to 0.60, the observed heterozygosity had very low values, and the mean polymorphism information content was 0.32. Molecular genetic similarity analyses clustered the accessions in 13 groups, also not following taxonomic ranks. There was no association between morphoagronomic and molecular groupings. In conclusion, there was great variability among the accessions and among and within botanic groups.

Quantitative structure-retention relationships of polycyclic aromatic hydrocarbons gas-chromatographic retention indices.

PubMed

Drosos, Juan Carlos; Viola-Rhenals, Maricela; Vivas-Reyes, Ricardo

2010-06-25

Polycyclic aromatic compounds (PAHs) are of concern in environmental chemistry and toxicology. In the present work, a QSRR study was performed for 209 previously reported PAHs using quantum mechanics and other sources descriptors estimated by different approaches. The B3LYP/6-31G* level of theory was used for geometrical optimization and quantum mechanics related variables. A good linear relationship between gas-chromatographic retention index and electronic or topologic descriptors was found by stepwise linear regression analysis. The molecular polarizability (alpha) and the second order molecular connectivity Kier and Hall index ((2)chi) showed evidence of significant correlation with retention index by means of important squared coefficient of determination, (R(2)), values (R(2)=0.950 and 0.962, respectively). A one variable QSRR model is presented for each descriptor and both models demonstrates a significant predictive capacity established using the leave-many-out LMO (excluding 25% of rows) cross validation method's q(2) cross-validation coefficients q(2)(CV-LMO25%), (obtained q(2)(CV-LMO25%) 0.947 and 0.960, respectively). Furthermore, the physicochemical interpretation of selected descriptors allowed detailed explanation of the source of the observed statistical correlation. The model analysis suggests that only one descriptor is sufficient to establish a consistent retention index-structure relationship. Moderate or non-significant improve was observed for quantitative results or statistical validation parameters when introducing more terms in predictive equation. The one parameter QSRR proposed model offers a consistent scheme to predict chromatographic properties of PAHs compounds. Copyright 2010 Elsevier B.V. All rights reserved.

Selection, application, and validation of a set of molecular descriptors for nuclear receptor ligands.

PubMed

Stewart, Eugene L; Brown, Peter J; Bentley, James A; Willson, Timothy M

2004-08-01

A methodology for the selection and validation of nuclear receptor ligand chemical descriptors is described. After descriptors for a targeted chemical space were selected, a virtual screening methodology utilizing this space was formulated for the identification of potential NR ligands from our corporate collection. Using simple descriptors and our virtual screening method, we are able to quickly identify potential NR ligands from a large collection of compounds. As validation of the virtual screening procedure, an 8, 000-membered NR targeted set and a 24, 000-membered diverse control set of compounds were selected from our in-house general screening collection and screened in parallel across a number of orphan NR FRET assays. For the two assays that provided at least one hit per set by the established minimum pEC(50) for activity, the results showed a 2-fold increase in the hit-rate of the targeted compound set over the diverse set.

Potential for protein surface shape analysis using spherical harmonics and 3D Zernike descriptors.

PubMed

Venkatraman, Vishwesh; Sael, Lee; Kihara, Daisuke

2009-01-01

With structure databases expanding at a rapid rate, the task at hand is to provide reliable clues to their molecular function and to be able to do so on a large scale. This, however, requires suitable encodings of the molecular structure which are amenable to fast screening. To this end, moment-based representations provide a compact and nonredundant description of molecular shape and other associated properties. In this article, we present an overview of some commonly used representations with specific focus on two schemes namely spherical harmonics and their extension, the 3D Zernike descriptors. Key features and differences of the two are reviewed and selected applications are highlighted. We further discuss recent advances covering aspects of shape and property-based comparison at both global and local levels and demonstrate their applicability through some of our studies.

Benchmarking of protein descriptor sets in proteochemometric modeling (part 2): modeling performance of 13 amino acid descriptor sets

PubMed Central

2013-01-01

Background While a large body of work exists on comparing and benchmarking descriptors of molecular structures, a similar comparison of protein descriptor sets is lacking. Hence, in the current work a total of 13 amino acid descriptor sets have been benchmarked with respect to their ability of establishing bioactivity models. The descriptor sets included in the study are Z-scales (3 variants), VHSE, T-scales, ST-scales, MS-WHIM, FASGAI, BLOSUM, a novel protein descriptor set (termed ProtFP (4 variants)), and in addition we created and benchmarked three pairs of descriptor combinations. Prediction performance was evaluated in seven structure-activity benchmarks which comprise Angiotensin Converting Enzyme (ACE) dipeptidic inhibitor data, and three proteochemometric data sets, namely (1) GPCR ligands modeled against a GPCR panel, (2) enzyme inhibitors (NNRTIs) with associated bioactivities against a set of HIV enzyme mutants, and (3) enzyme inhibitors (PIs) with associated bioactivities on a large set of HIV enzyme mutants. Results The amino acid descriptor sets compared here show similar performance (<0.1 log units RMSE difference and <0.1 difference in MCC), while errors for individual proteins were in some cases found to be larger than those resulting from descriptor set differences ( > 0.3 log units RMSE difference and >0.7 difference in MCC). Combining different descriptor sets generally leads to better modeling performance than utilizing individual sets. The best performers were Z-scales (3) combined with ProtFP (Feature), or Z-Scales (3) combined with an average Z-Scale value for each target, while ProtFP (PCA8), ST-Scales, and ProtFP (Feature) rank last. Conclusions While amino acid descriptor sets capture different aspects of amino acids their ability to be used for bioactivity modeling is still – on average – surprisingly similar. Still, combining sets describing complementary information consistently leads to small but consistent improvement in modeling performance (average MCC 0.01 better, average RMSE 0.01 log units lower). Finally, performance differences exist between the targets compared thereby underlining that choosing an appropriate descriptor set is of fundamental for bioactivity modeling, both from the ligand- as well as the protein side. PMID:24059743

Learning moment-based fast local binary descriptor

NASA Astrophysics Data System (ADS)

Bellarbi, Abdelkader; Zenati, Nadia; Otmane, Samir; Belghit, Hayet

2017-03-01

Recently, binary descriptors have attracted significant attention due to their speed and low memory consumption; however, using intensity differences to calculate the binary descriptive vector is not efficient enough. We propose an approach to binary description called POLAR_MOBIL, in which we perform binary tests between geometrical and statistical information using moments in the patch instead of the classical intensity binary test. In addition, we introduce a learning technique used to select an optimized set of binary tests with low correlation and high variance. This approach offers high distinctiveness against affine transformations and appearance changes. An extensive evaluation on well-known benchmark datasets reveals the robustness and the effectiveness of the proposed descriptor, as well as its good performance in terms of low computation complexity when compared with state-of-the-art real-time local descriptors.

Combined computational-experimental approach to predict blood-brain barrier (BBB) permeation based on "green" salting-out thin layer chromatography supported by simple molecular descriptors.

PubMed

Ciura, Krzesimir; Belka, Mariusz; Kawczak, Piotr; Bączek, Tomasz; Markuszewski, Michał J; Nowakowska, Joanna

2017-09-05

The objective of this paper is to build QSRR/QSAR model for predicting the blood-brain barrier (BBB) permeability. The obtained models are based on salting-out thin layer chromatography (SOTLC) constants and calculated molecular descriptors. Among chromatographic methods SOTLC was chosen, since the mobile phases are free of organic solvent. As consequences, there are less toxic, and have lower environmental impact compared to classical reserved phases liquid chromatography (RPLC). During the study three stationary phase silica gel, cellulose plates and neutral aluminum oxide were examined. The model set of solutes presents a wide range of log BB values, containing compounds which cross the BBB readily and molecules poorly distributed to the brain including drugs acting on the nervous system as well as peripheral acting drugs. Additionally, the comparison of three regression models: multiple linear regression (MLR), partial least-squares (PLS) and orthogonal partial least squares (OPLS) were performed. The designed QSRR/QSAR models could be useful to predict BBB of systematically synthesized newly compounds in the drug development pipeline and are attractive alternatives of time-consuming and demanding directed methods for log BB measurement. The study also shown that among several regression techniques, significant differences can be obtained in models performance, measured by R 2 and Q 2 , hence it is strongly suggested to evaluate all available options as MLR, PLS and OPLS. Copyright © 2017 Elsevier B.V. All rights reserved.

Conformational locking by design: relating strain energy with luminescence and stability in rigid metal-organic frameworks.

PubMed

Shustova, Natalia B; Cozzolino, Anthony F; Dincă, Mircea

2012-12-05

Minimization of the torsional barrier for phenyl ring flipping in a metal-organic framework (MOF) based on the new ethynyl-extended octacarboxylate ligand H(8)TDPEPE leads to a fluorescent material with a near-dark state. Immobilization of the ligand in the rigid structure also unexpectedly causes significant strain. We used DFT calculations to estimate the ligand strain energies in our and all other topologically related materials and correlated these with empirical structural descriptors to derive general rules for trapping molecules in high-energy conformations within MOFs. These studies portend possible applications of MOFs for studying fundamental concepts related to conformational locking and its effects on molecular reactivity and chromophore photophysics.

Predicting human skin absorption of chemicals: development of a novel quantitative structure activity relationship.

PubMed

Luo, Wen; Medrek, Sarah; Misra, Jatin; Nohynek, Gerhard J

2007-02-01

The objective of this study was to construct and validate a quantitative structure-activity relationship model for skin absorption. Such models are valuable tools for screening and prioritization in safety and efficacy evaluation, and risk assessment of drugs and chemicals. A database of 340 chemicals with percutaneous absorption was assembled. Two models were derived from the training set consisting 306 chemicals (90/10 random split). In addition to the experimental K(ow) values, over 300 2D and 3D atomic and molecular descriptors were analyzed using MDL's QsarIS computer program. Subsequently, the models were validated using both internal (leave-one-out) and external validation (test set) procedures. Using the stepwise regression analysis, three molecular descriptors were determined to have significant statistical correlation with K(p) (R2 = 0.8225): logK(ow), X0 (quantification of both molecular size and the degree of skeletal branching), and SsssCH (count of aromatic carbon groups). In conclusion, two models to estimate skin absorption were developed. When compared to other skin absorption QSAR models in the literature, our model incorporated more chemicals and explored a large number of descriptors. Additionally, our models are reasonably predictive and have met both internal and external statistical validations.

Selection of molecular descriptors with artificial intelligence for the understanding of HIV-1 protease peptidomimetic inhibitors-activity.

PubMed

Sirois, S; Tsoukas, C M; Chou, Kuo-Chen; Wei, Dongqing; Boucher, C; Hatzakis, G E

2005-03-01

Quantitative Structure Activity Relationship (QSAR) techniques are used routinely by computational chemists in drug discovery and development to analyze datasets of compounds. Quantitative numerical methods like Partial Least Squares (PLS) and Artificial Neural Networks (ANN) have been used on QSAR to establish correlations between molecular properties and bioactivity. However, ANN may be advantageous over PLS because it considers the interrelations of the modeled variables. This study focused on the HIV-1 Protease (HIV-1 Pr) inhibitors belonging to the peptidomimetic class of compounds. The main objective was to select molecular descriptors with the best predictive value for antiviral potency (Ki). PLS and ANN were used to predict Ki activity of HIV-1 Pr inhibitors and the results were compared. To address the issue of dimensionality reduction, Genetic Algorithms (GA) were used for variable selection and their performance was compared against that of ANN. Finally, the structure of the optimum ANN achieving the highest Pearson's-R coefficient was determined. On the basis of Pearson's-R, PLS and ANN were compared to determine which exhibits maximum performance. Training and validation of models was performed on 15 random split sets of the master dataset consisted of 231 compounds. For each compound 192 molecular descriptors were considered. The molecular structure and constant of inhibition (Ki) were selected from the NIAID database. Study findings suggested that non-covalent interactions such as hydrophobicity, shape and hydrogen bonding describe well the antiviral activity of the HIV-1 Pr compounds. The significance of lipophilicity and relationship to HIV-1 associated hyperlipidemia and lipodystrophy syndrome warrant further investigation.

QSAR, DFT and molecular modeling studies of peptides from HIV-1 to describe their recognition properties by MHC-I.

PubMed

Andrade-Ochoa, S; García-Machorro, J; Bello, Martiniano; Rodríguez-Valdez, L M; Flores-Sandoval, C A; Correa-Basurto, J

2017-08-03

Human immunodeficiency virus type-1 (HIV-1) has infected more than 40 million people around the world. HIV-1 treatment still has several side effects, and the development of a vaccine, which is another potential option for decreasing human infections, has faced challenges. This work presents a computational study that includes a quantitative structure activity relationship(QSAR) using density functional theory(DFT) for reported peptides to identify the principal quantum mechanics descriptors related to peptide activity. In addition, the molecular recognition properties of these peptides are explored on major histocompatibility complex I (MHC-I) through docking and molecular dynamics (MD) simulations accompanied by the Molecular Mechanics Generalized Born Surface Area (MMGBSA) approach for correlating peptide activity reported elsewhere vs. theoretical peptide affinity. The results show that the carboxylic acid and hydroxyl groups are chemical moieties that have an inverse relationship with biological activity. The number of sulfides, pyrroles and imidazoles from the peptide structure are directly related to biological activity. In addition, the HOMO orbital energy values of the total absolute charge and the Ghose-Crippen molar refractivity of peptides are descriptors directly related to the activity and affinity on MHC-I. Docking and MD simulation studies accompanied by an MMGBSA analysis show that the binding free energy without considering the entropic contribution is energetically favorable for all the complexes. Furthermore, good peptide interaction with the most affinity is evaluated experimentally for three proteins. Overall, this study shows that the combination of quantum mechanics descriptors and molecular modeling studies could help describe the immunogenic properties of peptides from HIV-1.

Effect of cation-anion interactions on the structural and vibrational properties of 1-buthyl-3-methyl imidazolium nitrate ionic liquid

NASA Astrophysics Data System (ADS)

Kausteklis, Jonas; Aleksa, Valdemaras; Iramain, Maximiliano A.; Brandán, Silvia Antonia

2018-07-01

The cation-anion interactions present in the 1-butyl-3-methylimidazolium nitrate ionic liquid [BMIm][NO3] were studied by using density functional theory (DFT) calculations and the experimental FT-Raman spectrum in liquid phase and its available FT-IR spectrum. For the three most stable conformers found in the potential energy surface and their 1-butyl-3-methylimidazolium [BMIm] cation, the atomic charges, molecular electrostatic potentials, stabilization energies, bond orders and topological properties were computed by using NBO and AIM calculations and the hybrid B3LYP level of theory with the 6-31G* and 6-311++G** basis sets. The force fields, force constants and complete vibrational assignments were also reported for those species by using their internal coordinates and the scaled quantum mechanical force field (SQMFF) approach. The dimeric species of [BMIm][NO3] were also considered because their presence could probably explain the most intense bands observed at 1344 and 1042 cm-1 in both experimental FT-IR and FT-Raman spectra, respectively. The geometrical parameters suggest monodentate cation-anion coordination while the studies by charges, NBO and AIM calculations support bidentate coordinations between those two species. Additionally several quantum chemical descriptors were also calculated in order to interpret various molecular properties such as electronic structure, reactivity of those species and predict their gas phase behaviours.

Hyaluronidase Inhibitory Activity of Pentacylic Triterpenoids from Prismatomeris tetrandra (Roxb.) K. Schum: Isolation, Synthesis and QSAR Study

PubMed Central

Abdullah, Nor Hayati; Thomas, Noel Francis; Sivasothy, Yasodha; Lee, Vannajan Sanghiran; Liew, Sook Yee; Noorbatcha, Ibrahim Ali; Awang, Khalijah

2016-01-01

The mammalian hyaluronidase degrades hyaluronic acid by the cleavage of the β-1,4-glycosidic bond furnishing a tetrasaccharide molecule as the main product which is a highly angiogenic and potent inducer of inflammatory cytokines. Ursolic acid 1, isolated from Prismatomeris tetrandra, was identified as having the potential to develop inhibitors of hyaluronidase. A series of ursolic acid analogues were either synthesized via structure modification of ursolic acid 1 or commercially obtained. The evaluation of the inhibitory activity of these compounds on the hyaluronidase enzyme was conducted. Several structural, topological and quantum chemical descriptors for these compounds were calculated using semi empirical quantum chemical methods. A quantitative structure activity relationship study (QSAR) was performed to correlate these descriptors with the hyaluronidase inhibitory activity. The statistical characteristics provided by the best multi linear model (BML) (R2 = 0.9717, R2cv = 0.9506) indicated satisfactory stability and predictive ability of the developed model. The in silico molecular docking study which was used to determine the binding interactions revealed that the ursolic acid analog 22 had a strong affinity towards human hyaluronidase. PMID:26907251

Structure-based predictions of 13C-NMR chemical shifts for a series of 2-functionalized 5-(methylsulfonyl)-1-phenyl-1H-indoles derivatives using GA-based MLR method

NASA Astrophysics Data System (ADS)

Ghavami, Raouf; Sadeghi, Faridoon; Rasouli, Zolikha; Djannati, Farhad

2012-12-01

Experimental values for the 13C NMR chemical shifts (ppm, TMS = 0) at 300 K ranging from 96.28 ppm (C4' of indole derivative 17) to 159.93 ppm (C4' of indole derivative 23) relative to deuteride chloroform (CDCl3, 77.0 ppm) or dimethylsulfoxide (DMSO, 39.50 ppm) as internal reference in CDCl3 or DMSO-d6 solutions have been collected from literature for thirty 2-functionalized 5-(methylsulfonyl)-1-phenyl-1H-indole derivatives containing different substituted groups. An effective quantitative structure-property relationship (QSPR) models were built using hybrid method combining genetic algorithm (GA) based on stepwise selection multiple linear regression (SWS-MLR) as feature-selection tools and correlation models between each carbon atom of indole derivative and calculated descriptors. Each compound was depicted by molecular structural descriptors that encode constitutional, topological, geometrical, electrostatic, and quantum chemical features. The accuracy of all developed models were confirmed using different types of internal and external procedures and various statistical tests. Furthermore, the domain of applicability for each model which indicates the area of reliable predictions was defined.

Quantum chemical investigation of levofloxacin-boron complexes: A computational approach

NASA Astrophysics Data System (ADS)

Sayin, Koray; Karakaş, Duran

2018-04-01

Quantum chemical calculations are performed over some boron complexes with levofloxacin. Boron complex with fluorine atoms are optimized at three different methods (HF, B3LYP and M062X) with 6-31 + G(d) basis set. The best level is determined as M062X/6-31 + G(d) by comparison of experimental and calculated results of complex (1). The other complexes are optimized by using the best level. Structural properties, IR and NMR spectrum are examined in detail. Biological activities of mentioned complexes are investigated by some quantum chemical descriptors and molecular docking analyses. As a result, biological activities of complex (2) and (4) are close to each other and higher than those of other complexes. Additionally, NLO properties of mentioned complexes are investigated by some quantum chemical parameters. It is found that complex (3) is the best candidate for NLO applications.

Revealing chemophoric sites in organophosphorus insecticides through the MIA-QSPR modeling of soil sorption data.

PubMed

Daré, Joyce K; Silva, Cristina F; Freitas, Matheus P

2017-10-01

Soil sorption of insecticides employed in agriculture is an important parameter to probe the environmental fate of organic chemicals. Therefore, methods for the prediction of soil sorption of new agrochemical candidates, as well as for the rationalization of the molecular characteristics responsible for a given sorption profile, are extremely beneficial for the environment. A quantitative structure-property relationship method based on chemical structure images as molecular descriptors provided a reliable model for the soil sorption prediction of 24 widely used organophosphorus insecticides. By means of contour maps obtained from the partial least squares regression coefficients and the variable importance in projection scores, key molecular moieties were targeted for possible structural modification, in order to obtain novel and more environmentally friendly insecticide candidates. The image-based descriptors applied encode molecular arrangement, atoms connectivity, groups size, and polarity; consequently, the findings in this work cannot be achieved by a simple relationship with hydrophobicity, usually described by the octanol-water partition coefficient. Copyright © 2017 Elsevier Inc. All rights reserved.

DFT analysis on the molecular structure, vibrational and electronic spectra of 2-(cyclohexylamino)ethanesulfonic acid

NASA Astrophysics Data System (ADS)

Renuga Devi, T. S.; Sharmi kumar, J.; Ramkumaar, G. R.

2015-02-01

The FTIR and FT-Raman spectra of 2-(cyclohexylamino)ethanesulfonic acid were recorded in the regions 4000-400 cm-1 and 4000-50 cm-1 respectively. The structural and spectroscopic data of the molecule in the ground state were calculated using Hartee-Fock and Density functional method (B3LYP) with the correlation consistent-polarized valence double zeta (cc-pVDZ) basis set and 6-311++G(d,p) basis set. The most stable conformer was optimized and the structural and vibrational parameters were determined based on this. The complete assignments were performed based on the Potential Energy Distribution (PED) of the vibrational modes, calculated using Vibrational Energy Distribution Analysis (VEDA) 4 program. With the observed FTIR and FT-Raman data, a complete vibrational assignment and analysis of the fundamental modes of the compound were carried out. Thermodynamic properties and Atomic charges were calculated using both Hartee-Fock and density functional method using the cc-pVDZ basis set and compared. The calculated HOMO-LUMO energy gap revealed that charge transfer occurs within the molecule. 1H and 13C NMR chemical shifts of the molecule were calculated using Gauge Including Atomic Orbital (GIAO) method and were compared with experimental results. Stability of the molecule arising from hyperconjugative interactions, charge delocalization have been analyzed using Natural Bond Orbital (NBO) analysis. The first order hyperpolarizability (β) and Molecular Electrostatic Potential (MEP) of the molecule was computed using DFT calculations. The electron density based local reactivity descriptor such as Fukui functions were calculated to explain the chemical reactivity site in the molecule.

Model based on GRID-derived descriptors for estimating CYP3A4 enzyme stability of potential drug candidates

NASA Astrophysics Data System (ADS)

Crivori, Patrizia; Zamora, Ismael; Speed, Bill; Orrenius, Christian; Poggesi, Italo

2004-03-01

A number of computational approaches are being proposed for an early optimization of ADME (absorption, distribution, metabolism and excretion) properties to increase the success rate in drug discovery. The present study describes the development of an in silico model able to estimate, from the three-dimensional structure of a molecule, the stability of a compound with respect to the human cytochrome P450 (CYP) 3A4 enzyme activity. Stability data were obtained by measuring the amount of unchanged compound remaining after a standardized incubation with human cDNA-expressed CYP3A4. The computational method transforms the three-dimensional molecular interaction fields (MIFs) generated from the molecular structure into descriptors (VolSurf and Almond procedures). The descriptors were correlated to the experimental metabolic stability classes by a partial least squares discriminant procedure. The model was trained using a set of 1800 compounds from the Pharmacia collection and was validated using two test sets: the first one including 825 compounds from the Pharmacia collection and the second one consisting of 20 known drugs. This model correctly predicted 75% of the first and 85% of the second test set and showed a precision above 86% to correctly select metabolically stable compounds. The model appears a valuable tool in the design of virtual libraries to bias the selection toward more stable compounds. Abbreviations: ADME - absorption, distribution, metabolism and excretion; CYP - cytochrome P450; MIFs - molecular interaction fields; HTS - high throughput screening; DDI - drug-drug interactions; 3D - three-dimensional; PCA - principal components analysis; CPCA - consensus principal components analysis; PLS - partial least squares; PLSD - partial least squares discriminant; GRIND - grid independent descriptors; GRID - software originally created and developed by Professor Peter Goodford.

Spectral investigations, DFT based global reactivity descriptors, Inhibition efficiency and analysis of 5-chloro-2-nitroanisole as π-spacer with donor-acceptor variations effect for DSSCs performance

NASA Astrophysics Data System (ADS)

Meenakshi, R.

2017-01-01

FTIR, FT-Raman, UV, NMR and quantum chemical calculation studies are performed on 5-chloro-2-nitroanisole, in order to gain the insights of its structural, spectroscopic and electronic properties (Fukui indices, HOMO and LUMO energy gap, MESP and Global reactivity descriptors). A complete vibrational analysis of 5-chloro-2-nitroanisole is performed by HF/B3LYP methods using 6-31G(d,p) basis set. To estimate the electronic transitions, the UV spectra of title compound are predicted in gas phase and ethanol. The obtained absorption maxima at 389.94 nm (in ethanol) is predicted possibly due to HOMO→LUMO transition with 85% contribution and assigned as π-π*. The MESP map shows that the negative potential sites are localized on oxygen atom (O10) as well as the positive potential sites are identified around the hydrogen and ring carbon atoms. The analysis of Fukui indices is also carried out to distinguish the nucleophilic and electrophiic centers. The prediction of reactive sites by MESP is well supported by this Fukui indices analysis. The correlations between the statistical thermodynamics and temperature are also obtained. It is seen that the heat capacities, entropies and enthalpies increase with increasing the intensities of the molecular vibrations. Furthermore, the first hyperpolarizability of 5-chloro-2-nitroanisole is calculated and the results are discussed. This result indicates that 5-chloro-2-nitroanisole is a good candidate of nonlinear optical materials.

Hybrid optimal descriptors as a tool to predict skin sensitization in accordance to OECD principles.

PubMed

Toropova, Alla P; Toropov, Andrey A

2017-06-05

Skin sensitization (allergic contact dermatitis) is a widespread problem arising from the contact of chemicals with the skin. The detection of molecular features with undesired effect for skin is complex task owing to unclear biochemical mechanisms and unclearness of conditions of action of chemicals to skin. The development of computational methods for estimation of this endpoint in order to reduce animal testing is recommended (Cosmetics Directive EC regulation 1907/2006; EU Regulation, Regulation, 1223/2009). The CORAL software (http://www.insilico.eu/coral) gives good predictive models for the skin sensitization. Simplified molecular input-line entry system (SMILES) together with molecular graph are used to represent the molecular structure for these models. So-called hybrid optimal descriptors are used to establish quantitative structure-activity relationships (QSARs). The aim of this study is the estimation of the predictive potential of the hybrid descriptors. Three different distributions into the training (≈70%), calibration (≈15%), and validation (≈15%) sets are studied. QSAR for these three distributions are built up with using the Monte Carlo technique. The statistical characteristics of these models for external validation set are used as a measure of predictive potential of these models. The best model, according to the above criterion, is characterized by n validation =29, r 2 validation =0.8596, RMSE validation =0.489. Mechanistic interpretation and domain of applicability for these models are defined. Copyright © 2017 Elsevier B.V. All rights reserved.

Electronic forces as descriptors of nucleophilic and electrophilic regioselectivity and stereoselectivity.

PubMed

Liu, Shubin; Rong, Chunying; Lu, Tian

2017-01-04

One of the main tasks of theoretical chemistry is to rationalize computational results with chemical insights. Key concepts of such nature include nucleophilicity, electrophilicity, regioselectivity, and stereoselectivity. While computational tools are available to predict barrier heights and other reactivity properties with acceptable accuracy, a conceptual framework to appreciate above quantities is still lacking. In this work, we introduce the electronic force as the fundamental driving force of chemical processes to understand and predict molecular reactivity. It has three components but only two are independent. These forces, electrostatic and steric, can be employed as reliable descriptors for nucleophilic and electrophilic regioselectivity and stereoselectivity. The advantages of using these forces to evaluate molecular reactivity are that electrophilic and nucleophilic attacks are featured by distinct characteristics in the electrostatic force and no knowledge of quantum effects included in the kinetic and exchange-correlation energies is required. Examples are provided to highlight the validity and general applicability of these reactivity descriptors. Possible applications in ambident reactivity, σ and π holes, frustrated Lewis pairs, and stereoselective reactions are also included in this work.

QM/MM methodology, docking and spectroscopic (FT-IR/FT-Raman, NMR, UV) and Fukui function analysis on adrenergic agonist

NASA Astrophysics Data System (ADS)

Uma Maheswari, J.; Muthu, S.; Sundius, Tom

2015-02-01

The Fourier transform infrared, FT-Raman, UV and NMR spectra of Ternelin have been recorded and analyzed. Harmonic vibrational frequencies have been investigated with the help of HF with 6-31G (d,p) and B3LYP with 6-31G (d,p) and LANL2DZ basis sets. The 1H and 13C nuclear magnetic resonance (NMR) chemical shifts of the molecule were calculated by GIAO method. The polarizability (α) and the first hyperpolarizability (β) values of the investigated molecule have been computed using DFT quantum mechanical calculations. Stability of the molecule arising from hyper conjugative interactions, and charge delocalization has been analyzed using natural bond orbital (NBO) analysis. The electron density-based local reactivity descriptors such as Fukui functions were calculated to explain the chemical selectivity or reactivity site in Ternelin. Finally the calculated results were compared to simulated infrared and Raman spectra of the title compound which show good agreement with observed spectra. Molecular docking studies have been carried out in the active site of Ternelin and reactivity with ONIOM was also investigated.

Is electronegativity a useful descriptor for the pseudo-alkali metal NH4?

PubMed

Whiteside, Alexander; Xantheas, Sotiris S; Gutowski, Maciej

2011-11-18

Molecular ions in the form of "pseudo-atoms" are common structural motifs in chemistry, with properties that are transferrable between different compounds. We have determined one such property--the electronegativity--for the "pseudo-alkali metal" ammonium (NH(4)), and evaluated its reliability as a descriptor versus the electronegativities of the alkali metals. The computed properties of ammonium's binary complexes with astatine and of selected borohydrides confirm the similarity of NH(4) to the alkali metal atoms, although the electronegativity of NH(4) is relatively large in comparison to its cationic radius. We have paid particular attention to the molecular properties of ammonium (angular anisotropy, geometric relaxation and reactivity), which can cause deviations from the behaviour expected of a conceptual "true alkali metal" with this electronegativity. These deviations allow for the discrimination of effects associated with the molecular nature of NH(4). Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Quantitative prediction of solvation free energy in octanol of organic compounds.

PubMed

Delgado, Eduardo J; Jaña, Gonzalo A

2009-03-01

The free energy of solvation, DeltaGS0, in octanol of organic compounds is quantitatively predicted from the molecular structure. The model, involving only three molecular descriptors, is obtained by multiple linear regression analysis from a data set of 147 compounds containing diverse organic functions, namely, halogenated and non-halogenated alkanes, alkenes, alkynes, aromatics, alcohols, aldehydes, ketones, amines, ethers and esters; covering a DeltaGS0 range from about -50 to 0 kJ.mol(-1). The model predicts the free energy of solvation with a squared correlation coefficient of 0.93 and a standard deviation, 2.4 kJ.mol(-1), just marginally larger than the generally accepted value of experimental uncertainty. The involved molecular descriptors have definite physical meaning corresponding to the different intermolecular interactions occurring in the bulk liquid phase. The model is validated with an external set of 36 compounds not included in the training set.

Quantitative Prediction of Solvation Free Energy in Octanol of Organic Compounds

PubMed Central

Delgado, Eduardo J.; Jaña, Gonzalo A.

2009-01-01

The free energy of solvation, ΔGS0, in octanol of organic compunds is quantitatively predicted from the molecular structure. The model, involving only three molecular descriptors, is obtained by multiple linear regression analysis from a data set of 147 compounds containing diverse organic functions, namely, halogenated and non-halogenated alkanes, alkenes, alkynes, aromatics, alcohols, aldehydes, ketones, amines, ethers and esters; covering a ΔGS0 range from about −50 to 0 kJ·mol−1. The model predicts the free energy of solvation with a squared correlation coefficient of 0.93 and a standard deviation, 2.4 kJ·mol−1, just marginally larger than the generally accepted value of experimental uncertainty. The involved molecular descriptors have definite physical meaning corresponding to the different intermolecular interactions occurring in the bulk liquid phase. The model is validated with an external set of 36 compounds not included in the training set. PMID:19399236

Synthesis, spectroscopic analyses, chemical reactivity and molecular docking study and anti-tubercular activity of pyrazine and condensed oxadiazole derivatives

NASA Astrophysics Data System (ADS)

Al-Tamimi, Abdul-Malek S.; Mary, Y. Sheena; Miniyar, Pankaj B.; Al-Wahaibi, Lamya H.; El-Emam, Ali A.; Armaković, Stevan; Armaković, Sanja J.

2018-07-01

The FT-IR spectral analysis and theoretical calculations of the wavenumbers of three oxadiazole derivatives, 2-(5-(2-chlorophenyl)-1,3,4-oxadiazol-2-yl)pyrazine (ORTHOPHPZ), 2-(5-(3-chlorophenyl)-1,3,4-oxadiazol-2-yl)pyrazine (METAPHPZ) and 2-(5-(4-chlorophenyl)-1,3,4-oxadiazol-2-yl)pyrazine (PARAPHPZ) were reported in the present work. The theoretically predicted values of polarizability give the nonlinear behaviour of the compounds. The frontier molecular orbital analysis show the chemical stability of the title compounds and the NBO analysis gives the interactions in the molecular systems. Understanding of reactivity of newly synthetiszed oxadiazole derivatives in this study has been achieved thanks to combination of density functional theory (DFT) calculations, molecular dynamics (MD) simulations and molecular docking procedures. New oxadiazole derivatives have also been characterized experimentally through FT-IR and NMR approaches, thanks to which detailed structural properties have been understood. Both global and local reactivity properties have been investigated by calculations of quantum molecular descriptors such as molecular electrostatic potential (MEP), local average ionization energy (ALIE), Fukui functions, bond dissociation energies for hydrogen abstraction (H-BDE), radial distribution functions and binding energies of ligand against selected protein. The first hyperpolarizabilities of ORTHOPHPZ, METAPHPZ and PARAPHPZ are respectively, 84.62, 94.71 and 184.10 times that of urea. The docked ligands form stable complexes with the receptor 1-phosphatidylinositol phosphodiesterase and the results suggest that these compounds can be developed as new anti-cancer drugs. The anti-TB activity of PM series against M. tuberculosis H37RV strain was performed by Middlebrooke 7H-9 method. The compounds, ORTHOPHPZ, METAPHPZ and PARAPHPZ were moderately active between 25 and 50 μg/ml concentration as compared with the standard anti-TB agents and the -log MIC activity was found in the range of 1.011-1.274 as compared with isoniazid (INH) (1.137) and pyrazinamide (PZA) (1.115) standard anti-TB agents.

One pot synthesis of Curcumin-NSAIDs prodrug, spectroscopic characterization, conformational analysis, chemical reactivity, intramolecular interactions and first order hyperpolarizability by DFT method

NASA Astrophysics Data System (ADS)

Srivastava, Sangeeta; Gupta, Preeti; Sethi, Arun; Singh, Ranvijay Pratap

2016-08-01

A novel Curcumin-NSAIDs prodrug 4-((1E, 3Z, 6E)-3-hydroxy-(4-hydroxy-3-methoxyphenyl)-5-oxohepta-1,3,3-trienyl)-2-methoxyphenyl-2-(4-isobutylphenyl) propanoate (2) derivative was synthesized by Steglich esterification in high yield and characterized with the help of 1H, 13C NMR, 1H-1H COSY, UV, FT-IR spectroscopy and mass spectrometry. The molecular geometry of synthesized compound was calculated in ground state by Density functional theory (DFT/B3LYP) using two different basis set 6-31G (d, p) and 6-311G (d, p). Conformational analysis of 2 was carried out to determine the most stable conformation. Stability of the molecule as a result of hyperconjugative interactions and electron delocalization were analysed using Natural bond orbital (NBO) analysis. Intramolecular interactions were analysed by AIM (Atom in molecule) approach. Global and local reactivity descriptors were calculated to study the reactive site within molecule. The electronic properties such as HOMO and LUMO energies were calculated using time dependent Density Functional Theory (TD-DFT). The vibrational wavenumbers were calculated using DFT method and assigned with the help of potential energy distribution (PED). First hyperpolarizability value has been calculated to describe the nonlinear optical (NLO) property of the synthesized compound. Molecular electrostatic potential (MEP) for synthesized compounds have also been determined to check their electrophilic or nucleophilic reactivity.

Quantitative Structure-Cytotoxicity Relationship of Oleoylamides.

PubMed

Sakagami, Hiroshi; Uesawa, Yoshihiro; Ishihara, Mariko; Kagaya, Hajime; Kanamoto, Taisei; Terakubo, Shigemi; Nakashima, Hideki; Takao, Koichi; Sugita, Yoshiaki

2015-10-01

Eighteen oleoylamides were subjected to quantitative structure-activity relationship analysis based on their cytotoxicity, tumor selectivity and anti-HIV activity, in order to assess their biological activities. Cytotoxicity against four human oral squamous cell carcinoma (OSCC) cell lines and five human oral normal cells (gingival fibroblast, periodontal ligament fibroblast, pulp cell, oral keratinocyte, primary gingival epithelial cells) was determined by the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) method. Tumor-selectivity (TS) was evaluated by the ratio of the mean 50% cytotoxic concentration (CC50) against normal human oral cells to that against OSCC cell lines. Potency-selectivity expression (PSE) was determined by the ratio of TS to CC50 against OSCC. Anti-HIV activity was evaluated by the ratio of CC50 to the concentration leading to 50% cytoprotection from HIV infection (EC50). Physicochemical, structural and quantum-chemical parameters were calculated based on the conformations optimized by the LowModeMD method. Among 18 derivatives, compounds 8: with a catechol group) and 18: with a (2-pyridyl)amino group) had the highest TS. On the other hand, doxorubicin and 5-fluorouracil (5-FU) were more highly cytotoxic to normal epithelial cells, displaying unexpectedly lower TS and PSE values. None of the compounds had anti-HIV activity. Among 330 chemical descriptors, 75, 73 and 19 descriptors significantly correlated to the cytotoxicity to normal and tumor cells, and TS, respectively. Multivariate statistics with chemical descriptors for molecular polarization and hydrophobicity may be useful for the evaluation of cytotoxicity and TS of oleoylamides. Copyright© 2015 International Institute of Anticancer Research (Dr. John G. Delinassios), All rights reserved.

Modeling the drugs' passive transfer in the body based on their chromatographic behavior.

PubMed

Kouskoura, Maria G; Kachrimanis, Kyriakos G; Markopoulou, Catherine K

2014-11-01

One of the most challenging aims in modern analytical chemistry and pharmaceutical analysis is to create models for drugs' behavior based on simulation experiments. Since drugs' effects are closely related to their molecular properties, numerous characteristics of drugs are used in order to acquire a model of passive absorption and transfer in the human body. Importantly, such direction in innovative bioanalytical methodologies is also of stressful need in the area of personalized medicine to implement nanotechnological and genomics advancements. Simulation experiments were carried out by examining and interpreting the chromatographic behavior of 113 analytes/drugs (400 observations) in RP-HPLC. The dataset employed for this purpose included 73 descriptors which are referring to the physicochemical properties of the mobile phase mixture in different proportions, the physicochemical properties of the analytes and the structural characteristics of their molecules. A series of different software packages was used to calculate all the descriptors apart from those referring to the structure of analytes. The correlation of the descriptors with the retention time of the analytes eluted from a C4 column with an aqueous mobile phase was employed as dataset to introduce the behavior models in the human body. Their evaluation with a Partial Least Squares (PLS) software proved that the chromatographic behavior of a drug on a lipophilic stationary and a polar mobile phase is directly related to its drug-ability. At the same time, the behavior of an unknown drug in the human body can be predicted with reliability via the Artificial Neural Networks (ANNs) software. Copyright © 2014 Elsevier B.V. All rights reserved.

Using open source computational tools for predicting human metabolic stability and additional absorption, distribution, metabolism, excretion, and toxicity properties.

PubMed

Gupta, Rishi R; Gifford, Eric M; Liston, Ted; Waller, Chris L; Hohman, Moses; Bunin, Barry A; Ekins, Sean

2010-11-01

Ligand-based computational models could be more readily shared between researchers and organizations if they were generated with open source molecular descriptors [e.g., chemistry development kit (CDK)] and modeling algorithms, because this would negate the requirement for proprietary commercial software. We initially evaluated open source descriptors and model building algorithms using a training set of approximately 50,000 molecules and a test set of approximately 25,000 molecules with human liver microsomal metabolic stability data. A C5.0 decision tree model demonstrated that CDK descriptors together with a set of Smiles Arbitrary Target Specification (SMARTS) keys had good statistics [κ = 0.43, sensitivity = 0.57, specificity = 0.91, and positive predicted value (PPV) = 0.64], equivalent to those of models built with commercial Molecular Operating Environment 2D (MOE2D) and the same set of SMARTS keys (κ = 0.43, sensitivity = 0.58, specificity = 0.91, and PPV = 0.63). Extending the dataset to ∼193,000 molecules and generating a continuous model using Cubist with a combination of CDK and SMARTS keys or MOE2D and SMARTS keys confirmed this observation. When the continuous predictions and actual values were binned to get a categorical score we observed a similar κ statistic (0.42). The same combination of descriptor set and modeling method was applied to passive permeability and P-glycoprotein efflux data with similar model testing statistics. In summary, open source tools demonstrated predictive results comparable to those of commercial software with attendant cost savings. We discuss the advantages and disadvantages of open source descriptors and the opportunity for their use as a tool for organizations to share data precompetitively, avoiding repetition and assisting drug discovery.

Synthesis, crystal structure analysis, spectral investigations, DFT computations and molecular dynamics and docking study of 4-benzyl-5-oxomorpholine-3-carbamide, a potential bioactive agent

NASA Astrophysics Data System (ADS)

Murthy, P. Krishna; Sheena Mary, Y.; Shyma Mary, Y.; Panicker, C. Yohannan; Suneetha, V.; Armaković, Stevan; Armaković, Sanja J.; Van Alsenoy, C.; Suchetan, P. A.

2017-04-01

4-benzyl-5-oxomorpholine-3-carbamide has been synthesized; single crystals were grown by slow evaporation solution growth technique at room temperature and characterized by single crystal X-ray diffraction, FT-IR, FT-Raman and 1H-NMR. The compound crystallizes in the monoclinic space group P21/n. The molecular geometry of the compound was optimized by using Density Functional Theory (DFT/B3LYP) method with 6-311++G(d,p) basis set in the ground state and geometric parameters are in agreement with the X-ray analysis results of the structure. The experimental vibrational spectra were compared with the calculated spectra and each vibrational wave number was assigned on the basis of potential energy distribution (PED). The electronic and charge transfer properties have been explained on the basis of highest occupied molecular orbital's (HOMOs) and lowest unoccupied molecular orbital's (LUMOs). Besides molecular electrostatic potential (MEP), frontier molecular orbital's (FMOs), some global reactivity descriptors, thermodynamic properties, non-linear optical (NLO) behavior and Mullikan charge analysis of the title compound were computed with the same method in gas phase, theoretically. Potential reactive sites of the title compound have been identified by average local ionization energy and Fukui functions, both mapped to the electron density surface. Bond dissociation energies for all single acyclic bonds have been calculated in order to investigate autoxidation and degradation properties of the title compound. Atoms with pronounced interactions with water molecules have been detected by calculations of radial distribution functions after molecular dynamics simulations. The experimental results are compared with the theoretical calculations using DFT methods for the fortification of the paper. Further the docking studies revealed that the title compound as a docked ligand forms a stable complex with pyrrole inhibitor with a binding affinity value of -7.5 kcal/mol. This suggests that the title compound might exhibit inhibitory activity against pyrrole inhibitor. To confirm the potential practical applicability of the title compound antimicrobial activity was tested against gram negative and gram positive bacteria.

Information origins of the chemical bond: Bond descriptors from molecular communication channels in orbital resolution

NASA Astrophysics Data System (ADS)

Nalewajski, Roman F.

The flow of information in the molecular communication networks in the (condensed) atomic orbital (AO) resolution is investigated and the plane-wave (momentum-space) interpretation of the average Fisher information in the molecular information system is given. It is argued using the quantum-mechanical superposition principle that, in the LCAO MO theory the squares of corresponding elements of the Charge and Bond-Order (CBO) matrix determine the conditional probabilities between AO, which generate the molecular communication system of the Orbital Communication Theory (OCT) of the chemical bond. The conditional-entropy ("noise," information-theoretic "covalency") and the mutual-information (information flow, information-theoretic "ionicity") descriptors of these molecular channels are related to Wiberg's covalency indices of chemical bonds. The illustrative application of OCT to the three-orbital model of the chemical bond X-Y, which is capable of describing the forward- and back-donations as well as the atom promotion accompanying the bond formation, is reported. It is demonstrated that the entropy/information characteristics of these separate bond-effects can be extracted by an appropriate reduction of the output of the molecular information channel, carried out by combining several exits into a single (condensed) one. The molecular channels in both the AO and hybrid orbital representations are examined for both the molecular and representative promolecular input probabilities.

Quantitative structure-activity relationship and molecular docking of artemisinin derivatives to vascular endothelial growth factor receptor 1.

PubMed

Saeed, Mohamed E M; Kadioglu, Onat; Seo, Ean-Jeong; Greten, Henry Johannes; Brenk, Ruth; Efferth, Thomas

2015-04-01

The antimalarial drug artemisinin has been shown to exert anticancer activity through anti-angiogenic effects. For further drug development, it may be useful to have derivatives with improved anti-angiogenic properties. We performed molecular docking of 52 artemisinin derivatives to vascular endothelial growth factor receptors (VEGFR1, VEGFR2), and VEGFA ligand using Autodock4 and AutodockTools-1.5.7.rc1 using the Lamarckian genetic algorithm. Quantitative structure-activity relationship (QSAR) analyses of the compounds prepared by Corina Molecular Networks were performed using the Molecular Operating Environment MOE 2012.10. A statistically significant inverse relationship was obtained between in silico binding energies to VEGFR1 and anti-angiogenic activity in vivo of a test-set of artemisinin derivatives (R=-0.843; p=0.035). This served as a control experiment to validate molecular docking predicting anti-angiogenc effects. Furthermore, 52 artemisinin derivatives were docked to VEGFR1 and in selected examples also to VEGFR2 and VEGFA. Higher binding affinities were calculated for receptors than for the ligand. The best binding affinities to VEGFR1 were found for an artemisinin dimer, 10-dihydroartemisinyl-2-propylpentanoate, and dihydroartemisinin α-hemisuccinate sodium salt. QSAR analyses revealed significant relationships between VEGFR1 binding energies and defined molecular descriptors of 35 artemisinins assigned to the training set (R=0.0848, p<0.0001) and 17 derivatives assigned to the test set (R=0.761, p<0.001). Molecular docking and QSAR calculations can be used to identify novel artemisinin derivatives with anti-angiogenic effects. Copyright© 2015 International Institute of Anticancer Research (Dr. John G. Delinassios), All rights reserved.

Dual descriptors within the framework of spin-polarized density functional theory.

PubMed

Chamorro, E; Pérez, P; Duque, M; De Proft, F; Geerlings, P

2008-08-14

Spin-polarized density functional theory (SP-DFT) allows both the analysis of charge-transfer (e.g., electrophilic and nucleophilic reactivity) and of spin-polarization processes (e.g., photophysical changes arising from electron transitions). In analogy with the dual descriptor introduced by Morell et al. [J. Phys. Chem. A 109, 205 (2005)], we introduce new dual descriptors intended to simultaneously give information of the molecular regions where the spin-polarization process linking states of different multiplicity will drive electron density and spin density changes. The electronic charge and spin rearrangement in the spin forbidden radiative transitions S(0)-->T(n,pi(*)) and S(0)-->T(pi,pi(*)) in formaldehyde and ethylene, respectively, have been used as benchmark examples illustrating the usefulness of the new spin-polarization dual descriptors. These quantities indicate those regions where spin-orbit coupling effects are at work in such processes. Additionally, the qualitative relationship between the topology of the spin-polarization dual descriptors and the vertical singlet triplet energy gap in simple substituted carbene series has been also discussed. It is shown that the electron density and spin density rearrangements arise in agreement with spectroscopic experimental evidence and other theoretical results on the selected target systems.

Salient aspects of PBP2A-inhibition; A QSAR Study.

PubMed

Ogunleye, Adewale J; Eniafe, Gabriel O; Inyang, Olumide K; Adewumi, Benjamin; Omotuyi, Olaposi I

2018-05-15

Backgound: Inhibition of penicillin binding protein 2A (PBP2A) represents a sound drug design strategy in combatting Methicillin resistant Staphylococcus aureus (MRSA). Considering the urgent need for effective antimicrobials in combatting MRSA infections, we have developed a statistically robust ensemble of molecular descriptors (1, 2, & 3-D) from compounds targeting PBP2A in vivo. 37 (training set: 26, test set: 11) PBP2A-inhibitors were submitted for descriptor generation after which an unsupervised, non-exhaustive genetic algorithm (GA) was deployed for fishing out the best descriptor subset. Assignment of descriptors to a regression model was accomplished with the Partial Least Square (PLS) algorithm. At the end, an ensemble of 30 descriptors accurately predicted the ligand bioactivity, IC50 (R = 0.9996, R2 = 0.9992, R2a = 0.9949, SEE =, 0.2297 Q2LOO = 0.9741). Inferentially, we noticed that the overall efficacy of this model greatly depends on atomic polarizability and negative charge (electron) density. Besides the formula derived, the high dimensional model also offers critical insights into salient cheminformatics parameter to note during hit-to-lead PBP2A-antagonist optimization. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Maximal Predictability Approach for Identifying the Right Descriptors for Electrocatalytic Reactions.

PubMed

Krishnamurthy, Dilip; Sumaria, Vaidish; Viswanathan, Venkatasubramanian

2018-02-01

Density functional theory (DFT) calculations are being routinely used to identify new material candidates that approach activity near fundamental limits imposed by thermodynamics or scaling relations. DFT calculations are associated with inherent uncertainty, which limits the ability to delineate materials (distinguishability) that possess high activity. Development of error-estimation capabilities in DFT has enabled uncertainty propagation through activity-prediction models. In this work, we demonstrate an approach to propagating uncertainty through thermodynamic activity models leading to a probability distribution of the computed activity and thereby its expectation value. A new metric, prediction efficiency, is defined, which provides a quantitative measure of the ability to distinguish activity of materials and can be used to identify the optimal descriptor(s) ΔG opt . We demonstrate the framework for four important electrochemical reactions: hydrogen evolution, chlorine evolution, oxygen reduction and oxygen evolution. Future studies could utilize expected activity and prediction efficiency to significantly improve the prediction accuracy of highly active material candidates.

A complete computational and spectroscopic study of 2-bromo-1, 4-dichlorobenzene - A frequently used benzene derivative

NASA Astrophysics Data System (ADS)

Vennila, P.; Govindaraju, M.; Venkatesh, G.; Kamal, C.; Mary, Y. Sheena; Panicker, C. Yohannan; Kaya, S.; Armaković, Stevan; Armaković, Sanja J.

2018-01-01

The coupled experimental and theoretical vibrational investigation of 2-bromo-1, 4-dichlorobenzene (BDB) molecule has been carried out and they have been duly compared with standard values in order to produce the reliability of the results. Results of DFT analysis carried out using B3LYP functional with 6-31 + G/6-311++G (d,p) basis set revealed that BDB has higher electronic density. The molecular geometry, 13C &1H Nuclear Magnetic Resonance (NMR), Natural Bond Orbital (NBO) and Natural Atomic Charge analyses have been obtained by DFT calculations. Nonlinear optical (NLO) properties, quantum chemical descriptors and first order hyperpolarizability have been calculated. In addition, Local reactivity properties reflected through average local ionization energies (ALIE), Fukui functions and bond dissociation energies have also been investigated. Besides investigation of docking properties, molecular dynamics simulations were also taken in account with a view to identify atoms that have relatively important interactions with water molecules. The title compound forms a stable complex with isopentenylpyrophosphate transferase with a binding affinity value as -4.6 kCal./Mol. and shows inhibitory activity against isopentenylpyrophosphate transferase.

Use of Biodescriptors and Chemodescriptors in Predictive Toxicology: A Mathematical/Computational Approach

DTIC Science & Technology

2005-01-01

proteomic gel analyses. The research group has explored the use of chemodescriptors calculated using high-level ab initio quantum chemical basis sets...descriptors that characterize the entire proteomics map, local descriptors that characterize a subset of the proteins present in the gel, and spectrum...techniques for analyzing the full set of proteins present in a proteomics map. 14. SUBJECT TERMS 1S. NUMBER OF PAGES Topological indices

High-throughput screening for thermoelectric sulphides by using crystal structure features as descriptors

NASA Astrophysics Data System (ADS)

Zhang, Ruizhi; Du, Baoli; Chen, Kan; Reece, Mike; Materials Research Insititute Team

With the increasing computational power and reliable databases, high-throughput screening is playing a more and more important role in the search of new thermoelectric materials. Rather than the well established density functional theory (DFT) calculation based methods, we propose an alternative approach to screen for new TE materials: using crystal structural features as 'descriptors'. We show that a non-distorted transition metal sulphide polyhedral network can be a good descriptor for high power factor according to crystal filed theory. By using Cu/S containing compounds as an example, 1600+ Cu/S containing entries in the Inorganic Crystal Structure Database (ICSD) were screened, and of those 84 phases are identified as promising thermoelectric materials. The screening results are validated by both electronic structure calculations and experimental results from the literature. We also fabricated some new compounds to test our screening results. Another advantage of using crystal structure features as descriptors is that we can easily establish structural relationships between the identified phases. Based on this, two material design approaches are discussed: 1) High-pressure synthesis of metastable phase; 2) In-situ 2-phase composites with coherent interface. This work was supported by a Marie Curie International Incoming Fellowship of the European Community Human Potential Program.

Artificial intelligence systems based on texture descriptors for vaccine development.

PubMed

Nanni, Loris; Brahnam, Sheryl; Lumini, Alessandra

2011-02-01

The aim of this work is to analyze and compare several feature extraction methods for peptide classification that are based on the calculation of texture descriptors starting from a matrix representation of the peptide. This texture-based representation of the peptide is then used to train a support vector machine classifier. In our experiments, the best results are obtained using local binary patterns variants and the discrete cosine transform with selected coefficients. These results are better than those previously reported that employed texture descriptors for peptide representation. In addition, we perform experiments that combine standard approaches based on amino acid sequence. The experimental section reports several tests performed on a vaccine dataset for the prediction of peptides that bind human leukocyte antigens and on a human immunodeficiency virus (HIV-1). Experimental results confirm the usefulness of our novel descriptors. The matlab implementation of our approaches is available at http://bias.csr.unibo.it/nanni/TexturePeptide.zip.

Local Descriptors of Dynamic and Nondynamic Correlation.

PubMed

Ramos-Cordoba, Eloy; Matito, Eduard

2017-06-13

Quantitatively accurate electronic structure calculations rely on the proper description of electron correlation. A judicious choice of the approximate quantum chemistry method depends upon the importance of dynamic and nondynamic correlation, which is usually assesed by scalar measures. Existing measures of electron correlation do not consider separately the regions of the Cartesian space where dynamic or nondynamic correlation are most important. We introduce real-space descriptors of dynamic and nondynamic electron correlation that admit orbital decomposition. Integration of the local descriptors yields global numbers that can be used to quantify dynamic and nondynamic correlation. Illustrative examples over different chemical systems with varying electron correlation regimes are used to demonstrate the capabilities of the local descriptors. Since the expressions only require orbitals and occupation numbers, they can be readily applied in the context of local correlation methods, hybrid methods, density matrix functional theory, and fractional-occupancy density functional theory.

Vibrational and structural study of onopordopicrin based on the FTIR spectrum and DFT calculations.

PubMed

Chain, Fernando E; Romano, Elida; Leyton, Patricio; Paipa, Carolina; Catalán, César A N; Fortuna, Mario; Brandán, Silvia Antonia

2015-01-01

In the present work, the structural and vibrational properties of the sesquiterpene lactone onopordopicrin (OP) were studied by using infrared spectroscopy and density functional theory (DFT) calculations together with the 6-31G(∗) basis set. The harmonic vibrational wavenumbers for the optimized geometry were calculated at the same level of theory. The complete assignment of the observed bands in the infrared spectrum was performed by combining the DFT calculations with Pulay's scaled quantum mechanical force field (SQMFF) methodology. The comparison between the theoretical and experimental infrared spectrum demonstrated good agreement. Then, the results were used to predict the Raman spectrum. Additionally, the structural properties of OP, such as atomic charges, bond orders, molecular electrostatic potentials, characteristics of electronic delocalization and topological properties of the electronic charge density were evaluated by natural bond orbital (NBO), atoms in molecules (AIM) and frontier orbitals studies. The calculated energy band gap and the chemical potential (μ), electronegativity (χ), global hardness (η), global softness (S) and global electrophilicity index (ω) descriptors predicted for OP low reactivity, higher stability and lower electrophilicity index as compared with the sesquiterpene lactone cnicin containing similar rings. Copyright © 2015 Elsevier B.V. All rights reserved.

Introducing a new bond reactivity index: Philicities for natural bond orbitals.

PubMed

Sánchez-Márquez, Jesús; Zorrilla, David; García, Víctor; Fernández, Manuel

2017-12-22

In the present work, a new methodology defined for obtaining reactivity indices (philicities) is proposed. This is based on reactivity functions such as the Fukui function or the dual descriptor, and makes it possible to project the information from reactivity functions onto molecular orbitals, instead of onto the atoms of the molecule (atomic reactivity indices). The methodology focuses on the molecules' natural bond orbitals (bond reactivity indices) because these orbitals have the advantage of being localized, allowing the reaction site of an electrophile or nucleophile to be determined within a very precise molecular region. This methodology provides a "philicity" index for every NBO, and a representative set of molecules has been used to test the new definition. A new methodology has also been developed to compare the "finite difference" and the "frontier molecular orbital" approximations. To facilitate their use, the proposed methodology as well as the possibility of calculating the new indices have been implemented in a new version of UCA-FUKUI software. In addition, condensation schemes based on atomic populations of the "atoms in molecules" theory, the Hirshfeld population analysis, the approximation of Mulliken (with a minimal basis set) and electrostatic potential-derived charges have also been implemented, including the calculation of "bond reactivity indices" defined in previous studies. Graphical abstract A new methodology defined for obtaining bond reactivity indices (philicities) is proposed and makes it possible to project the information from reactivity functions onto molecular orbitals. The proposed methodology as well as the possibility of calculating the new indices have been implemented in a new version of UCA-FUKUI software. In addition, this version can use new atomic condensation schemes and new "utilities" have also been included in this second version.

A theoretical and experimental study on isonitrosoacetophenone nicotinoyl hydrazone: Crystal structure, spectroscopic properties, NBO, NPA and NLMO analyses and the investigation of interaction with some transition metals

NASA Astrophysics Data System (ADS)

Zülfikaroğlu, Ayşin; Batı, Hümeyra; Dege, Necmi

2018-06-01

A new hydrazone oxime compound, isonitrosoacetophenone nicotinoyl hydrazone (inapNH2), was synthesized and characterized by spectroscopic techniques (FT-IR, 1H-NMR and 13C-NMR) and single-crystal X-ray diffraction. The molecular geometry, NMR chemical shift values and vibrational frequencies of the inapNH2 in the ground state have been calculated by using the Density Functional Method (DFT/B3LYP) with 6-31G(d) and 6-311++G(d,p) basis sets. The computational results obtained were in agreement with the experimental results. The thermodynamic parameters of the inapNH2 were calculated at different temperatures, and the changes in thermodynamic properties were studied with increasing temperature. The molecular stability originating from charge transfer and hyperconjugative interactions in the title compound was analyzed using Natural Bond Orbital (NBO) and Natural Localized Molecular Orbital (NLMO) analyzes. The Natural Population Analysis (NPA) charges obtained from NBO analysis were used in order to find out the possible coordination modes of the inapNH2 compound with metal ions. To predict the chemical reactivity of the molecule, the molecular electrostatic potential (MEP) surface map of inapNH2 was investigated and some of its global reactivity descriptors (chemical potential μ, electronegativity χ, hardness η and electrophilicity index ω) were calculated using DFT. Furthermore, the strength of metal-ligand interaction between chlorides of Co(II), Ni(II), Cu(II), Zn(II) and inapNH2, in both aqueous and ethanol phases, was elucidated by using the values of Charge Transfer (ΔN) and Energy Lowering (ΔE). The results indicated that the best interaction in both solvents is between CuCl2 and inapNH2.

Artificial Intelligence Methods Applied to Parameter Detection of Atrial Fibrillation

NASA Astrophysics Data System (ADS)

Arotaritei, D.; Rotariu, C.

2015-09-01

In this paper we present a novel method to develop an atrial fibrillation (AF) based on statistical descriptors and hybrid neuro-fuzzy and crisp system. The inference of system produce rules of type if-then-else that care extracted to construct a binary decision system: normal of atrial fibrillation. We use TPR (Turning Point Ratio), SE (Shannon Entropy) and RMSSD (Root Mean Square of Successive Differences) along with a new descriptor, Teager- Kaiser energy, in order to improve the accuracy of detection. The descriptors are calculated over a sliding window that produce very large number of vectors (massive dataset) used by classifier. The length of window is a crisp descriptor meanwhile the rest of descriptors are interval-valued type. The parameters of hybrid system are adapted using Genetic Algorithm (GA) algorithm with fitness single objective target: highest values for sensibility and sensitivity. The rules are extracted and they are part of the decision system. The proposed method was tested using the Physionet MIT-BIH Atrial Fibrillation Database and the experimental results revealed a good accuracy of AF detection in terms of sensitivity and specificity (above 90%).

The quantitative structure-insecticidal activity relationships from plant derived compounds against chikungunya and zika Aedes aegypti (Diptera:Culicidae) vector.

PubMed

Saavedra, Laura M; Romanelli, Gustavo P; Rozo, Ciro E; Duchowicz, Pablo R

2018-01-01

The insecticidal activity of a series of 62 plant derived molecules against the chikungunya, dengue and zika vector, the Aedes aegypti (Diptera:Culicidae) mosquito, is subjected to a Quantitative Structure-Activity Relationships (QSAR) analysis. The Replacement Method (RM) variable subset selection technique based on Multivariable Linear Regression (MLR) proves to be successful for exploring 4885 molecular descriptors calculated with Dragon 6. The predictive capability of the obtained models is confirmed through an external test set of compounds, Leave-One-Out (LOO) cross-validation and Y-Randomization. The present study constitutes a first necessary computational step for designing less toxic insecticides. Copyright © 2017 Elsevier B.V. All rights reserved.

Feature extraction and descriptor calculation methods for automatic georeferencing of Philippines' first microsatellite imagery

NASA Astrophysics Data System (ADS)

Tupas, M. E. A.; Dasallas, J. A.; Jiao, B. J. D.; Magallon, B. J. P.; Sempio, J. N. H.; Ramos, M. K. F.; Aranas, R. K. D.; Tamondong, A. M.

2017-10-01

The FAST-SIFT corner detector and descriptor extractor combination was used to automatically georeference DIWATA-1 Spaceborne Multispectral Imager images. Features from the Fast Accelerated Segment Test (FAST) algorithm detects corners or keypoints in an image, and these robustly detected keypoints have well-defined positions. Descriptors were computed using Scale-Invariant Feature Transform (SIFT) extractor. FAST-SIFT method effectively SMI same-subscene images detected by the NIR sensor. The method was also tested in stitching NIR images with varying subscene swept by the camera. The slave images were matched to the master image. The keypoints served as the ground control points. Random sample consensus was used to eliminate fall-out matches and ensure accuracy of the feature points from which the transformation parameters were derived. Keypoints are matched based on their descriptor vector. Nearest-neighbor matching is employed based on a metric distance between the descriptors. The metrics include Euclidean and city block, among others. Rough matching outputs not only the correct matches but also the faulty matches. A previous work in automatic georeferencing incorporates a geometric restriction. In this work, we applied a simplified version of the learning method. RANSAC was used to eliminate fall-out matches and ensure accuracy of the feature points. This method identifies if a point fits the transformation function and returns inlier matches. The transformation matrix was solved by Affine, Projective, and Polynomial models. The accuracy of the automatic georeferencing method were determined by calculating the RMSE of interest points, selected randomly, between the master image and transformed slave image.

Quantitative structure-activation barrier relationship modeling for Diels-Alder ligations utilizing quantum chemical structural descriptors.

PubMed

Nandi, Sisir; Monesi, Alessandro; Drgan, Viktor; Merzel, Franci; Novič, Marjana

2013-10-30

In the present study, we show the correlation of quantum chemical structural descriptors with the activation barriers of the Diels-Alder ligations. A set of 72 non-catalysed Diels-Alder reactions were subjected to quantitative structure-activation barrier relationship (QSABR) under the framework of theoretical quantum chemical descriptors calculated solely from the structures of diene and dienophile reactants. Experimental activation barrier data were obtained from literature. Descriptors were computed using Hartree-Fock theory using 6-31G(d) basis set as implemented in Gaussian 09 software. Variable selection and model development were carried out by stepwise multiple linear regression methodology. Predictive performance of the quantitative structure-activation barrier relationship (QSABR) model was assessed by training and test set concept and by calculating leave-one-out cross-validated Q2 and predictive R2 values. The QSABR model can explain and predict 86.5% and 80% of the variances, respectively, in the activation energy barrier training data. Alternatively, a neural network model based on back propagation of errors was developed to assess the nonlinearity of the sought correlations between theoretical descriptors and experimental reaction barriers. A reasonable predictability for the activation barrier of the test set reactions was obtained, which enabled an exploration and interpretation of the significant variables responsible for Diels-Alder interaction between dienes and dienophiles. Thus, studies in the direction of QSABR modelling that provide efficient and fast prediction of activation barriers of the Diels-Alder reactions turn out to be a meaningful alternative to transition state theory based computation.

Quantitative structure-property relationships for octanol-water partition coefficients of polybrominated diphenyl ethers.

PubMed

Li, Linnan; Xie, Shaodong; Cai, Hao; Bai, Xuetao; Xue, Zhao

2008-08-01

Theoretical molecular descriptors were tested against logK(OW) values for polybrominated diphenyl ethers (PBDEs) using the Partial Least-Squares Regression method which can be used to analyze data with many variables and few observations. A quantitative structure-property relationship (QSPR) model was successfully developed with a high cross-validated value (Q(cum)(2)) of 0.961, indicating a good predictive ability and stability of the model. The predictive power of the QSPR model was further cross-validated. The values of logK(OW) for PBDEs are mainly governed by molecular surface area, energy of the lowest unoccupied molecular orbital and the net atomic charges on the oxygen atom. All these descriptors have been discussed to interpret the partitioning mechanism of PBDE chemicals. The bulk property of the molecules represented by molecular surface area is the leading factor, and K(OW) values increase with the increase of molecular surface area. Higher energy of the lowest unoccupied molecular orbital and higher net atomic charge on the oxygen atom of PBDEs result in smaller K(OW). The energy of the lowest unoccupied molecular orbital and the net atomic charge on PBDEs oxygen also play important roles in affecting the partition of PBDEs between octanol and water by influencing the interactions between PBDEs and solvent molecules.

COMPARATIVE STUDY OF THREE FUNDAMENTAL ORGANIC COMPOUNDS OF CHAIN STRUCTURE OF THREE RINGS An approach based in the molecular descriptors of the DFT (Density Functional Theory)

NASA Astrophysics Data System (ADS)

Leon, Neira B. Oscar; Fabio, Mejía Elio; Elizabeth, y. Rincón B.

2008-04-01

The organic molecules of a chain structure containing phenyl, oxazole and oxadiazole rings are used in different combinations as active media for tunable lasers. From this viewpoint, we focused in the theoretical study of organic compounds of three rings, which have similar optical properties (fluorescence and laser properties). The main goal of this study is to compare the electronic structure through the analysis of molecular global descriptors defined in the DFT framework of2-[2-X-phenyl]-5-phenyl-1,3-Oxazole, 2-[2-X-phenyl]-5-phenyl-1,3,4-Oxadiazole, and 2-[2-X-phenyl]-5-phenyl-furane with X = H, F and Cl. The basis set used was 6-31G+(d).

Explaining reaction mechanisms using the dual descriptor: a complementary tool to the molecular electrostatic potential.

PubMed

Martínez-Araya, Jorge Ignacio

2013-07-01

The intrinsic reactivity of cyanide when interacting with a silver cation was rationalized using the dual descriptor (DD) as a complement to the molecular electrostatic potential (MEP) in order to predict interactions at the local level. It was found that DD accurately explains covalent interactions that cannot be explained by MEP, which focuses on essentially ionic interactions. This allowed the rationalization of the reaction mechanism that yields silver cyanide in the gas phase. Other similar reaction mechanisms involving a silver cation interacting with water, ammonia, and thiosulfate were also explained by the combination of MEP and DD. This analysis provides another example of the usefulness of DD as a tool for gaining a deeper understanding of any reaction mechanism that is mainly governed by covalent interactions.

Quantitative structure-activity relationship study of antioxidative peptide by using different sets of amino acids descriptors

NASA Astrophysics Data System (ADS)

Li, Yao-Wang; Li, Bo; He, Jiguo; Qian, Ping

2011-07-01

A database consisting of 214 tripeptides which contain either His or Tyr residue was applied to study quantitative structure-activity relationships (QSAR) of antioxidative tripeptides. Partial Least-Squares Regression analysis (PLSR) was conducted using parameters individually of each amino acid descriptor, including Divided Physico-chemical Property Scores (DPPS), Hydrophobic, Electronic, Steric, and Hydrogen (HESH), Vectors of Hydrophobic, Steric, and Electronic properties (VHSE), Molecular Surface-Weighted Holistic Invariant Molecular (MS-WHIM), isotropic surface area-electronic charge index (ISA-ECI) and Z-scale, to describe antioxidative tripeptides as X-variables and antioxidant activities measured with ferric thiocyanate methods were as Y-variable. After elimination of outliers by Hotelling's T 2 method and residual analysis, six significant models were obtained describing the entire data set. According to cumulative squared multiple correlation coefficients ( R2), cumulative cross-validation coefficients ( Q2) and relative standard deviation for calibration set (RSD c), the qualities of models using DPPS, HESH, ISA-ECI, and VHSE descriptors are better ( R2 > 0.6, Q2 > 0.5, RSD c < 0.39) than that of models using MS-WHIM and Z-scale descriptors ( R2 < 0.6, Q2 < 0.5, RSD c > 0.44). Furthermore, the predictive ability of models using DPPS descriptor is best among the six descriptors systems (cumulative multiple correlation coefficient for predict set ( Rext2) > 0.7). It was concluded that the DPPS is better to describe the amino acid of antioxidative tripeptides. The results of DPPS descriptor reveal that the importance of the center amino acid and the N-terminal amino acid are far more than the importance of the C-terminal amino acid for antioxidative tripeptides. The hydrophobic (positively to activity) and electronic (negatively to activity) properties of the N-terminal amino acid are suggested to play the most important significance to activity, followed by the hydrogen bond (positively to activity) of the center amino acid. The N-terminal amino acid should be a high hydrophobic and low electronic amino acid (such as Ala, Gly, Val, and Leu); the center amino acid would be an amino acid that possesses high hydrogen bond property (such as base amino acid Arg, Lys, and His). The structural characteristics of antioxidative peptide be found in this paper may contribute to the further research of antioxidative mechanism.

PHOENIX: a scoring function for affinity prediction derived using high-resolution crystal structures and calorimetry measurements.

PubMed

Tang, Yat T; Marshall, Garland R

2011-02-28

Binding affinity prediction is one of the most critical components to computer-aided structure-based drug design. Despite advances in first-principle methods for predicting binding affinity, empirical scoring functions that are fast and only relatively accurate are still widely used in structure-based drug design. With the increasing availability of X-ray crystallographic structures in the Protein Data Bank and continuing application of biophysical methods such as isothermal titration calorimetry to measure thermodynamic parameters contributing to binding free energy, sufficient experimental data exists that scoring functions can now be derived by separating enthalpic (ΔH) and entropic (TΔS) contributions to binding free energy (ΔG). PHOENIX, a scoring function to predict binding affinities of protein-ligand complexes, utilizes the increasing availability of experimental data to improve binding affinity predictions by the following: model training and testing using high-resolution crystallographic data to minimize structural noise, independent models of enthalpic and entropic contributions fitted to thermodynamic parameters assumed to be thermodynamically biased to calculate binding free energy, use of shape and volume descriptors to better capture entropic contributions. A set of 42 descriptors and 112 protein-ligand complexes were used to derive functions using partial least-squares for change of enthalpy (ΔH) and change of entropy (TΔS) to calculate change of binding free energy (ΔG), resulting in a predictive r2 (r(pred)2) of 0.55 and a standard error (SE) of 1.34 kcal/mol. External validation using the 2009 version of the PDBbind "refined set" (n = 1612) resulted in a Pearson correlation coefficient (R(p)) of 0.575 and a mean error (ME) of 1.41 pK(d). Enthalpy and entropy predictions were of limited accuracy individually. However, their difference resulted in a relatively accurate binding free energy. While the development of an accurate and applicable scoring function was an objective of this study, the main focus was evaluation of the use of high-resolution X-ray crystal structures with high-quality thermodynamic parameters from isothermal titration calorimetry for scoring function development. With the increasing application of structure-based methods in molecular design, this study suggests that using high-resolution crystal structures, separating enthalpy and entropy contributions to binding free energy, and including descriptors to better capture entropic contributions may prove to be effective strategies toward rapid and accurate calculation of binding affinity.

Energy profile, spectroscopic (FT-IR, FT-Raman and FT-NMR) and DFT studies of 4-bromoisophthalic acid

NASA Astrophysics Data System (ADS)

Arjunan, V.; Thirunarayanan, S.; Mohan, S.

2018-04-01

The stable conformer of 4-bromoisophthalic acid (BIPA) has been identified by potential energy profile analysis. All the structural parameters of 4-bromoisophthalic acid are determined by B3LYP method with 6-311++G**, 6-31G** and cc-pVTZ basis sets. The fundamental vibrations are analysed with the use of FT-IR (4000-400 cm-1) and FT-Raman (4000-100 cm-1) spectra. The harmonic vibrational frequencies are theoretically calculated and compared with experimental FTIR and FT-Raman frequencies. The 1H and 13C NMR spectra have been analysed and compared with theoretical 1H and 13C NMR chemical shifts calculated by gauge independent atomic orbital (GIAO) method. The electronic properties, such as HOMO (highest occupied molecular orbital) and LUMO (lowest unoccupied molecular orbital) energies are determined by B3LYP/cc-pVTZ method. The electron density distribution and site of chemical reactivity of BIPA molecule have been obtained by mapping electron density isosurface with molecular electrostatic potential (MEP). Stability of the molecules arising from hyperconjugative interactions, charge delocalizations have been analysed by using natural bond orbital (NBO) analysis. The thermodynamic properties and atomic natural charges of the compound are analysed and the reactive sites of the molecule are identified. The global and local reactivity descriptors are evaluated to analyse the chemical reactivity and site selectivity of molecule through Fukui functions.

Building Scientific Confidence in the Development and ...

EPA Pesticide Factsheets

Read-across remains a popular data gap filling technique within category and analogue approaches for regulatory purposes. Acceptance of read-across is an ongoing challenge with several efforts underway for identifying and addressing uncertainties. Here we demonstrate an algorithmic approach to facilitate read-across using ToxCast in vitro bioactivity data in conjunction with chemical descriptor information to predict in vivo outcomes in guideline testing studies from ToxRefDB. Over 3400 different chemical structure descriptors were generated for a set of 976 chemicals and supplemented with the outcomes from 821 in vitro assays. The read-across prediction for a given chemical was based on the similarity weighted endpoint outcomes of its nearest neighbors calculated using in vitro bioactivity and chemical structure descriptors, called GenRA. GenRA is based on a computational approach for: (i) defining local validity domains using chemical and bioactivity descriptors, (ii) systematically deriving endpoint read-across predictions within these domains using similarity weighted activity of nearest neighbours, (iii) objectively evaluating predicted performance using tested chemicals, and (iv) assigning read-across predictions to untested chemicals along with estimates of uncertainty. We found in vitro bioactivity descriptors were often found to be more predictive of in vivo toxicity outcomes than chemical structure descriptors. We believe GenRA is an important first st

Evaluation of structure-reactivity descriptors and biological activity spectra of 4-(6-methoxy-2-naphthyl)-2-butanone using spectroscopic techniques

NASA Astrophysics Data System (ADS)

Agrawal, Megha; Deval, Vipin; Gupta, Archana; Sangala, Bagvanth Reddy; Prabhu, S. S.

2016-10-01

The structure and several spectroscopic features along with reactivity parameters of the compound 4-(6-methoxy-2-naphthyl)-2-butanone (Nabumetone) have been studied using experimental techniques and tools derived from quantum chemical calculations. Structure optimization is followed by force field calculations based on density functional theory (DFT) at the B3LYP/6-311++G(d,p) level of theory. The vibrational spectra have been interpreted with the aid of normal coordinate analysis. UV-visible spectrum and the effect of solvent have been discussed. The electronic properties such as HOMO and LUMO energies have been determined by TD-DFT approach. In order to understand various aspects of pharmacological sciences several new chemical reactivity descriptors - chemical potential, global hardness and electrophilicity have been evaluated. Local reactivity descriptors - Fukui functions and local softnesses have also been calculated to find out the reactive sites within molecule. Aqueous solubility and lipophilicity have been calculated which are crucial for estimating transport properties of organic molecules in drug development. Estimation of biological effects, toxic/side effects has been made on the basis of prediction of activity spectra for substances (PASS) prediction results and their analysis by Pharma Expert software. Using the THz-TDS technique, the frequency-dependent absorptions of NBM have been measured in the frequency range up to 3 THz.

DFT analysis on the molecular structure, vibrational and electronic spectra of 2-(cyclohexylamino)ethanesulfonic acid.

PubMed

Renuga Devi, T S; Sharmi kumar, J; Ramkumaar, G R

2015-02-25

The FTIR and FT-Raman spectra of 2-(cyclohexylamino)ethanesulfonic acid were recorded in the regions 4000-400 cm(-1) and 4000-50 cm(-1) respectively. The structural and spectroscopic data of the molecule in the ground state were calculated using Hartee-Fock and Density functional method (B3LYP) with the correlation consistent-polarized valence double zeta (cc-pVDZ) basis set and 6-311++G(d,p) basis set. The most stable conformer was optimized and the structural and vibrational parameters were determined based on this. The complete assignments were performed based on the Potential Energy Distribution (PED) of the vibrational modes, calculated using Vibrational Energy Distribution Analysis (VEDA) 4 program. With the observed FTIR and FT-Raman data, a complete vibrational assignment and analysis of the fundamental modes of the compound were carried out. Thermodynamic properties and Atomic charges were calculated using both Hartee-Fock and density functional method using the cc-pVDZ basis set and compared. The calculated HOMO-LUMO energy gap revealed that charge transfer occurs within the molecule. (1)H and (13)C NMR chemical shifts of the molecule were calculated using Gauge Including Atomic Orbital (GIAO) method and were compared with experimental results. Stability of the molecule arising from hyperconjugative interactions, charge delocalization have been analyzed using Natural Bond Orbital (NBO) analysis. The first order hyperpolarizability (β) and Molecular Electrostatic Potential (MEP) of the molecule was computed using DFT calculations. The electron density based local reactivity descriptor such as Fukui functions were calculated to explain the chemical reactivity site in the molecule. Copyright © 2014 Elsevier B.V. All rights reserved.

Development of quantitative structure-activity relationships and its application in rational drug design.

PubMed

Yang, Guang-Fu; Huang, Xiaoqin

2006-01-01

Over forty years have elapsed since Hansch and Fujita published their pioneering work of quantitative structure-activity relationships (QSAR). Following the introduction of Comparative Molecular Field Analysis (CoMFA) by Cramer in 1998, other three-dimensional QSAR methods have been developed. Currently, combination of classical QSAR and other computational techniques at three-dimensional level is of greatest interest and generally used in the process of modern drug discovery and design. During the last several decades, a number of different mythologies incorporating a range of molecular descriptors and different statistical regression ways have been proposed and successfully applied in developing of new drugs, thus QSAR method has been proven to be indispensable in not only the reliable prediction of specific properties of new compounds, but also the help to elucidate the possible molecular mechanism of the receptor-ligand interactions. Here, we review the recent developments in QSAR and their applications in rational drug design, focusing on the reasonable selection of novel molecular descriptors and the construction of predictive QSAR models by the help of advanced computational techniques.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pederson, Mark R.; Baruah, Tunna; Basurto, Luis

We have applied a recently developed method to incorporate the self-interaction correction through Fermi orbitals to Mg-porphyrin, C{sub 60}, and pentacene molecules. The Fermi-Löwdin orbitals are localized and unitarily invariant to the Kohn-Sham orbitals from which they are constructed. The self-interaction-corrected energy is obtained variationally leading to an optimum set of Fermi-Löwdin orbitals (orthonormalized Fermi orbitals) that gives the minimum energy. A Fermi orbital, by definition, is dependent on a certain point which is referred to as the descriptor position. The degree to which the initial choice of descriptor positions influences the variational approach to the minimum and the complexitymore » of the energy landscape as a function of Fermi-orbital descriptors is examined in detail for Mg-porphyrin. The applications presented here also demonstrate that the method can be applied to larger molecular systems containing a few hundred electrons. The atomization energy of the C{sub 60} molecule within the Fermi-Löwdin-orbital self-interaction-correction approach is significantly improved compared to local density approximation in the Perdew-Wang 92 functional and generalized gradient approximation of Perdew-Burke-Ernzerhof functionals. The eigenvalues of the highest occupied molecular orbitals show qualitative improvement.« less

Self-interaction corrections applied to Mg-porphyrin, C60, and pentacene molecules

NASA Astrophysics Data System (ADS)

Pederson, Mark R.; Baruah, Tunna; Kao, Der-you; Basurto, Luis

2016-04-01

We have applied a recently developed method to incorporate the self-interaction correction through Fermi orbitals to Mg-porphyrin, C60, and pentacene molecules. The Fermi-Löwdin orbitals are localized and unitarily invariant to the Kohn-Sham orbitals from which they are constructed. The self-interaction-corrected energy is obtained variationally leading to an optimum set of Fermi-Löwdin orbitals (orthonormalized Fermi orbitals) that gives the minimum energy. A Fermi orbital, by definition, is dependent on a certain point which is referred to as the descriptor position. The degree to which the initial choice of descriptor positions influences the variational approach to the minimum and the complexity of the energy landscape as a function of Fermi-orbital descriptors is examined in detail for Mg-porphyrin. The applications presented here also demonstrate that the method can be applied to larger molecular systems containing a few hundred electrons. The atomization energy of the C60 molecule within the Fermi-Löwdin-orbital self-interaction-correction approach is significantly improved compared to local density approximation in the Perdew-Wang 92 functional and generalized gradient approximation of Perdew-Burke-Ernzerhof functionals. The eigenvalues of the highest occupied molecular orbitals show qualitative improvement.

Molecular orbital studies (hardness, chemical potential, electronegativity and electrophilicity), vibrational spectroscopic investigation and normal coordinate analysis of 5-{1-hydroxy-2-[(propan-2-yl)amino]ethyl}benzene-1,3-diol

NASA Astrophysics Data System (ADS)

Muthu, S.; Renuga, S.

2014-01-01

FT-IR and FT-Raman spectra of 5-{1-hydroxy-2-[(propan-2-yl) amino] ethyl} benzene-1,3-diol (abbrevi- 54 ated as HPAEBD) were recorded in the region 4000-450 cm-1 and 4000-100 cm-1 respectively. The structure of the molecule was optimized and the structural characteristics were determined by density functional theory (B3LYP) and HF method with 6-31 G(d,p) as basis set. The theoretical wave numbers were scaled and compared with experimental FT-IR and FT-Raman spectra. A detailed interpretation of the vibrational spectra of this compound has been made on the basis of the calculated Potential energy distribution (PED). Stability of the molecule arising from hyperconjugation and charge delocalization is confirmed by the natural bond orbital analysis (NBO). The results show that electron density (ED) in the σ antibonding orbitals and E (2) energies confirm the occurrence of intra molecular charge transfer (ICT) within the molecule. The molecule orbital contributions were studied by using the total (TDOS), sum of α and β electron (αβDOS) density of States. Mulliken population analysis of atomic charges is also calculated. The calculated HOMO and LUMO energy gap shows that charge transfer occurs within the molecule. The electron density-based local reactivity descriptors such as Fukui functions were calculated to explain the chemical selectivity or reactivity site in this compound. On the basis of vibrational analyses, the thermodynamic properties of title compound at different temperatures have been calculated.

Determination of receptor-bound drug conformations by QSAR using flexible fitting to derive a molecular similarity index

NASA Astrophysics Data System (ADS)

Montanari, C. A.; Tute, M. S.; Beezer, A. E.; Mitchell, J. C.

1996-02-01

Results are presented for a QSAR analysis of bisamidines, using a similarity index as descriptor. The method allows for differences in conformation of bisamidines at the receptor site to be taken into consideration. In particular, it has been suggested by others that pentamidine binds in the minor groove of DNA in a so-called isohelical conformation, and our QSAR supports this suggestion. The molecular similarity index for comparison of molecules can be used as a parameter for correlating and hence rationalising the activity as well as suggesting the design of bioactive molecules. The studied compounds had been evaluated for potency against Leishmania mexicana amazonensis, and this potency was used as a dependent variable in a series of QSAR analyses. For the calculation of similarity indexes, each analogue was in turn superimposed on a chosen lead compound in a reference conformation, either extended or isohelical, maximising overlap and hence similarity by flexible fitting.

A comparative DFT study on the antioxidant activity of apigenin and scutellarein flavonoid compounds

NASA Astrophysics Data System (ADS)

Sadasivam, K.; Kumaresan, R.

2011-03-01

The potent antioxidant activity of flavonoids relevant to their ability to scavenge reactive oxygen species is the most important function of flavonoids. Density functional theory calculations were explored to investigate the antioxidant activity of flavonoid compounds such as apigenin and scutellarein. The biological characteristics are dependent on electronic parameters, describing the charge distribution on the rings of the flavonoid molecules. The computation of structural and various molecular descriptors such as polarizability, dipole moment, energy gap, homolytic O-H bond dissociation enthalpies (BDEs), ionization potential (IP), electron affinity, hardness, softness, electronegativity, electrophilic index and density plot of molecular orbital for neutral as well as radical species were carried out and studied. The B3LYP/6-311G(d,p) basis set was adopted for all the computations. This computation reveals that scutellarein exhibits higher degree of antioxidant activity than apigenin. Their dipole moment and polarizability analysis show that both the compounds are polar in nature and have the capacity to polarize other atoms.

Optimal descriptor as a translator of eclectic information into the prediction of membrane damage: the case of a group of ZnO and TiO2 nanoparticles.

PubMed

Toropova, Alla P; Toropov, Andrey A; Benfenati, Emilio; Puzyn, Tomasz; Leszczynska, Danuta; Leszczynski, Jerzy

2014-10-01

The development of quantitative structure-activity relationships for nanomaterials needs representation of molecular structure of extremely complex molecular systems. Obviously, various characteristics of nanomaterial could impact associated biochemical endpoints. Following features of TiO2 and ZnO nanoparticles (n=42) are considered here: (i) engineered size (nm); (ii) size in water suspension (nm); (iii) size in phosphate buffered saline (PBS, nm); (iv) concentration (mg/L); and (v) zeta potential (mV). The damage to cellular membranes (units/L) is selected as an endpoint. Quantitative features-activity relationships (QFARs) are calculated by the Monte Carlo technique for three distributions of data representing values associated with membrane damage into the training and validation sets. The obtained models are characterized by the following average statistics: 0.78

Hand-Based Biometric Analysis

NASA Technical Reports Server (NTRS)

Bebis, George (Inventor); Amayeh, Gholamreza (Inventor)

2015-01-01

Hand-based biometric analysis systems and techniques are described which provide robust hand-based identification and verification. An image of a hand is obtained, which is then segmented into a palm region and separate finger regions. Acquisition of the image is performed without requiring particular orientation or placement restrictions. Segmentation is performed without the use of reference points on the images. Each segment is analyzed by calculating a set of Zernike moment descriptors for the segment. The feature parameters thus obtained are then fused and compared to stored sets of descriptors in enrollment templates to arrive at an identity decision. By using Zernike moments, and through additional manipulation, the biometric analysis is invariant to rotation, scale, or translation or an in put image. Additionally, the analysis utilizes re-use of commonly-seen terms in Zernike calculations to achieve additional efficiencies over traditional Zernike moment calculation.

Hand-Based Biometric Analysis

NASA Technical Reports Server (NTRS)

Bebis, George

2013-01-01

Hand-based biometric analysis systems and techniques provide robust hand-based identification and verification. An image of a hand is obtained, which is then segmented into a palm region and separate finger regions. Acquisition of the image is performed without requiring particular orientation or placement restrictions. Segmentation is performed without the use of reference points on the images. Each segment is analyzed by calculating a set of Zernike moment descriptors for the segment. The feature parameters thus obtained are then fused and compared to stored sets of descriptors in enrollment templates to arrive at an identity decision. By using Zernike moments, and through additional manipulation, the biometric analysis is invariant to rotation, scale, or translation or an input image. Additionally, the analysis uses re-use of commonly seen terms in Zernike calculations to achieve additional efficiencies over traditional Zernike moment calculation.

DFT/PCM, QTAIM, 1H NMR conformational studies and QSAR modeling of thirty-two anti-Leishmania amazonensis Morita-Baylis-Hillman Adducts

NASA Astrophysics Data System (ADS)

Filho, Edilson B. A.; Moraes, Ingrid A.; Weber, Karen C.; Rocha, Gerd B.; Vasconcellos, Mário L. A. A.

2012-08-01

Morita-Baylis-Hillman Adducts (MBHA) has been recently synthesized and bio-evaluated by our research group against Leishmania amazonensis, parasite that causes cutaneous and mucocutaneous leishmaniasis. We present here a theoretical conformational study of thirty-two leismanicidal MBHA by B3LYP/6-31+g(d) calculations with Polarized Continuum Model (PCM) to simulate water influence. Intramolecular Hydrogen Bonds (IHBs) indicated to control the most conformational preferences of MBHA. Quantum Theory Atoms in Molecules (QTAIM) calculations were able to characterize these interactions at Bond Critical Point level. Compounds presenting an unusual seven member IHB between NO2 group and hydroxyl moiety, supported by experimental spectroscopic data, showed a considerable improvement of biological activity (lower IC50 values). These results are in accordance to redox NO2 mechanism of action. Based on structural observations, some molecular descriptors were calculated and submitted to Quantitative Structure-Activity Relationship (QSAR) studies through the PLS Regression Method. These studies provided a model with good validation parameters values (R2 = 0.71, Q2 = 0.61 and Qext2 = 0.92).

Deep Eutectic Solvents as Convenient Media for Synthesis of Novel Coumarinyl Schiff Bases and Their QSAR Studies.

PubMed

Molnar, Maja; Komar, Mario; Brahmbhatt, Harshad; Babić, Jurislav; Jokić, Stela; Rastija, Vesna

2017-09-05

Deep eutectic solvents, as green and environmentally friendly media, were utilized in the synthesis of novel coumarinyl Schiff bases. Novel derivatives were synthesized from 2-((4-methyl-2-oxo-2 H -chromen-7-yl)oxy)acetohydrazide and corresponding aldehyde in choline chloride:malonic acid (1:1) based deep eutectic solvent. In these reactions, deep eutectic solvent acted as a solvent and catalyst as well. Novel Schiff bases were synthesized in high yields (65-75%) with no need for further purification, and their structures were confirmed by mass spectra, ¹H and 13 C NMR. Furthermore, their antioxidant activity was determined and compared to antioxidant activity of previously synthesized derivatives, thus investigating their structure-activity relationship utilizing quantitative structure-activity relationship QSAR studies. Calculation of molecular descriptors has been performed by DRAGON software. The best QSAR model ( R tr = 0.636; R ext = 0.709) obtained with three descriptors ( MATS3m , Mor22u , Hy ) implies that the pairs of atoms higher mass at the path length 3, three-dimensional arrangement of atoms at scattering parameter s = 21 Å - ¹, and higher number of hydrophilic groups (-OH, -NH) enhanced antioxidant activity. Electrostatic potential surface of the most active compounds showed possible regions for donation of electrons to 1,1-diphenyl-2-picryhydrazyl (DPPH) radicals.

Modeling of adipose/blood partition coefficient for environmental chemicals.

PubMed

Papadaki, K C; Karakitsios, S P; Sarigiannis, D A

2017-12-01

A Quantitative Structure Activity Relationship (QSAR) model was developed in order to predict the adipose/blood partition coefficient of environmental chemical compounds. The first step of QSAR modeling was the collection of inputs. Input data included the experimental values of adipose/blood partition coefficient and two sets of molecular descriptors for 67 organic chemical compounds; a) the descriptors from Linear Free Energy Relationship (LFER) and b) the PaDEL descriptors. The datasets were split to training and prediction set and were analysed using two statistical methods; Genetic Algorithm based Multiple Linear Regression (GA-MLR) and Artificial Neural Networks (ANN). The models with LFER and PaDEL descriptors, coupled with ANN, produced satisfying performance results. The fitting performance (R 2 ) of the models, using LFER and PaDEL descriptors, was 0.94 and 0.96, respectively. The Applicability Domain (AD) of the models was assessed and then the models were applied to a large number of chemical compounds with unknown values of adipose/blood partition coefficient. In conclusion, the proposed models were checked for fitting, validity and applicability. It was demonstrated that they are stable, reliable and capable to predict the values of adipose/blood partition coefficient of "data poor" chemical compounds that fall within the applicability domain. Copyright © 2017. Published by Elsevier Ltd.

Harmony Search as a Powerful Tool for Feature Selection in QSPR Study of the Drugs Lipophilicity.

PubMed

Bahadori, Behnoosh; Atabati, Morteza

2017-01-01

Aims & Scope: Lipophilicity represents one of the most studied and most frequently used fundamental physicochemical properties. In the present work, harmony search (HS) algorithm is suggested to feature selection in quantitative structure-property relationship (QSPR) modeling to predict lipophilicity of neutral, acidic, basic and amphotheric drugs that were determined by UHPLC. Harmony search is a music-based metaheuristic optimization algorithm. It was affected by the observation that the aim of music is to search for a perfect state of harmony. Semi-empirical quantum-chemical calculations at AM1 level were used to find the optimum 3D geometry of the studied molecules and variant descriptors (1497 descriptors) were calculated by the Dragon software. The selected descriptors by harmony search algorithm (9 descriptors) were applied for model development using multiple linear regression (MLR). In comparison with other feature selection methods such as genetic algorithm and simulated annealing, harmony search algorithm has better results. The root mean square error (RMSE) with and without leave-one out cross validation (LOOCV) were obtained 0.417 and 0.302, respectively. The results were compared with those obtained from the genetic algorithm and simulated annealing methods and it showed that the HS is a helpful tool for feature selection with fine performance. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Cinnamaldehyde, Cinnamic Acid, and Cinnamyl Alcohol, the Bioactives of Cinnamomum cassia Exhibit HDAC8 Inhibitory Activity: An In vitro and In silico Study

PubMed Central

Patil, Mangesh; Choudhari, Amit S.; Pandita, Savita; Islam, Md Ataul; Raina, Prerna; Kaul-Ghanekar, Ruchika

2017-01-01

Background: The altered expression of histone deacetylase family member 8 (HDAC8) has been found to be linked with various cancers, thereby making its selective inhibition a potential strategy in cancer therapy. Recently, plant secondary metabolites, particularly phenolic compounds, have been shown to possess HDAC inhibitory activity. Objective: In the present work, we have evaluated the potential of cinnamaldehyde (CAL), cinnamic acid (CA), and cinnamyl alcohol (CALC) (bioactives of Cinnamomum) as well as aqueous cinnamon extract (ACE), to inhibit HDAC8 activity in vitro and in silico. Materials and Methods: HDAC8 inhibitory activity of ACE and cinnamon bioactives was determined in vitro using HDAC8 inhibitor screening kit. Trichostatin A (TSA), a well-known anti-cancer agent and HDAC inhibitor, was used as a positive control. In silico studies included molecular descriptor Analysis molecular docking absorption, distribution, metabolism, excretion, and toxicity prediction, density function theory calculation and synthetic accessibility program. Results: Pharmacoinformatics studies implicated that ACE and its Bioactives (CAL, CA, and CALC) exhibited comparable activity with that of TSA. The highest occupied molecular orbitals and lowest unoccupied molecular orbitals along with binding energy of cinnamon bioactives were comparable with that of TSA. Molecular docking results suggested that all the ligands maintained two hydrogen bond interactions within the active site of HDAC8. Finally, the synthetic accessibility values showed that cinnamon bioactives were easy to synthesize compared to TSA. Conclusion: It was evident from both the experimental and computational data that cinnamon bioactives exhibited significant HDAC8 inhibitory activity, thereby suggesting their potential therapeutic implications against cancer. SUMMARY Pharmacoinformatics studies revealed that cinnamon bioactives bound to the active site of HDAC8 enzyme in a way similar to that of TSAThe molecular descriptors of cinnamon compounds successfully correlated with TSA values. The binding interactions and energies were also found to be close to TSASynthetic accessibility values showed that cinnamon bioactives were easy to synthesize compared to TSA. Abbreviations used: ACE: Aqueous Cinnamon Extract; DFT: Density Function Theory; CAL: Cinnamaldehyde; CA: Cinnamic Acid; CALC: Cinnamyl Alcohol; MW: Molecular Weight; ROTBs: Rotatable Bonds; ROF: Lipinski's Rule of Five; TSA: Trichostatin A; PDB: Protein Data Bank; RMSD: Root Mean Square Deviation; HBA: Hydrogen Bond Acceptor; HBD: Hydrogen Bond Donor; ADMET: Absorption, Distribution, Metabolism, Excretion and Toxicity; FO: Frontier Orbital; HOMOs: Highest Occupied Molecular Orbitals; LUMOs: Lowest Unoccupied Molecular Orbitals; BE: Binding Energy. PMID:29142427

Improved nucleic acid descriptors for siRNA efficacy prediction.

PubMed

Sciabola, Simone; Cao, Qing; Orozco, Modesto; Faustino, Ignacio; Stanton, Robert V

2013-02-01

Although considerable progress has been made recently in understanding how gene silencing is mediated by the RNAi pathway, the rational design of effective sequences is still a challenging task. In this article, we demonstrate that including three-dimensional descriptors improved the discrimination between active and inactive small interfering RNAs (siRNAs) in a statistical model. Five descriptor types were used: (i) nucleotide position along the siRNA sequence, (ii) nucleotide composition in terms of presence/absence of specific combinations of di- and trinucleotides, (iii) nucleotide interactions by means of a modified auto- and cross-covariance function, (iv) nucleotide thermodynamic stability derived by the nearest neighbor model representation and (v) nucleic acid structure flexibility. The duplex flexibility descriptors are derived from extended molecular dynamics simulations, which are able to describe the sequence-dependent elastic properties of RNA duplexes, even for non-standard oligonucleotides. The matrix of descriptors was analysed using three statistical packages in R (partial least squares, random forest, and support vector machine), and the most predictive model was implemented in a modeling tool we have made publicly available through SourceForge. Our implementation of new RNA descriptors coupled with appropriate statistical algorithms resulted in improved model performance for the selection of siRNA candidates when compared with publicly available siRNA prediction tools and previously published test sets. Additional validation studies based on in-house RNA interference projects confirmed the robustness of the scoring procedure in prospective studies.

Modifications of the chemical structure of phenolics differentially affect physiological activities in pulvinar cells of Mimosa pudica L. II. Influence of various molecular properties in relation to membrane transport.

PubMed

Rocher, Françoise; Roblin, Gabriel; Chollet, Jean-François

2017-03-01

Early prediction of compound absorption by cells is of considerable importance in the building of an integrated scheme describing the impact of a compound on intracellular biological processes. In this scope, we study the structure-activity relationships of several benzoic acid-related phenolics which are involved in many plant biological phenomena (growth, flowering, allelopathy, defense processes). Using the partial least squares (PLS) regression method, the impact of molecular descriptors that have been shown to play an important role concerning the uptake of pharmacologically active compounds by animal cells was analyzed in terms of the modification of membrane potential, variations in proton flux, and inhibition of the osmocontractile reaction of pulvinar cells of Mimosa pudica leaves. The hydrogen bond donors (HBD) and hydrogen bond acceptors (HBA), polar surface area (PSA), halogen ratio (Hal ratio), number of rotatable bonds (FRB), molar volume (MV), molecular weight (MW), and molar refractivity (MR) were considered in addition to two physicochemical properties (logD and the amount of non-dissociated form in relation to pKa). HBD + HBA and PSA predominantly impacted the three biological processes compared to the other descriptors. The coefficient of determination in the quantitative structure-activity relationship (QSAR) models indicated that a major part of the observed seismonasty inhibition and proton flux modification can be explained by the impact of these descriptors, whereas this was not the case for membrane potential variations. These results indicate that the transmembrane transport of the compounds is a predominant component. An increasing number of implicated descriptors as the biological processes become more complex may reflect their impacts on an increasing number of sites in the cell. The determination of the most efficient effectors may lead to a practical use to improve drugs in the control of microbial attacks on plants.

Olfactory perception of chemically diverse molecules.

PubMed

Keller, Andreas; Vosshall, Leslie B

2016-08-08

Understanding the relationship between a stimulus and how it is perceived reveals fundamental principles about the mechanisms of sensory perception. While this stimulus-percept problem is mostly understood for color vision and tone perception, it is not currently possible to predict how a given molecule smells. While there has been some progress in predicting the pleasantness and intensity of an odorant, perceptual data for a larger number of diverse molecules are needed to improve current predictions. Towards this goal, we tested the olfactory perception of 480 structurally and perceptually diverse molecules at two concentrations using a panel of 55 healthy human subjects. For each stimulus, we collected data on perceived intensity, pleasantness, and familiarity. In addition, subjects were asked to apply 20 semantic odor quality descriptors to these stimuli, and were offered the option to describe the smell in their own words. Using this dataset, we replicated several previous correlations between molecular features of the stimulus and olfactory perception. The number of sulfur atoms in a molecule was correlated with the odor quality descriptors "garlic," "fish," and "decayed," and large and structurally complex molecules were perceived to be more pleasant. We discovered a number of correlations in intensity perception between molecules. We show that familiarity had a strong effect on the ability of subjects to describe a smell. Many subjects used commercial products to describe familiar odorants, highlighting the role of prior experience in verbal reports of olfactory perception. Nonspecific descriptors like "chemical" were applied frequently to unfamiliar odorants, and unfamiliar odorants were generally rated as neither pleasant nor unpleasant. We present a very large psychophysical dataset and use this to correlate molecular features of a stimulus to olfactory percept. Our work reveals robust correlations between molecular features and perceptual qualities, and highlights the dominant role of familiarity and experience in assigning verbal descriptors to odorants.

Molecule kernels: a descriptor- and alignment-free quantitative structure-activity relationship approach.

PubMed

Mohr, Johannes A; Jain, Brijnesh J; Obermayer, Klaus

2008-09-01

Quantitative structure activity relationship (QSAR) analysis is traditionally based on extracting a set of molecular descriptors and using them to build a predictive model. In this work, we propose a QSAR approach based directly on the similarity between the 3D structures of a set of molecules measured by a so-called molecule kernel, which is independent of the spatial prealignment of the compounds. Predictors can be build using the molecule kernel in conjunction with the potential support vector machine (P-SVM), a recently proposed machine learning method for dyadic data. The resulting models make direct use of the structural similarities between the compounds in the test set and a subset of the training set and do not require an explicit descriptor construction. We evaluated the predictive performance of the proposed method on one classification and four regression QSAR datasets and compared its results to the results reported in the literature for several state-of-the-art descriptor-based and 3D QSAR approaches. In this comparison, the proposed molecule kernel method performed better than the other QSAR methods.

The implementation of aerial object recognition algorithm based on contour descriptor in FPGA-based on-board vision system

NASA Astrophysics Data System (ADS)

Babayan, Pavel; Smirnov, Sergey; Strotov, Valery

2017-10-01

This paper describes the aerial object recognition algorithm for on-board and stationary vision system. Suggested algorithm is intended to recognize the objects of a specific kind using the set of the reference objects defined by 3D models. The proposed algorithm based on the outer contour descriptor building. The algorithm consists of two stages: learning and recognition. Learning stage is devoted to the exploring of reference objects. Using 3D models we can build the database containing training images by rendering the 3D model from viewpoints evenly distributed on a sphere. Sphere points distribution is made by the geosphere principle. Gathered training image set is used for calculating descriptors, which will be used in the recognition stage of the algorithm. The recognition stage is focusing on estimating the similarity of the captured object and the reference objects by matching an observed image descriptor and the reference object descriptors. The experimental research was performed using a set of the models of the aircraft of the different types (airplanes, helicopters, UAVs). The proposed orientation estimation algorithm showed good accuracy in all case studies. The real-time performance of the algorithm in FPGA-based vision system was demonstrated.

Correlating subjective and objective descriptors of ultra high molecular weight wear particles from total joint prostheses.

PubMed

McMullin, Brian T; Leung, Ming-Ying; Shanbhag, Arun S; McNulty, Donald; Mabrey, Jay D; Agrawal, C Mauli

2006-02-01

A total of 750 images of individual ultra-high molecular weight polyethylene (UHMWPE) particles isolated from periprosthetic failed hip, knee, and shoulder arthroplasties were extracted from archival scanning electron micrographs. Particle size and morphology was subsequently analyzed using computerized image analysis software utilizing five descriptors found in ASTM F1877-98, a standard for quantitative description of wear debris. An online survey application was developed to display particle images, and allowed ten respondents to classify particle morphologies according to commonly used terminology as fibers, flakes, or granules. Particles were categorized based on a simple majority of responses. All descriptors were evaluated using a one-way ANOVA and Tukey-Kramer test for all-pairs comparison among each class of particles. A logistic regression model using half of the particles included in the survey was then used to develop a mathematical scheme to predict whether a given particle should be classified as a fiber, flake, or granule based on its quantitative measurements. The validity of the model was then assessed using the other half of the survey particles and compared with human responses. Comparison of the quantitative measurements of isolated particles showed that the morphologies of each particle type classified by respondents were statistically different from one another (p<0.05). The average agreement between mathematical prediction and human respondents was 83.5% (standard error 0.16%). These data suggest that computerized descriptors can be feasibly correlated with subjective terminology, thus providing a basis for a common vocabulary for particle description which can be translated into quantitative dimensions.

Correlating subjective and objective descriptors of ultra high molecular weight wear particles from total joint prostheses

PubMed Central

McMullin, Brian T.; Leung, Ming-Ying; Shanbhag, Arun S.; McNulty, Donald; Mabrey, Jay D.; Agrawal, C. Mauli

2014-01-01

A total of 750 images of individual ultra-high molecular weight polyethylene (UHMWPE) particles isolated from periprosthetic failed hip, knee, and shoulder arthroplasties were extracted from archival scanning electron micrographs. Particle size and morphology was subsequently analyzed using computerized image analysis software utilizing five descriptors found in ASTM F1877-98, a standard for quantitative description of wear debris. An online survey application was developed to display particle images, and allowed ten respondents to classify particle morphologies according to commonly used terminology as fibers, flakes, or granules. Particles were categorized based on a simple majority of responses. All descriptors were evaluated using a one-way ANOVA and Tukey–Kramer test for all-pairs comparison among each class of particles. A logistic regression model using half of the particles included in the survey was then used to develop a mathematical scheme to predict whether a given particle should be classified as a fiber, flake, or granule based on its quantitative measurements. The validity of the model was then assessed using the other half of the survey particles and compared with human responses. Comparison of the quantitative measurements of isolated particles showed that the morphologies of each particle type classified by respondents were statistically different from one another (po0:05). The average agreement between mathematical prediction and human respondents was 83.5% (standard error 0.16%). These data suggest that computerized descriptors can be feasibly correlated with subjective terminology, thus providing a basis for a common vocabulary for particle description which can be translated into quantitative dimensions. PMID:16112725

Lagrangian Descriptors: A Method for Revealing Phase Space Structures of General Time Dependent Dynamical Systems

NASA Astrophysics Data System (ADS)

Mancho, Ana M.; Wiggins, Stephen; Curbelo, Jezabel; Mendoza, Carolina

2013-11-01

Lagrangian descriptors are a recent technique which reveals geometrical structures in phase space and which are valid for aperiodically time dependent dynamical systems. We discuss a general methodology for constructing them and we discuss a ``heuristic argument'' that explains why this method is successful. We support this argument by explicit calculations on a benchmark problem. Several other benchmark examples are considered that allow us to assess the performance of Lagrangian descriptors with both finite time Lyapunov exponents (FTLEs) and finite time averages of certain components of the vector field (``time averages''). In all cases Lagrangian descriptors are shown to be both more accurate and computationally efficient than these methods. We thank CESGA for computing facilities. This research was supported by MINECO grants: MTM2011-26696, I-Math C3-0104, ICMAT Severo Ochoa project SEV-2011-0087, and CSIC grant OCEANTECH. SW acknowledges the support of the ONR (Grant No. N00014-01-1-0769).

Optical, Fluorescence with quantum analysis of hydrazine (1, 3- Dinitro Phenyl) by DFT and Ab initio approach

NASA Astrophysics Data System (ADS)

Cecily Mary Glory, D.; Sambathkumar, K.; Madivanane, R.; Velmurugan, G.; Gayathri, R.; Nithiyanantham, S.; Venkatachalapathy, M.; Rajkamal, N.

2018-07-01

Experimental and computational study of molecular structure, vibrational and UV-spectral analysis of Hydrazine (1, 3- Dinitrophenyl) (HDP) derivatives. The crystal was grown by slow cooling method and the crystalline perfection of single crystals was evaluated by high resolution X-ray diffractometry (HRXRD) using a multicrystal X-ray diffractometer. Fluorescence, FT-IR and FT-Raman spectra of HDP crystal were recorded. The assignments of the vibrational spectra have been carried out with the help of normal co-ordinate analysis (NCA) followed by scaled quantum force field methodology (SQMFF). NMR studies have confirmed respectively the crystal structure and functional groups of the grown crystal. The energy and oscillator strength calculated by Time-Dependent Density Functional Theory (TD-DFT) result complements the experimental findings. The calculated MESP, UV, HOMO-LUMO energies show that charge transfer done within the molecule. And various thermodynamic parameters are studied. Fukui determines the local reactive site of electrophilic, nucleophilic, descriptor.

modlAMP: Python for antimicrobial peptides.

PubMed

Müller, Alex T; Gabernet, Gisela; Hiss, Jan A; Schneider, Gisbert

2017-09-01

We have implemented the lecular esign aboratory's nti icrobial eptides package ( ), a Python-based software package for the design, classification and visual representation of peptide data. modlAMP offers functions for molecular descriptor calculation and the retrieval of amino acid sequences from public or local sequence databases, and provides instant access to precompiled datasets for machine learning. The package also contains methods for the analysis and representation of circular dichroism spectra. The modlAMP Python package is available under the BSD license from URL http://doi.org/10.5905/ethz-1007-72 or via pip from the Python Package Index (PyPI). gisbert.schneider@pharma.ethz.ch. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Quantitative Structure-Antifungal Activity Relationships for cinnamate derivatives.

PubMed

Saavedra, Laura M; Ruiz, Diego; Romanelli, Gustavo P; Duchowicz, Pablo R

2015-12-01

Quantitative Structure-Activity Relationships (QSAR) are established with the aim of analyzing the fungicidal activities of a set of 27 active cinnamate derivatives. The exploration of more than a thousand of constitutional, topological, geometrical and electronic molecular descriptors, which are calculated with Dragon software, leads to predictions of the growth inhibition on Pythium sp and Corticium rolfsii fungi species, in close agreement to the experimental values extracted from the literature. A set containing 21 new structurally related cinnamate compounds is prepared. The developed QSAR models are applied to predict the unknown fungicidal activity of this set, showing that cinnamates like 38, 28 and 42 are expected to be highly active for Pythium sp, while this is also predicted for 28 and 34 in C. rolfsii. Copyright © 2015 Elsevier Inc. All rights reserved.

A physically interpretable quantum-theoretic QSAR for some carbonic anhydrase inhibitors with diverse aromatic rings, obtained by a new QSAR procedure.

PubMed

Clare, Brian W; Supuran, Claudiu T

2005-03-15

A QSAR based almost entirely on quantum theoretically calculated descriptors has been developed for a large and heterogeneous group of aromatic and heteroaromatic carbonic anhydrase inhibitors, using orbital energies, nodal angles, atomic charges, and some other intuitively appealing descriptors. Most calculations have been done at the B3LYP/6-31G* level of theory. For the first time we have treated five-membered rings by the same means that we have used for benzene rings in the past. Our flip regression technique has been expanded to encompass automatic variable selection. The statistical quality of the results, while not equal to those we have had with benzene derivatives, is very good considering the noncongeneric nature of the compounds. The most significant correlation was with charge on the atoms of the sulfonamide group, followed by the nodal orientation and the solvation energy calculated by COSMO and the charge polarization of the molecule calculated as the mean absolute Mulliken charge over all atoms.

Establishment of an in silico phototoxicity prediction method by combining descriptors related to photo-absorption and photo-reaction.

PubMed

Haranosono, Yu; Kurata, Masaaki; Sakaki, Hideyuki

2014-08-01

One of the mechanisms of phototoxicity is photo-reaction, such as reactive oxygen species (ROS) generation following photo-absorption. We focused on ROS generation and photo-absorption as key-steps, because these key-steps are able to be described by photochemical properties, and these properties are dependent on chemical structure. Photo-reactivity of a compound is described by HOMO-LUMO Gap (HLG), generally. Herein, we showed that HLG can be used as a descriptor of the generation of reactive oxygen species. Moreover, the maximum-conjugated π electron number (PENMC), which we found as a descriptor of photo-absorption, could also predict in vitro phototoxicity. Each descriptor could predict in vitro phototoxicity with 70.0% concordance, but there was un-predicted area found (gray zone). Interestingly, some compounds in each gray zone were not common, indicating that the combination of two descriptors could improve prediction potential. We reset the cut-off lines to define positive zone, negative zone and gray zone for each descriptor. Thereby we overlapped HLG and PENMC in a graph, and divided the total area to nine zones with cut-off lines of each descriptor. The rules to prediction were decided to achieve the best concordance, and the concordances were improved up to 82.8% for self-validation, 81.6% for cross-validation. We found common properties among false positive or negative compounds, photo-reactive structure and photo-allergenic, respectively. In addition, our method could be adapted to compounds rich in structural diversity using only chemical structure without any statistical analysis and complicated calculation.

In silico environmental chemical science: properties and processes from statistical and computational modelling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tratnyek, Paul G.; Bylaska, Eric J.; Weber, Eric J.

2017-01-01

Quantitative structure–activity relationships (QSARs) have long been used in the environmental sciences. More recently, molecular modeling and chemoinformatic methods have become widespread. These methods have the potential to expand and accelerate advances in environmental chemistry because they complement observational and experimental data with “in silico” results and analysis. The opportunities and challenges that arise at the intersection between statistical and theoretical in silico methods are most apparent in the context of properties that determine the environmental fate and effects of chemical contaminants (degradation rate constants, partition coefficients, toxicities, etc.). The main example of this is the calibration of QSARs usingmore » descriptor variable data calculated from molecular modeling, which can make QSARs more useful for predicting property data that are unavailable, but also can make them more powerful tools for diagnosis of fate determining pathways and mechanisms. Emerging opportunities for “in silico environmental chemical science” are to move beyond the calculation of specific chemical properties using statistical models and toward more fully in silico models, prediction of transformation pathways and products, incorporation of environmental factors into model predictions, integration of databases and predictive models into more comprehensive and efficient tools for exposure assessment, and extending the applicability of all the above from chemicals to biologicals and materials.« less

Combined molecular docking and QSAR study of fused heterocyclic herbicide inhibitors of D1 protein in photosystem II of plants.

PubMed

Funar-Timofei, Simona; Borota, Ana; Crisan, Luminita

2017-05-01

Cinnoline, pyridine, pyrimidine, and triazine herbicides were found be inhibitors of the D1 protein in photosystem II (D1 PSII) electron transport of plants. The photosystem II inhibitory activity of these herbicides, expressed by experimental [Formula: see text] values, was modeled by a docking and quantitative structure-activity relationships study. A conformer ensemble for each of the herbicide structure was generated using the MMFF94s force field. These conformers were further employed in a docking approach, which provided new information about the rational "active conformations" and various interaction patterns of the herbicide derivatives with D1 PSII. The most "active conformers" from the docking study were used to calculate structural descriptors, which were further related to the inhibitory experimental [Formula: see text] values by multiple linear regression (MLR). The dataset was divided into training and test sets according to the partition around medoids approach, taking 27% of the compounds from the entire series for the test set. Variable selection was performed using the genetic algorithm, and several criteria were checked for model performance. WHIM and GETAWAY geometrical descriptors (position of substituents and moieties in the molecular space) were found to contribute to the herbicidal activity. The derived MLR model is statistically significant, shows very good stability and was used to predict the herbicidal activity of new derivatives having cinnoline, indeno[1.2-c]cinnoline-ll-one, triazolo[1,5-a] pyridine, imidazo[1,2-a]pyridine, triazine and triazolo[1,5-a] pyrimidine scaffolds whose experimental inhibitory activity against D1 PSII had not been determined up to now.

A cascaded QSAR model for efficient prediction of overall power conversion efficiency of all-organic dye-sensitized solar cells.

PubMed

Li, Hongzhi; Zhong, Ziyan; Li, Lin; Gao, Rui; Cui, Jingxia; Gao, Ting; Hu, Li Hong; Lu, Yinghua; Su, Zhong-Min; Li, Hui

2015-05-30

A cascaded model is proposed to establish the quantitative structure-activity relationship (QSAR) between the overall power conversion efficiency (PCE) and quantum chemical molecular descriptors of all-organic dye sensitizers. The cascaded model is a two-level network in which the outputs of the first level (JSC, VOC, and FF) are the inputs of the second level, and the ultimate end-point is the overall PCE of dye-sensitized solar cells (DSSCs). The model combines quantum chemical methods and machine learning methods, further including quantum chemical calculations, data division, feature selection, regression, and validation steps. To improve the efficiency of the model and reduce the redundancy and noise of the molecular descriptors, six feature selection methods (multiple linear regression, genetic algorithms, mean impact value, forward selection, backward elimination, and +n-m algorithm) are used with the support vector machine. The best established cascaded model predicts the PCE values of DSSCs with a MAE of 0.57 (%), which is about 10% of the mean value PCE (5.62%). The validation parameters according to the OECD principles are R(2) (0.75), Q(2) (0.77), and Qcv2 (0.76), which demonstrate the great goodness-of-fit, predictivity, and robustness of the model. Additionally, the applicability domain of the cascaded QSAR model is defined for further application. This study demonstrates that the established cascaded model is able to effectively predict the PCE for organic dye sensitizers with very low cost and relatively high accuracy, providing a useful tool for the design of dye sensitizers with high PCE. © 2015 Wiley Periodicals, Inc.

Selection of representative emerging micropollutants for drinking water treatment studies: a systematic approach.

PubMed

Jin, Xiaohui; Peldszus, Sigrid

2012-01-01

Micropollutants remain of concern in drinking water, and there is a broad interest in the ability of different treatment processes to remove these compounds. To gain a better understanding of treatment effectiveness for structurally diverse compounds and to be cost effective, it is necessary to select a small set of representative micropollutants for experimental studies. Unlike other approaches to-date, in this research micropollutants were systematically selected based solely on their physico-chemical and structural properties that are important in individual water treatment processes. This was accomplished by linking underlying principles of treatment processes such as coagulation/flocculation, oxidation, activated carbon adsorption, and membrane filtration to compound characteristics and corresponding molecular descriptors. A systematic statistical approach not commonly used in water treatment was then applied to a compound pool of 182 micropollutants (identified from the literature) and their relevant calculated molecular descriptors. Principal component analysis (PCA) was used to summarize the information residing in this large dataset. D-optimal onion design was then applied to the PCA results to select structurally representative compounds that could be used in experimental treatment studies. To demonstrate the applicability and flexibility of this selection approach, two sets of 22 representative micropollutants are presented. Compounds in the first set are representative when studying a range of water treatment processes (coagulation/flocculation, oxidation, activated carbon adsorption, and membrane filtration), whereas the second set shows representative compounds for ozonation and advanced oxidation studies. Overall, selected micropollutants in both lists are structurally diverse, have wide-ranging physico-chemical properties and cover a large spectrum of applications. The systematic compound selection approach presented here can also be adjusted to fit individual research needs with respect to type of micropollutants, treatment processes and number of compounds selected. Copyright © 2011 Elsevier B.V. All rights reserved.

Resolving Transition Metal Chemical Space: Feature Selection for Machine Learning and Structure-Property Relationships.

PubMed

Janet, Jon Paul; Kulik, Heather J

2017-11-22

Machine learning (ML) of quantum mechanical properties shows promise for accelerating chemical discovery. For transition metal chemistry where accurate calculations are computationally costly and available training data sets are small, the molecular representation becomes a critical ingredient in ML model predictive accuracy. We introduce a series of revised autocorrelation functions (RACs) that encode relationships of the heuristic atomic properties (e.g., size, connectivity, and electronegativity) on a molecular graph. We alter the starting point, scope, and nature of the quantities evaluated in standard ACs to make these RACs amenable to inorganic chemistry. On an organic molecule set, we first demonstrate superior standard AC performance to other presently available topological descriptors for ML model training, with mean unsigned errors (MUEs) for atomization energies on set-aside test molecules as low as 6 kcal/mol. For inorganic chemistry, our RACs yield 1 kcal/mol ML MUEs on set-aside test molecules in spin-state splitting in comparison to 15-20× higher errors for feature sets that encode whole-molecule structural information. Systematic feature selection methods including univariate filtering, recursive feature elimination, and direct optimization (e.g., random forest and LASSO) are compared. Random-forest- or LASSO-selected subsets 4-5× smaller than the full RAC set produce sub- to 1 kcal/mol spin-splitting MUEs, with good transferability to metal-ligand bond length prediction (0.004-5 Å MUE) and redox potential on a smaller data set (0.2-0.3 eV MUE). Evaluation of feature selection results across property sets reveals the relative importance of local, electronic descriptors (e.g., electronegativity, atomic number) in spin-splitting and distal, steric effects in redox potential and bond lengths.

Diagnostic performance and reproducibility of T2w based and diffusion weighted imaging (DWI) based PI-RADSv2 lexicon descriptors for prostate MRI.

PubMed

Benndorf, Matthias; Hahn, Felix; Krönig, Malte; Jilg, Cordula Annette; Krauss, Tobias; Langer, Mathias; Dovi-Akué, Philippe

2017-08-01

To examine the diagnostic performance of PI-RADSv2 T2w and diffusion weighted imaging (DWI) based lexicon descriptors, inter-observer agreement for descriptor assignment and diagnostic accuracy of the PI-RADSv2 assessment categories for multiparametric prostate MRI. 176 lesions in 79 consecutive patients are analyzed, lesions are histopathologically verified by MRI-ultrasound fusion biopsy. All lesions are rated according to the PI-RADSv2 lexicon, descriptors for T2w and DWI sequences and resulting assessment categories are assigned by two independent blinded radiologists. We perform receiver-operating-characteristic analysis using the assessment categories. To analyze inter-observer agreement, we calculate weighted kappa values for assessment category assignment and unweighted kappa values for descriptor assignment. PI-RADSv2 assessment categories yield an area under the curve of 0.76/0.74 (radiologist 1/radiologist 2), P >0.05. Weighted kappa for agreement is 0.601 in the peripheral zone and 0.580 in the transition zone. We detect a difference in the cancer rate for PI-RADSv2 category 3 between peripheral zone (32%) and transition zone (12%), P <0.05. We obtain moderate agreement at most for descriptor assignment with kappa values ranging from 0.082 (T2w shape in the transition zone) to 0.407 (T2w signal intensity in the peripheral zone) and 0.493 (ADC pattern in the peripheral zone). Our analysis corroborates typical descriptors for benign/malignant lesions, but also reveals insights into potential pitfalls - T2w wedge shaped lesions in the peripheral zone have a considerable cancer rate, despite being labelled category 2 in the lexicon. Agreement for descriptor assignment in the PI-RADSv2 lexicon is at most moderate in our study. Typical descriptors for benign and malignant lesions are validated, whereas the discriminatory power of some descriptors is challenged. The difference in the cancer rate for PI-RADSv2 category 3 between peripheral zone and transition zone should be considered when management recommendations are linked to assessment categories in the future. Copyright © 2017 Elsevier B.V. All rights reserved.

Reduced density gradient as a novel approach for estimating QSAR descriptors, and its application to 1, 4-dihydropyridine derivatives with potential antihypertensive effects.

PubMed

Jardínez, Christiaan; Vela, Alberto; Cruz-Borbolla, Julián; Alvarez-Mendez, Rodrigo J; Alvarado-Rodríguez, José G

2016-12-01

The relationship between the chemical structure and biological activity (log IC 50 ) of 40 derivatives of 1,4-dihydropyridines (DHPs) was studied using density functional theory (DFT) and multiple linear regression analysis methods. With the aim of improving the quantitative structure-activity relationship (QSAR) model, the reduced density gradient s( r) of the optimized equilibrium geometries was used as a descriptor to include weak non-covalent interactions. The QSAR model highlights the correlation between the log IC 50 with highest molecular orbital energy (E HOMO ), molecular volume (V), partition coefficient (log P), non-covalent interactions NCI(H4-G) and the dual descriptor [Δf(r)]. The model yielded values of R 2 =79.57 and Q 2 =69.67 that were validated with the next four internal analytical validations DK=0.076, DQ=-0.006, R P =0.056, and R N =0.000, and the external validation Q 2 boot =64.26. The QSAR model found can be used to estimate biological activity with high reliability in new compounds based on a DHP series. Graphical abstract The good correlation between the log IC 50 with the NCI (H4-G) estimated by the reduced density gradient approach of the DHP derivatives.

Multi-sensor image registration based on algebraic projective invariants.

PubMed

Li, Bin; Wang, Wei; Ye, Hao

2013-04-22

A new automatic feature-based registration algorithm is presented for multi-sensor images with projective deformation. Contours are firstly extracted from both reference and sensed images as basic features in the proposed method. Since it is difficult to design a projective-invariant descriptor from the contour information directly, a new feature named Five Sequential Corners (FSC) is constructed based on the corners detected from the extracted contours. By introducing algebraic projective invariants, we design a descriptor for each FSC that is ensured to be robust against projective deformation. Further, no gray scale related information is required in calculating the descriptor, thus it is also robust against the gray scale discrepancy between the multi-sensor image pairs. Experimental results utilizing real image pairs are presented to show the merits of the proposed registration method.

Molecular properties of steroids involved in their effects on the biophysical state of membranes.

PubMed

Wenz, Jorge J

2015-10-01

The activity of steroids on membranes was studied in relation to their ordering, rigidifying, condensing and/or raft promoting ability. The structures of 82 steroids were modeled by a semi-empirical procedure (AM1) and 245 molecular descriptors were next computed on the optimized energy conformations. Principal component analysis, mean contrasting and logistic regression were used to correlate the molecular properties with 212 cases of documented activities. It was possible to group steroids based on their properties and activities, indicating that steroids having similar molecular properties have similar activities on membranes. Steroids having high values of area, partition coefficient, volume, number of rotatable bonds, molar refractivity, polarizability or mass displayed ordering, rigidifying, condensing and/or raft promoting activity on membranes higher than those steroids having low values in such molecular properties. After a variable selection procedure circumventing correlation problems among descriptors, area and log P were found as the most relevant properties in governing and predicting the activity of steroids on membranes. A logistic regression model as a function of the area and log P of the steroids is proposed, which is able to predict correctly 92.5% of the cases. A rationale of the findings is discussed. Copyright © 2015 Elsevier B.V. All rights reserved.

Antioxidant behavior of mearnsetin and myricetin flavonoid compounds — A DFT study

NASA Astrophysics Data System (ADS)

Sadasivam, K.; Kumaresan, R.

2011-06-01

The molecular characteristics of two naturally occurring flavonoid compounds mearnsetin and myricetin have been computed using density functional theory (DFT) approach with B3LYP/6-311G(d,p) level of theory. The computation and analysis of bond dissociation enthalpy magnitudes for all the OH sites for both the compounds clearly denotes the contribution of the B-ring for the antioxidant activity. The analysis has also indicated the higher values of BDE on the C5-OH radical species in both the compounds. The computed vibrational frequency analysis indicates the absence of imaginary frequency in the neutral as well as radical species of both the flavonoid compounds. The ionisation potential (IP) analysis was found to be within the range of the IP of synthetic food additives. In addition, various molecular descriptors such as electron affinity, hardness, softness, electronegativity, electrophilic index have also been calculated and the validity of Koopman's theorem is verified. The plot of frontier molecular orbital and spin density distribution analysis for neutral and the corresponding radical species for both the compounds have been computed and interpreted. The polar nature and their polarizing capacity are well established through the analysis of dipole moment and polarisability magnitudes.

Analysing and Rationalising Molecular and Materials Databases Using Machine-Learning

NASA Astrophysics Data System (ADS)

de, Sandip; Ceriotti, Michele

Computational materials design promises to greatly accelerate the process of discovering new or more performant materials. Several collaborative efforts are contributing to this goal by building databases of structures, containing between thousands and millions of distinct hypothetical compounds, whose properties are computed by high-throughput electronic-structure calculations. The complexity and sheer amount of information has made manual exploration, interpretation and maintenance of these databases a formidable challenge, making it necessary to resort to automatic analysis tools. Here we will demonstrate how, starting from a measure of (dis)similarity between database items built from a combination of local environment descriptors, it is possible to apply hierarchical clustering algorithms, as well as dimensionality reduction methods such as sketchmap, to analyse, classify and interpret trends in molecular and materials databases, as well as to detect inconsistencies and errors. Thanks to the agnostic and flexible nature of the underlying metric, we will show how our framework can be applied transparently to different kinds of systems ranging from organic molecules and oligopeptides to inorganic crystal structures as well as molecular crystals. Funded by National Center for Computational Design and Discovery of Novel Materials (MARVEL) and Swiss National Science Foundation.

The interaction of flavonoid-lysozyme and the relationship between molecular structure of flavonoids and their binding activity to lysozyme.

PubMed

Yang, Ran; Yu, Lanlan; Zeng, Huajin; Liang, Ruiling; Chen, Xiaolan; Qu, Lingbo

2012-11-01

In this work, the interactions of twelve structurally different flavonoids with Lysozyme (Lys) were studied by fluorescence quenching method. The interaction mechanism and binding properties were investigated. It was found that the binding capacities of flavonoids to Lys were highly depend on the number and position of hydrogen, the kind and position of glycosyl. To explore the selectivity of the bindings of flavonoids with Lys, the structure descriptors of the flavonoids were calculated under QSAR software package of Cerius2, the quantitative relationship between the structures of flavonoids and their binding activities to Lys (QSAR) was performed through genetic function approximation (GFA) regression analysis. The QSAR regression equation was K(A) = 37850.460 + 1630.01Dipole +3038.330HD-171.795MR. (r = 0.858, r(CV)(2) = 0.444, F((11,3)) = 7.48), where K(A) is binding constants, Dipole, HD and MR was dipole moment, number of hydrogen-bond donor and molecular refractivity, respectively. The obtained results make us understand better how the molecular structures influencing their binding to protein which may open up new avenues for the design of the most suitable flavonoids derivatives with structure variants.

Self-Attractive Hartree Decomposition: Partitioning Electron Density into Smooth Localized Fragments.

PubMed

Zhu, Tianyu; de Silva, Piotr; Van Voorhis, Troy

2018-01-09

Chemical bonding plays a central role in the description and understanding of chemistry. Many methods have been proposed to extract information about bonding from quantum chemical calculations, the majority of them resorting to molecular orbitals as basic descriptors. Here, we present a method called self-attractive Hartree (SAH) decomposition to unravel pairs of electrons directly from the electron density, which unlike molecular orbitals is a well-defined observable that can be accessed experimentally. The key idea is to partition the density into a sum of one-electron fragments that simultaneously maximize the self-repulsion and maintain regular shapes. This leads to a set of rather unusual equations in which every electron experiences self-attractive Hartree potential in addition to an external potential common for all the electrons. The resulting symmetry breaking and localization are surprisingly consistent with chemical intuition. SAH decomposition is also shown to be effective in visualization of single/multiple bonds, lone pairs, and unusual bonds due to the smooth nature of fragment densities. Furthermore, we demonstrate that it can be used to identify specific chemical bonds in molecular complexes and provides a simple and accurate electrostatic model of hydrogen bonding.

Is Electronegativity a Useful Descriptor for the "Pseudo-Alkali-Metal" NH4?

DOE Office of Scientific and Technical Information (OSTI.GOV)

Whiteside, Alexander; Xantheas, Sotiris S.; Gutowski, Maciej S.

2011-11-18

Molecular ions in the form of "pseudo-atoms" are common structural motifs in chemistry, with properties that are transferrable between different compounds. We have determined the electronegativity of the "pseudo-alkali metal" ammonium (NH4) and evaluated its reliability as a descriptor in comparison to the electronegativities of the alkali metals. The computed properties of its binary complexes with astatine and of selected borohydrides confirm the similarity of NH4 to the alkali metal atoms, although the electronegativity of NH4 is relatively large in comparison to its cationic radius. We paid particular attention to the molecular properties of ammonium (angular anisotropy, geometric relaxation, andmore » reactivity), which can cause deviations from the behaviour expected of a conceptual "true alkali metal" with this electronegativity. These deviations allow for the discrimination of effects associated with the polyatomic nature of NH4.« less

Quantitative structure-activity relationships of selective antagonists of glucagon receptor using QuaSAR descriptors.

PubMed

Manoj Kumar, Palanivelu; Karthikeyan, Chandrabose; Hari Narayana Moorthy, Narayana Subbiah; Trivedi, Piyush

2006-11-01

In the present paper, quantitative structure activity relationship (QSAR) approach was applied to understand the affinity and selectivity of a novel series of triaryl imidazole derivatives towards glucagon receptor. Statistically significant and highly predictive QSARs were derived for glucagon receptor inhibition by triaryl imidazoles using QuaSAR descriptors of molecular operating environment (MOE) employing computer-assisted multiple regression procedure. The generated QSAR models revealed that factors related to hydrophobicity, molecular shape and geometry predominantly influences glucagon receptor binding affinity of the triaryl imidazoles indicating the relevance of shape specific steric interactions between the molecule and the receptor. Further, QSAR models formulated for selective inhibition of glucagon receptor over p38 mitogen activated protein (MAP) kinase of the compounds in the series highlights that the same structural features, which influence the glucagon receptor affinity, also contribute to their selective inhibition.

Prioritization of in silico models and molecular descriptors for the assessment of ready biodegradability.

PubMed

Fernández, Alberto; Rallo, Robert; Giralt, Francesc

2015-10-01

Ready biodegradability is a key property for evaluating the long-term effects of chemicals on the environment and human health. As such, it is used as a screening test for the assessment of persistent, bioaccumulative and toxic substances. Regulators encourage the use of non-testing methods, such as in silico models, to save money and time. A dataset of 757 chemicals was collected to assess the performance of four freely available in silico models that predict ready biodegradability. They were applied to develop a new consensus method that prioritizes the use of each individual model according to its performance on chemical subsets driven by the presence or absence of different molecular descriptors. This consensus method was capable of almost eliminating unpredictable chemicals, while the performance of combined models was substantially improved with respect to that of the individual models. Copyright © 2015 Elsevier Inc. All rights reserved.

Molecular surface representation using 3D Zernike descriptors for protein shape comparison and docking.

PubMed

Kihara, Daisuke; Sael, Lee; Chikhi, Rayan; Esquivel-Rodriguez, Juan

2011-09-01

The tertiary structures of proteins have been solved in an increasing pace in recent years. To capitalize the enormous efforts paid for accumulating the structure data, efficient and effective computational methods need to be developed for comparing, searching, and investigating interactions of protein structures. We introduce the 3D Zernike descriptor (3DZD), an emerging technique to describe molecular surfaces. The 3DZD is a series expansion of mathematical three-dimensional function, and thus a tertiary structure is represented compactly by a vector of coefficients of terms in the series. A strong advantage of the 3DZD is that it is invariant to rotation of target object to be represented. These two characteristics of the 3DZD allow rapid comparison of surface shapes, which is sufficient for real-time structure database screening. In this article, we review various applications of the 3DZD, which have been recently proposed.

The Development of Novel Chemical Fragment-Based Descriptors Using Frequent Common Subgraph Mining Approach and Their Application in QSAR Modeling.

PubMed

Khashan, Raed; Zheng, Weifan; Tropsha, Alexander

2014-03-01

We present a novel approach to generating fragment-based molecular descriptors. The molecules are represented by labeled undirected chemical graph. Fast Frequent Subgraph Mining (FFSM) is used to find chemical-fragments (subgraphs) that occur in at least a subset of all molecules in a dataset. The collection of frequent subgraphs (FSG) forms a dataset-specific descriptors whose values for each molecule are defined by the number of times each frequent fragment occurs in this molecule. We have employed the FSG descriptors to develop variable selection k Nearest Neighbor (kNN) QSAR models of several datasets with binary target property including Maximum Recommended Therapeutic Dose (MRTD), Salmonella Mutagenicity (Ames Genotoxicity), and P-Glycoprotein (PGP) data. Each dataset was divided into training, test, and validation sets to establish the statistical figures of merit reflecting the model validated predictive power. The classification accuracies of models for both training and test sets for all datasets exceeded 75 %, and the accuracy for the external validation sets exceeded 72 %. The model accuracies were comparable or better than those reported earlier in the literature for the same datasets. Furthermore, the use of fragment-based descriptors affords mechanistic interpretation of validated QSAR models in terms of essential chemical fragments responsible for the compounds' target property. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Local functional descriptors for surface comparison based binding prediction

PubMed Central

2012-01-01

Background Molecular recognition in proteins occurs due to appropriate arrangements of physical, chemical, and geometric properties of an atomic surface. Similar surface regions should create similar binding interfaces. Effective methods for comparing surface regions can be used in identifying similar regions, and to predict interactions without regard to the underlying structural scaffold that creates the surface. Results We present a new descriptor for protein functional surfaces and algorithms for using these descriptors to compare protein surface regions to identify ligand binding interfaces. Our approach uses descriptors of local regions of the surface, and assembles collections of matches to compare larger regions. Our approach uses a variety of physical, chemical, and geometric properties, adaptively weighting these properties as appropriate for different regions of the interface. Our approach builds a classifier based on a training corpus of examples of binding sites of the target ligand. The constructed classifiers can be applied to a query protein providing a probability for each position on the protein that the position is part of a binding interface. We demonstrate the effectiveness of the approach on a number of benchmarks, demonstrating performance that is comparable to the state-of-the-art, with an approach with more generality than these prior methods. Conclusions Local functional descriptors offer a new method for protein surface comparison that is sufficiently flexible to serve in a variety of applications. PMID:23176080

Hapten-antibody recognition studies in competitive immunoassay of α-zearalanol analogs by computational chemistry and Pearson Correlation analysis.

PubMed

Wang, Zhanhui; Luo, Pengjie; Cheng, Linli; Zhang, Suxia; Shen, Jianzhong

2011-01-01

The molecular recognition of hapten-antibody is a fundamental event in competitive immunoassay, which guarantees the sensitivity and specificity of immunoassay for the detection of haptens. The aim of this study is to investigate the correlation between binding ability of one monoclonal antibody, 1H9B4, recognizing and the molecular aspects of α-zearalanol analogs. The mouse-derived monoclonal antibody was produced by using α-zearalanol conjugated to bovine serum albumin as an immunogen. The antibody recognition abilities, expressed as IC(50) values, were determined by a competitive ELISA. All of the hapten molecules were optimized by Density Function Theory (DFT) at B3LYP/ 6-31G* level and the conformation and electrostatic molecular isosurface were employed to explain the molecular recognition between α-zearalanol analogs and antibody 1H9B4. Pearson Correlation analysis between molecular descriptors and IC(50) values was qualitatively undertaken and the results showed that one molecular descriptor, surface of the hapten molecule, clearly demonstrated linear relationship with antibody recognition ability, where the relationship coefficient was 0.88 and the correlation was significant at p < 0.05 level. The study shows that computational chemistry and Pearson Correlation analysis can be used as tool to help the immunochemistries better understand the processing of antibody recognition of hapten molecules in competitive immunoassay. Copyright © 2011 John Wiley & Sons, Ltd.

Synthesis, XRD single crystal structure analysis, vibrational spectral analysis, molecular dynamics and molecular docking studies of 2-(3-methoxy-4-hydroxyphenyl) benzothiazole

NASA Astrophysics Data System (ADS)

Sarau Devi, A.; Aswathy, V. V.; Sheena Mary, Y.; Yohannan Panicker, C.; Armaković, Stevan; Armaković, Sanja J.; Ravindran, Reena; Van Alsenoy, C.

2017-11-01

The vibrational spectra and corresponding vibrational assignments of 2-(3-methoxy-4-hydroxyphenyl)benzothiazole is reported. Single crystal XRD data of the title compound is reported and the orientation of methoxy group is cis to nitrogen atom of the thiazole ring. The phenyl ring breathing modes of the title compound are assigned at 1042 and 731 cm-1 theoretically. The charge transfer within the molecule is studied using frontier molecular orbital analysis. The chemical reactivity descriptors are calculated theoretically. The NMR spectral data predicted theoretically are in good agreement with the experimental data. The strong negative region spread over the phenyl rings, nitrogen atom and oxygen atom of the hydroxyl group in the MEP plot is due to the immense conjugative and hyper conjugative resonance charge delocalization of π-electrons. Molecule sites prone to electrophilic attacks have been determined by analysis of ALIE surfaces, while Fukui functions provided further insight into the local reactivity properties of title molecule. Autoxidation properties have been investigated by calculation of bond dissociation energies (BDEs) of hydrogen abstraction, while BDEs of the rest of the single acyclic bonds were valuable for the further investigation of degradation properties. Calculation of radial distribution functions was performed in order to determine which atoms of the title molecule have pronounced interactions with water molecules. The title compound forms a stable complex with aryl hydrocarbon receptor and can be a lead compound for developing new anti-tumor drug. Antimicrobial properties of the title compound was screened against one bacterial culture Escherchia coli and four fungal cultures viz., Aspergillus niger, Pencillum chrysogenum, Saccharomyces cerevisiae and Rhyzopus stolonifer.

Amp: A modular approach to machine learning in atomistic simulations

NASA Astrophysics Data System (ADS)

Khorshidi, Alireza; Peterson, Andrew A.

2016-10-01

Electronic structure calculations, such as those employing Kohn-Sham density functional theory or ab initio wavefunction theories, have allowed for atomistic-level understandings of a wide variety of phenomena and properties of matter at small scales. However, the computational cost of electronic structure methods drastically increases with length and time scales, which makes these methods difficult for long time-scale molecular dynamics simulations or large-sized systems. Machine-learning techniques can provide accurate potentials that can match the quality of electronic structure calculations, provided sufficient training data. These potentials can then be used to rapidly simulate large and long time-scale phenomena at similar quality to the parent electronic structure approach. Machine-learning potentials usually take a bias-free mathematical form and can be readily developed for a wide variety of systems. Electronic structure calculations have favorable properties-namely that they are noiseless and targeted training data can be produced on-demand-that make them particularly well-suited for machine learning. This paper discusses our modular approach to atomistic machine learning through the development of the open-source Atomistic Machine-learning Package (Amp), which allows for representations of both the total and atom-centered potential energy surface, in both periodic and non-periodic systems. Potentials developed through the atom-centered approach are simultaneously applicable for systems with various sizes. Interpolation can be enhanced by introducing custom descriptors of the local environment. We demonstrate this in the current work for Gaussian-type, bispectrum, and Zernike-type descriptors. Amp has an intuitive and modular structure with an interface through the python scripting language yet has parallelizable fortran components for demanding tasks; it is designed to integrate closely with the widely used Atomic Simulation Environment (ASE), which makes it compatible with a wide variety of commercial and open-source electronic structure codes. We finally demonstrate that the neural network model inside Amp can accurately interpolate electronic structure energies as well as forces of thousands of multi-species atomic systems.

Prediction of blood-brain barrier permeation of α-adrenergic and imidazoline receptor ligands using PAMPA technique and quantitative-structure permeability relationship analysis.

PubMed

Vucicevic, Jelica; Nikolic, Katarina; Dobričić, Vladimir; Agbaba, Danica

2015-02-20

Imidazoline receptor ligands are a numerous family of biologically active compounds known to produce central hypotensive effect by interaction with both α2-adrenoreceptors (α2-AR) and imidazoline receptors (IRs). Recent hypotheses connect those ligands with several neurological disorders. Therefore some IRs ligands are examined as novel centrally acting antihypertensives and drug candidates for treatment of various neurological diseases. Effective Blood-Brain Barrier (BBB) permeability (P(e)) of 18 IRs/α-ARs ligands and 22 Central Nervous System (CNS) drugs was experimentally determined using Parallel Artificial Membrane Permeability Assay (PAMPA) and studied by the Quantitative-Structure-Permeability Relationship (QSPR) methodology. The dominant molecules/cations species of compounds have been calculated at pH = 7.4. The analyzed ligands were optimized using Density Functional Theory (B3LYP/6-31G(d,p)) included in ChemBio3D Ultra 13.0 program and molecule descriptors for optimized compounds were calculated using ChemBio3D Ultra 13.0, Dragon 6.0 and ADMET predictor 6.5 software. Effective permeability of compounds was used as dependent variable (Y), while calculated molecular parametres were used as independent variables (X) in the QSPR study. SIMCA P+ 12.0 was used for Partial Least Square (PLS) analysis, while the stepwise Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN) modeling were performed using STASTICA Neural Networks 4.0. Predictive potential of the formed models was confirmed by Leave-One-Out Cross- and external-validation and the most reliable models were selected. The descriptors that are important for model building are identified as well as their influence on BBB permeability. Results of the QSPR studies could be used as time and cost efficient screening tools for evaluation of BBB permeation of novel α-adrenergic/imidazoline receptor ligands, as promising drug candidates for treatment of hypertension or neurological diseases. Copyright © 2014 Elsevier B.V. All rights reserved.

Molecular orbital studies (hardness, chemical potential, electronegativity and electrophilicity), vibrational spectroscopic investigation and normal coordinate analysis of 5-{1-hydroxy-2-[(propan-2-yl)amino]ethyl}benzene-1,3-diol.

PubMed

Muthu, S; Renuga, S

2014-01-24

FT-IR and FT-Raman spectra of 5-{1-hydroxy-2-[(propan-2-yl) amino] ethyl} benzene-1,3-diol (abbrevi- 54 ated as HPAEBD) were recorded in the region 4000-450 cm(-1) and 4000-100 cm(-1) respectively. The structure of the molecule was optimized and the structural characteristics were determined by density functional theory (B3LYP) and HF method with 6-31 G(d,p) as basis set. The theoretical wave numbers were scaled and compared with experimental FT-IR and FT-Raman spectra. A detailed interpretation of the vibrational spectra of this compound has been made on the basis of the calculated Potential energy distribution (PED). Stability of the molecule arising from hyperconjugation and charge delocalization is confirmed by the natural bond orbital analysis (NBO). The results show that electron density (ED) in the σ antibonding orbitals and E (2) energies confirm the occurrence of intra molecular charge transfer (ICT) within the molecule. The molecule orbital contributions were studied by using the total (TDOS), sum of α and β electron (αβDOS) density of States. Mulliken population analysis of atomic charges is also calculated. The calculated HOMO and LUMO energy gap shows that charge transfer occurs within the molecule. The electron density-based local reactivity descriptors such as Fukui functions were calculated to explain the chemical selectivity or reactivity site in this compound. On the basis of vibrational analyses, the thermodynamic properties of title compound at different temperatures have been calculated. Copyright © 2013 Elsevier B.V. All rights reserved.

Development of QSAR models using artificial neural network analysis for risk assessment of repeated-dose, reproductive, and developmental toxicities of cosmetic ingredients.

PubMed

Hisaki, Tomoka; Aiba Née Kaneko, Maki; Yamaguchi, Masahiko; Sasa, Hitoshi; Kouzuki, Hirokazu

2015-04-01

Use of laboratory animals for systemic toxicity testing is subject to strong ethical and regulatory constraints, but few alternatives are yet available. One possible approach to predict systemic toxicity of chemicals in the absence of experimental data is quantitative structure-activity relationship (QSAR) analysis. Here, we present QSAR models for prediction of maximum "no observed effect level" (NOEL) for repeated-dose, developmental and reproductive toxicities. NOEL values of 421 chemicals for repeated-dose toxicity, 315 for reproductive toxicity, and 156 for developmental toxicity were collected from Japan Existing Chemical Data Base (JECDB). Descriptors to predict toxicity were selected based on molecular orbital (MO) calculations, and QSAR models employing multiple independent descriptors as the input layer of an artificial neural network (ANN) were constructed to predict NOEL values. Robustness of the models was indicated by the root-mean-square (RMS) errors after 10-fold cross-validation (0.529 for repeated-dose, 0.508 for reproductive, and 0.558 for developmental toxicity). Evaluation of the models in terms of the percentages of predicted NOELs falling within factors of 2, 5 and 10 of the in-vivo-determined NOELs suggested that the model is applicable to both general chemicals and the subset of chemicals listed in International Nomenclature of Cosmetic Ingredients (INCI). Our results indicate that ANN models using in silico parameters have useful predictive performance, and should contribute to integrated risk assessment of systemic toxicity using a weight-of-evidence approach. Availability of predicted NOELs will allow calculation of the margin of safety, as recommended by the Scientific Committee on Consumer Safety (SCCS).

Beware of external validation! - A Comparative Study of Several Validation Techniques used in QSAR Modelling.

PubMed

Majumdar, Subhabrata; Basak, Subhash C

2018-04-26

Proper validation is an important aspect of QSAR modelling. External validation is one of the widely used validation methods in QSAR where the model is built on a subset of the data and validated on the rest of the samples. However, its effectiveness for datasets with a small number of samples but large number of predictors remains suspect. Calculating hundreds or thousands of molecular descriptors using currently available software has become the norm in QSAR research, owing to computational advances in the past few decades. Thus, for n chemical compounds and p descriptors calculated for each molecule, the typical chemometric dataset today has high value of p but small n (i.e. n < p). Motivated by the evidence of inadequacies of external validation in estimating the true predictive capability of a statistical model in recent literature, this paper performs an extensive and comparative study of this method with several other validation techniques. We compared four validation methods: leave-one-out, K-fold, external and multi-split validation, using statistical models built using the LASSO regression, which simultaneously performs variable selection and modelling. We used 300 simulated datasets and one real dataset of 95 congeneric amine mutagens for this evaluation. External validation metrics have high variation among different random splits of the data, hence are not recommended for predictive QSAR models. LOO has the overall best performance among all validation methods applied in our scenario. Results from external validation are too unstable for the datasets we analyzed. Based on our findings, we recommend using the LOO procedure for validating QSAR predictive models built on high-dimensional small-sample data. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Learning structure-property relationship in crystalline materials: A study of lanthanide-transition metal alloys

NASA Astrophysics Data System (ADS)

Pham, Tien-Lam; Nguyen, Nguyen-Duong; Nguyen, Van-Doan; Kino, Hiori; Miyake, Takashi; Dam, Hieu-Chi

2018-05-01

We have developed a descriptor named Orbital Field Matrix (OFM) for representing material structures in datasets of multi-element materials. The descriptor is based on the information regarding atomic valence shell electrons and their coordination. In this work, we develop an extension of OFM called OFM1. We have shown that these descriptors are highly applicable in predicting the physical properties of materials and in providing insights on the materials space by mapping into a low embedded dimensional space. Our experiments with transition metal/lanthanide metal alloys show that the local magnetic moments and formation energies can be accurately reproduced using simple nearest-neighbor regression, thus confirming the relevance of our descriptors. Using kernel ridge regressions, we could accurately reproduce formation energies and local magnetic moments calculated based on first-principles, with mean absolute errors of 0.03 μB and 0.10 eV/atom, respectively. We show that meaningful low-dimensional representations can be extracted from the original descriptor using descriptive learning algorithms. Intuitive prehension on the materials space, qualitative evaluation on the similarities in local structures or crystalline materials, and inference in the designing of new materials by element substitution can be performed effectively based on these low-dimensional representations.

Deconstructing field-induced ketene isomerization through Lagrangian descriptors.

PubMed

Craven, Galen T; Hernandez, Rigoberto

2016-02-07

The time-dependent geometrical separatrices governing state transitions in field-induced ketene isomerization are constructed using the method of Lagrangian descriptors. We obtain the stable and unstable manifolds of time-varying transition states as dynamic phase space objects governing configurational changes when the ketene molecule is subjected to an oscillating electric field. The dynamics of the isomerization reaction are modeled through classical trajectory studies on the Gezelter-Miller potential energy surface and an approximate dipole moment model which is coupled to a time-dependent electric field. We obtain a representation of the reaction geometry, over varying field strengths and oscillation frequencies, by partitioning an initial phase space into basins labeled according to which product state is reached at a given time. The borders between these basins are in agreement with those obtained using Lagrangian descriptors, even in regimes exhibiting chaotic dynamics. Major outcomes of this work are: validation and extension of a transition state theory framework built from Lagrangian descriptors, elaboration of the applicability for this theory to periodically- and aperiodically-driven molecular systems, and prediction of regimes in which isomerization of ketene and its derivatives may be controlled using an external field.

The proposal of architecture for chemical splitting to optimize QSAR models for aquatic toxicity.

PubMed

Colombo, Andrea; Benfenati, Emilio; Karelson, Mati; Maran, Uko

2008-06-01

One of the challenges in the field of quantitative structure-activity relationship (QSAR) analysis is the correct classification of a chemical compound to an appropriate model for the prediction of activity. Thus, in previous studies, compounds have been divided into distinct groups according to their mode of action or chemical class. In the current study, theoretical molecular descriptors were used to divide 568 organic substances into subsets with toxicity measured for the 96-h lethal median concentration for the Fathead minnow (Pimephales promelas). Simple constitutional descriptors such as the number of aliphatic and aromatic rings and a quantum chemical descriptor, maximum bond order of a carbon atom divide compounds into nine subsets. For each subset of compounds the automatic forward selection of descriptors was applied to construct QSAR models. Significant correlations were achieved for each subset of chemicals and all models were validated with the leave-one-out internal validation procedure (R(2)(cv) approximately 0.80). The results encourage to consider this alternative way for the prediction of toxicity using QSAR subset models without direct reference to the mechanism of toxic action or the traditional chemical classification.

Essential Set of Molecular Descriptors for ADME Prediction in Drug and Environmental Chemical Space

EPA Science Inventory

Historically, the disciplines of pharmacology and toxicology have embraced quantitative structure-activity relationships (QSAR) and quantitative structure-property relationships (QSPR) to predict ADME properties or biological activities of untested chemicals. The question arises ...

Prediction of solvation enthalpy of gaseous organic compounds in propanol

NASA Astrophysics Data System (ADS)

Golmohammadi, Hassan; Dashtbozorgi, Zahra

2016-09-01

The purpose of this paper is to present a novel way for developing quantitative structure-property relationship (QSPR) models to predict the gas-to-propanol solvation enthalpy (Δ H solv) of 95 organic compounds. Different kinds of descriptors were calculated for each compound using the Dragon software package. The variable selection technique of replacement method (RM) was employed to select the optimal subset of solute descriptors. Our investigation reveals that the dependence of physical chemistry properties of solution on solvation enthalpy is nonlinear and that the RM method is unable to model the solvation enthalpy accurately. The results established that the calculated Δ H solv values by SVM were in good agreement with the experimental ones, and the performances of the SVM models were superior to those obtained by RM model.

A chirality-based metrics for free-energy calculations in biomolecular systems.

PubMed

Pietropaolo, Adriana; Branduardi, Davide; Bonomi, Massimiliano; Parrinello, Michele

2011-09-01

In this work, we exploit the chirality index introduced in (Pietropaolo et al., Proteins 2008, 70, 667) as an effective descriptor of the secondary structure of proteins to explore their complex free-energy landscape. We use the chirality index as an alternative metrics in the path collective variables (PCVs) framework and we show in the prototypical case of the C-terminal domain of immunoglobulin binding protein GB1 that relevant configurations can be efficiently sampled in combination with well-tempered metadynamics. While the projections of the configurations found onto a variety of different descriptors are fully consistent with previously reported calculations, this approach provides a unifying perspective of the folding mechanism which was not possible using metadynamics with the previous formulation of PCVs. Copyright © 2011 Wiley Periodicals, Inc.

Registration algorithm of point clouds based on multiscale normal features

NASA Astrophysics Data System (ADS)

Lu, Jun; Peng, Zhongtao; Su, Hang; Xia, GuiHua

2015-01-01

The point cloud registration technology for obtaining a three-dimensional digital model is widely applied in many areas. To improve the accuracy and speed of point cloud registration, a registration method based on multiscale normal vectors is proposed. The proposed registration method mainly includes three parts: the selection of key points, the calculation of feature descriptors, and the determining and optimization of correspondences. First, key points are selected from the point cloud based on the changes of magnitude of multiscale curvatures obtained by using principal components analysis. Then the feature descriptor of each key point is proposed, which consists of 21 elements based on multiscale normal vectors and curvatures. The correspondences in a pair of two point clouds are determined according to the descriptor's similarity of key points in the source point cloud and target point cloud. Correspondences are optimized by using a random sampling consistency algorithm and clustering technology. Finally, singular value decomposition is applied to optimized correspondences so that the rigid transformation matrix between two point clouds is obtained. Experimental results show that the proposed point cloud registration algorithm has a faster calculation speed, higher registration accuracy, and better antinoise performance.

Quantitative structure-activity relationships of the antimalarial agent artemisinin and some of its derivatives - a DFT approach.

PubMed

Rajkhowa, Sanchaita; Hussain, Iftikar; Hazarika, Kalyan K; Sarmah, Pubalee; Deka, Ramesh Chandra

2013-09-01

Artemisinin form the most important class of antimalarial agents currently available, and is a unique sesquiterpene peroxide occurring as a constituent of Artemisia annua. Artemisinin is effectively used in the treatment of drug-resistant Plasmodium falciparum and because of its rapid clearance of cerebral malaria, many clinically useful semisynthetic drugs for severe and complicated malaria have been developed. However, one of the major disadvantages of using artemisinins is their poor solubility either in oil or water and therefore, in order to overcome this difficulty many derivatives of artemisinin were prepared. A comparative study on the chemical reactivity of artemisinin and some of its derivatives is performed using density functional theory (DFT) calculations. DFT based global and local reactivity descriptors, such as hardness, chemical potential, electrophilicity index, Fukui function, and local philicity calculated at the optimized geometries are used to investigate the usefulness of these descriptors for understanding the reactive nature and reactive sites of the molecules. Multiple regression analysis is applied to build up a quantitative structure-activity relationship (QSAR) model based on the DFT based descriptors against the chloroquine-resistant, mefloquine-sensitive Plasmodium falciparum W-2 clone.

Towards interoperable and reproducible QSAR analyses: Exchange of datasets.

PubMed

Spjuth, Ola; Willighagen, Egon L; Guha, Rajarshi; Eklund, Martin; Wikberg, Jarl Es

2010-06-30

QSAR is a widely used method to relate chemical structures to responses or properties based on experimental observations. Much effort has been made to evaluate and validate the statistical modeling in QSAR, but these analyses treat the dataset as fixed. An overlooked but highly important issue is the validation of the setup of the dataset, which comprises addition of chemical structures as well as selection of descriptors and software implementations prior to calculations. This process is hampered by the lack of standards and exchange formats in the field, making it virtually impossible to reproduce and validate analyses and drastically constrain collaborations and re-use of data. We present a step towards standardizing QSAR analyses by defining interoperable and reproducible QSAR datasets, consisting of an open XML format (QSAR-ML) which builds on an open and extensible descriptor ontology. The ontology provides an extensible way of uniquely defining descriptors for use in QSAR experiments, and the exchange format supports multiple versioned implementations of these descriptors. Hence, a dataset described by QSAR-ML makes its setup completely reproducible. We also provide a reference implementation as a set of plugins for Bioclipse which simplifies setup of QSAR datasets, and allows for exporting in QSAR-ML as well as old-fashioned CSV formats. The implementation facilitates addition of new descriptor implementations from locally installed software and remote Web services; the latter is demonstrated with REST and XMPP Web services. Standardized QSAR datasets open up new ways to store, query, and exchange data for subsequent analyses. QSAR-ML supports completely reproducible creation of datasets, solving the problems of defining which software components were used and their versions, and the descriptor ontology eliminates confusions regarding descriptors by defining them crisply. This makes is easy to join, extend, combine datasets and hence work collectively, but also allows for analyzing the effect descriptors have on the statistical model's performance. The presented Bioclipse plugins equip scientists with graphical tools that make QSAR-ML easily accessible for the community.

Towards interoperable and reproducible QSAR analyses: Exchange of datasets

PubMed Central

2010-01-01

Background QSAR is a widely used method to relate chemical structures to responses or properties based on experimental observations. Much effort has been made to evaluate and validate the statistical modeling in QSAR, but these analyses treat the dataset as fixed. An overlooked but highly important issue is the validation of the setup of the dataset, which comprises addition of chemical structures as well as selection of descriptors and software implementations prior to calculations. This process is hampered by the lack of standards and exchange formats in the field, making it virtually impossible to reproduce and validate analyses and drastically constrain collaborations and re-use of data. Results We present a step towards standardizing QSAR analyses by defining interoperable and reproducible QSAR datasets, consisting of an open XML format (QSAR-ML) which builds on an open and extensible descriptor ontology. The ontology provides an extensible way of uniquely defining descriptors for use in QSAR experiments, and the exchange format supports multiple versioned implementations of these descriptors. Hence, a dataset described by QSAR-ML makes its setup completely reproducible. We also provide a reference implementation as a set of plugins for Bioclipse which simplifies setup of QSAR datasets, and allows for exporting in QSAR-ML as well as old-fashioned CSV formats. The implementation facilitates addition of new descriptor implementations from locally installed software and remote Web services; the latter is demonstrated with REST and XMPP Web services. Conclusions Standardized QSAR datasets open up new ways to store, query, and exchange data for subsequent analyses. QSAR-ML supports completely reproducible creation of datasets, solving the problems of defining which software components were used and their versions, and the descriptor ontology eliminates confusions regarding descriptors by defining them crisply. This makes is easy to join, extend, combine datasets and hence work collectively, but also allows for analyzing the effect descriptors have on the statistical model's performance. The presented Bioclipse plugins equip scientists with graphical tools that make QSAR-ML easily accessible for the community. PMID:20591161

Assessment of conformational, spectral, antimicrobial activity, chemical reactivity and NLO application of Pyrrole-2,5-dicarboxaldehyde bis(oxaloyldihydrazone).

PubMed

Rawat, Poonam; Singh, R N

2015-04-05

An orange colored pyrrole dihydrazone: Pyrrole-2,5-dicarboxaldehyde bis(oxaloyldihydrazone) (PDBO) has been synthesized by reaction of oxalic acid dihydrazide with 2,5 diformyl-1H-pyrrole and has been characterized by spectroscopic analysis (1H, 13C NMR, UV-visible, FT-IR and DART Mass). The properties of the compound has been evaluated using B3LYP functional and 6-31G(d,p)/6-311+G(d,p) basis set. The symmetric (3319, 3320 cm(-1)) and asymmetric (3389, 3382 cm(-1)) stretching wave number confirm free NH2 groups in PDBO. NBO analysis shows, inter/intra molecular interactions within the molecule. Topological parameters have been analyzed by QTAIM theory and provide the existence of intramolecular hydrogen bonding (N-H⋯O). The local reactivity descriptors analyses determine the reactive sites within molecule. The calculated first hyperpolarizability value (β0=23.83×10(-30) esu) of pyrrole dihydrazone shows its suitability for non-linear optical (NLO) response. The preliminary bioassay suggested that the PDBO exhibits relatively good antibacterial and fungicidal activity against Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus, Streptococcus pyogenes, Candida albicans, Aspergillus niger. The local reactivity descriptors--Fukui functions (fk+, fk-), local softnesses (sk+, sk-) and electrophilicity indices (ωk+, ωk-) analyses have been used to determine the reactive sites within molecule. Copyright © 2014 Elsevier B.V. All rights reserved.

Assessment of conformational, spectral, antimicrobial activity, chemical reactivity and NLO application of Pyrrole-2,5-dicarboxaldehyde bis(oxaloyldihydrazone)

NASA Astrophysics Data System (ADS)

Rawat, Poonam; Singh, R. N.

2015-04-01

An orange colored pyrrole dihydrazone: Pyrrole-2,5-dicarboxaldehyde bis(oxaloyldihydrazone) (PDBO) has been synthesized by reaction of oxalic acid dihydrazide with 2,5 diformyl-1H-pyrrole and has been characterized by spectroscopic analysis (1H, 13C NMR, UV-visible, FT-IR and DART Mass). The properties of the compound has been evaluated using B3LYP functional and 6-31G(d,p)/6-311+G(d,p) basis set. The symmetric (3319, 3320 cm-1) and asymmetric (3389, 3382 cm-1) stretching wave number confirm free NH2 groups in PDBO. NBO analysis shows, inter/intra molecular interactions within the molecule. Topological parameters have been analyzed by QTAIM theory and provide the existence of intramolecular hydrogen bonding (N-H⋯O). The local reactivity descriptors analyses determine the reactive sites within molecule. The calculated first hyperpolarizability value (β0 = 23.83 × 10-30 esu) of pyrrole dihydrazone shows its suitability for non-linear optical (NLO) response. The preliminary bioassay suggested that the PDBO exhibits relatively good antibacterial and fungicidal activity against Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus, Streptococcus pyogenes, Candida albicans, Aspergillus niger. The local reactivity descriptors - Fukui functions (fk+, fk-), local softnesses (sk+, sk-) and electrophilicity indices (ωk+, ωk-) analyses have been used to determine the reactive sites within molecule.

Application of 3D-QSAR in the rational design of receptor ligands and enzyme inhibitors.

PubMed

Mor, Marco; Rivara, Silvia; Lodola, Alessio; Lorenzi, Simone; Bordi, Fabrizio; Plazzi, Pier Vincenzo; Spadoni, Gilberto; Bedini, Annalida; Duranti, Andrea; Tontini, Andrea; Tarzia, Giorgio

2005-11-01

Quantitative structure-activity relationships (QSARs) are frequently employed in medicinal chemistry projects, both to rationalize structure-activity relationships (SAR) for known series of compounds and to help in the design of innovative structures endowed with desired pharmacological actions. As a difference from the so-called structure-based drug design tools, they do not require the knowledge of the biological target structure, but are based on the comparison of drug structural features, thus being defined ligand-based drug design tools. In the 3D-QSAR approach, structural descriptors are calculated from molecular models of the ligands, as interaction fields within a three-dimensional (3D) lattice of points surrounding the ligand structure. These descriptors are collected in a large X matrix, which is submitted to multivariate analysis to look for correlations with biological activity. Like for other QSARs, the reliability and usefulness of the correlation models depends on the validity of the assumptions and on the quality of the data. A careful selection of compounds and pharmacological data can improve the application of 3D-QSAR analysis in drug design. Some examples of the application of CoMFA and CoMSIA approaches to the SAR study and design of receptor or enzyme ligands is described, pointing the attention to the fields of melatonin receptor ligands and FAAH inhibitors.

LQTA-QSAR: a new 4D-QSAR methodology.

PubMed

Martins, João Paulo A; Barbosa, Euzébio G; Pasqualoto, Kerly F M; Ferreira, Márcia M C

2009-06-01

A novel 4D-QSAR approach which makes use of the molecular dynamics (MD) trajectories and topology information retrieved from the GROMACS package is presented in this study. This new methodology, named LQTA-QSAR (LQTA, Laboratório de Quimiometria Teórica e Aplicada), has a module (LQTAgrid) that calculates intermolecular interaction energies at each grid point considering probes and all aligned conformations resulting from MD simulations. These interaction energies are the independent variables or descriptors employed in a QSAR analysis. The comparison of the proposed methodology to other 4D-QSAR and CoMFA formalisms was performed using a set of forty-seven glycogen phosphorylase b inhibitors (data set 1) and a set of forty-four MAP p38 kinase inhibitors (data set 2). The QSAR models for both data sets were built using the ordered predictor selection (OPS) algorithm for variable selection. Model validation was carried out applying y-randomization and leave-N-out cross-validation in addition to the external validation. PLS models for data set 1 and 2 provided the following statistics: q(2) = 0.72, r(2) = 0.81 for 12 variables selected and 2 latent variables and q(2) = 0.82, r(2) = 0.90 for 10 variables selected and 5 latent variables, respectively. Visualization of the descriptors in 3D space was successfully interpreted from the chemical point of view, supporting the applicability of this new approach in rational drug design.

Size-independent neural networks based first-principles method for accurate prediction of heat of formation of fuels

NASA Astrophysics Data System (ADS)

Yang, GuanYa; Wu, Jiang; Chen, ShuGuang; Zhou, WeiJun; Sun, Jian; Chen, GuanHua

2018-06-01

Neural network-based first-principles method for predicting heat of formation (HOF) was previously demonstrated to be able to achieve chemical accuracy in a broad spectrum of target molecules [L. H. Hu et al., J. Chem. Phys. 119, 11501 (2003)]. However, its accuracy deteriorates with the increase in molecular size. A closer inspection reveals a systematic correlation between the prediction error and the molecular size, which appears correctable by further statistical analysis, calling for a more sophisticated machine learning algorithm. Despite the apparent difference between simple and complex molecules, all the essential physical information is already present in a carefully selected set of small molecule representatives. A model that can capture the fundamental physics would be able to predict large and complex molecules from information extracted only from a small molecules database. To this end, a size-independent, multi-step multi-variable linear regression-neural network-B3LYP method is developed in this work, which successfully improves the overall prediction accuracy by training with smaller molecules only. And in particular, the calculation errors for larger molecules are drastically reduced to the same magnitudes as those of the smaller molecules. Specifically, the method is based on a 164-molecule database that consists of molecules made of hydrogen and carbon elements. 4 molecular descriptors were selected to encode molecule's characteristics, among which raw HOF calculated from B3LYP and the molecular size are also included. Upon the size-independent machine learning correction, the mean absolute deviation (MAD) of the B3LYP/6-311+G(3df,2p)-calculated HOF is reduced from 16.58 to 1.43 kcal/mol and from 17.33 to 1.69 kcal/mol for the training and testing sets (small molecules), respectively. Furthermore, the MAD of the testing set (large molecules) is reduced from 28.75 to 1.67 kcal/mol.

String method for calculation of minimum free-energy paths in Cartesian space in freely-tumbling systems.

PubMed

Branduardi, Davide; Faraldo-Gómez, José D

2013-09-10

The string method is a molecular-simulation technique that aims to calculate the minimum free-energy path of a chemical reaction or conformational transition, in the space of a pre-defined set of reaction coordinates that is typically highly dimensional. Any descriptor may be used as a reaction coordinate, but arguably the Cartesian coordinates of the atoms involved are the most unprejudiced and intuitive choice. Cartesian coordinates, however, present a non-trivial problem, in that they are not invariant to rigid-body molecular rotations and translations, which ideally ought to be unrestricted in the simulations. To overcome this difficulty, we reformulate the framework of the string method to integrate an on-the-fly structural-alignment algorithm. This approach, referred to as SOMA (String method with Optimal Molecular Alignment), enables the use of Cartesian reaction coordinates in freely tumbling molecular systems. In addition, this scheme permits the dissection of the free-energy change along the most probable path into individual atomic contributions, thus revealing the dominant mechanism of the simulated process. This detailed analysis also provides a physically-meaningful criterion to coarse-grain the representation of the path. To demonstrate the accuracy of the method we analyze the isomerization of the alanine dipeptide in vacuum and the chair-to-inverted-chair transition of β -D mannose in explicit water. Notwithstanding the simplicity of these systems, the SOMA approach reveals novel insights into the atomic mechanism of these isomerizations. In both cases, we find that the dynamics and the energetics of these processes are controlled by interactions involving only a handful of atoms in each molecule. Consistent with this result, we show that a coarse-grained SOMA calculation defined in terms of these subsets of atoms yields nearidentical minimum free-energy paths and committor distributions to those obtained via a highly-dimensional string.

String method for calculation of minimum free-energy paths in Cartesian space in freely-tumbling systems

PubMed Central

Branduardi, Davide; Faraldo-Gómez, José D.

2014-01-01

The string method is a molecular-simulation technique that aims to calculate the minimum free-energy path of a chemical reaction or conformational transition, in the space of a pre-defined set of reaction coordinates that is typically highly dimensional. Any descriptor may be used as a reaction coordinate, but arguably the Cartesian coordinates of the atoms involved are the most unprejudiced and intuitive choice. Cartesian coordinates, however, present a non-trivial problem, in that they are not invariant to rigid-body molecular rotations and translations, which ideally ought to be unrestricted in the simulations. To overcome this difficulty, we reformulate the framework of the string method to integrate an on-the-fly structural-alignment algorithm. This approach, referred to as SOMA (String method with Optimal Molecular Alignment), enables the use of Cartesian reaction coordinates in freely tumbling molecular systems. In addition, this scheme permits the dissection of the free-energy change along the most probable path into individual atomic contributions, thus revealing the dominant mechanism of the simulated process. This detailed analysis also provides a physically-meaningful criterion to coarse-grain the representation of the path. To demonstrate the accuracy of the method we analyze the isomerization of the alanine dipeptide in vacuum and the chair-to-inverted-chair transition of β-D mannose in explicit water. Notwithstanding the simplicity of these systems, the SOMA approach reveals novel insights into the atomic mechanism of these isomerizations. In both cases, we find that the dynamics and the energetics of these processes are controlled by interactions involving only a handful of atoms in each molecule. Consistent with this result, we show that a coarse-grained SOMA calculation defined in terms of these subsets of atoms yields nearidentical minimum free-energy paths and committor distributions to those obtained via a highly-dimensional string. PMID:24729762

Gibbs Free Energy of Hydrolytic Water Molecule in Acyl-Enzyme Intermediates of a Serine Protease: A Potential Application for Computer-Aided Discovery of Mechanism-Based Reversible Covalent Inhibitors.

PubMed

Masuda, Yosuke; Yamaotsu, Noriyuki; Hirono, Shuichi

2017-01-01

In order to predict the potencies of mechanism-based reversible covalent inhibitors, the relationships between calculated Gibbs free energy of hydrolytic water molecule in acyl-trypsin intermediates and experimentally measured catalytic rate constants (k cat ) were investigated. After obtaining representative solution structures by molecular dynamics (MD) simulations, hydration thermodynamics analyses using WaterMap™ were conducted. Consequently, we found for the first time that when Gibbs free energy of the hydrolytic water molecule was lower, logarithms of k cat were also lower. The hydrolytic water molecule with favorable Gibbs free energy may hydrolyze acylated serine slowly. Gibbs free energy of hydrolytic water molecule might be a useful descriptor for computer-aided discovery of mechanism-based reversible covalent inhibitors of hydrolytic enzymes.

QSPR using MOLGEN-QSPR: the challenge of fluoroalkane boiling points.

PubMed

Rücker, Christoph; Meringer, Markus; Kerber, Adalbert

2005-01-01

By means of the new software MOLGEN-QSPR, a multilinear regression model for the boiling points of lower fluoroalkanes is established. The model is based exclusively on simple descriptors derived directly from molecular structure and nevertheless describes a broader set of data more precisely than previous attempts that used either more demanding (quantum chemical) descriptors or more demanding (nonlinear) statistical methods such as neural networks. The model's internal consistency was confirmed by leave-one-out cross-validation. The model was used to predict all unknown boiling points of fluorobutanes, and the quality of predictions was estimated by means of comparison with boiling point predictions for fluoropentanes.

Towards accurate free energy calculations in ligand protein-binding studies.

PubMed

Steinbrecher, Thomas; Labahn, Andreas

2010-01-01

Cells contain a multitude of different chemical reaction paths running simultaneously and quite independently next to each other. This amazing feat is enabled by molecular recognition, the ability of biomolecules to form stable and specific complexes with each other and with their substrates. A better understanding of this process, i.e. of the kinetics, structures and thermodynamic properties of biomolecule binding, would be invaluable in the study of biological systems. In addition, as the mode of action of many pharmaceuticals is based upon their inhibition or activation of biomolecule targets, predictive models of small molecule receptor binding are very helpful tools in rational drug design. Since the goal here is normally to design a new compound with a high inhibition strength, one of the most important thermodynamic properties is the binding free energy DeltaG(0). The prediction of binding constants has always been one of the major goals in the field of computational chemistry, because the ability to reliably assess a hypothetical compound's binding properties without having to synthesize it first would save a tremendous amount of work. The different approaches to this question range from fast and simple empirical descriptor methods to elaborate simulation protocols aimed at putting the computation of free energies onto a solid foundation of statistical thermodynamics. While the later methods are still not suited for the screenings of thousands of compounds that are routinely performed in computational drug design studies, they are increasingly put to use for the detailed study of protein ligand interactions. This review will focus on molecular mechanics force field based free energy calculations and their application to the study of protein ligand interactions. After a brief overview of other popular methods for the calculation of free energies, we will describe recent advances in methodology and a variety of exemplary studies of molecular dynamics simulation based free energy calculations.

Kinetic rate constant prediction supports the conformational selection mechanism of protein binding.

PubMed

Moal, Iain H; Bates, Paul A

2012-01-01

The prediction of protein-protein kinetic rate constants provides a fundamental test of our understanding of molecular recognition, and will play an important role in the modeling of complex biological systems. In this paper, a feature selection and regression algorithm is applied to mine a large set of molecular descriptors and construct simple models for association and dissociation rate constants using empirical data. Using separate test data for validation, the predicted rate constants can be combined to calculate binding affinity with accuracy matching that of state of the art empirical free energy functions. The models show that the rate of association is linearly related to the proportion of unbound proteins in the bound conformational ensemble relative to the unbound conformational ensemble, indicating that the binding partners must adopt a geometry near to that of the bound prior to binding. Mirroring the conformational selection and population shift mechanism of protein binding, the models provide a strong separate line of evidence for the preponderance of this mechanism in protein-protein binding, complementing structural and theoretical studies.

Can we apply the MRI BI-RADS lexicon morphology descriptors on contrast-enhanced spectral mammography?

PubMed

Kamal, Rasha M; Helal, Maha H; Mansour, Sahar M; Haggag, Marwa A; Nada, Omniya M; Farahat, Iman G; Alieldin, Nelly H

2016-07-12

To assess the feasibility of using the MRI breast imaging reporting and data system (BI-RADS) lexicon morphology descriptors to characterize enhancing breast lesions identified on contrast-enhanced spectral mammography (CESM). The study is a retrospective analysis of the morphology descriptors of 261 enhancing breast lesions identified on CESM in 239 patients. We presented the morphological categorization of the included lesions into focus, mass and non-mass. Further classifications included (1) the multiplicity for "focus" category, (2) the shape, margin and internal enhancement for "mass" category and (3) the distribution and internal enhancement for "non-mass" category. Each morphology descriptor was evaluated individually (irrespective of all other descriptors) by calculating its sensitivity, specificity, positive-predictive value (PPV) and negative-predictive value (NPV) and likelihood ratios (LRs). The study included 68/261 (26.1%) benign lesions and 193/261 (73.9%) malignant lesions. Intensely enhancing foci, whether single (7/12, 58.3%) or multiple (2/12, 16.7%), were malignant. Descriptors of "irregular"-shape (PPV: 92.4%) and "non-circumscribed" margin (odds ratio: 55.2, LR positive: 4.77; p-value: <0.001) were more compatible with malignancy. Internal mass enhancement patterns showed a very low specificity (58.0%) and NPV (40.0%). Non-mass enhancement (NME) was detected in 81/261 lesions. Asymmetrical NME in 81% (n = 52/81) lesions was malignant lesions and internal enhancement patterns indicative of malignancy were the heterogeneous and clumped ones. We can apply the MRI morphology descriptors to characterize lesions on CESM, but with few expectations. In many situations, irregular-shaped, non-circumscribed masses and NME with focal, ductal or segmental distribution and heterogeneous or clumped enhancement are the most suggestive descriptors of malignant pathologies. (1) The MRI BI-RADS lexicon morphology descriptors can be applied in the characterization of enhancing lesions on CESM with a few exceptions. (2) Multiple bilateral intensely enhancing foci should not be included under the normal background parenchymal enhancement unless they are proved to be benign by biopsy. (3) Mass lesion features that indicated malignancy were irregular-shaped, spiculated and irregular margins and heterogeneous internal enhancement patterns. The rim enhancement pattern should not be considered as a descriptor of malignant lesions unless CESM is coupled with an ultrasound examination.

Can we apply the MRI BI-RADS lexicon morphology descriptors on contrast-enhanced spectral mammography?

PubMed Central

Kamal, Rasha M; Helal, Maha H; Haggag, Marwa A; Nada, Omniya M; Farahat, Iman G; Alieldin, Nelly H

2016-01-01

Objective: To assess the feasibility of using the MRI breast imaging reporting and data system (BI-RADS) lexicon morphology descriptors to characterize enhancing breast lesions identified on contrast-enhanced spectral mammography (CESM). Methods: The study is a retrospective analysis of the morphology descriptors of 261 enhancing breast lesions identified on CESM in 239 patients. We presented the morphological categorization of the included lesions into focus, mass and non-mass. Further classifications included (1) the multiplicity for “focus” category, (2) the shape, margin and internal enhancement for “mass” category and (3) the distribution and internal enhancement for “non-mass” category. Each morphology descriptor was evaluated individually (irrespective of all other descriptors) by calculating its sensitivity, specificity, positive-predictive value (PPV) and negative-predictive value (NPV) and likelihood ratios (LRs). Results: The study included 68/261 (26.1%) benign lesions and 193/261 (73.9%) malignant lesions. Intensely enhancing foci, whether single (7/12, 58.3%) or multiple (2/12, 16.7%), were malignant. Descriptors of “irregular”-shape (PPV: 92.4%) and “non-circumscribed” margin (odds ratio: 55.2, LR positive: 4.77; p-value: <0.001) were more compatible with malignancy. Internal mass enhancement patterns showed a very low specificity (58.0%) and NPV (40.0%). Non-mass enhancement (NME) was detected in 81/261 lesions. Asymmetrical NME in 81% (n = 52/81) lesions was malignant lesions and internal enhancement patterns indicative of malignancy were the heterogeneous and clumped ones. Conclusion: We can apply the MRI morphology descriptors to characterize lesions on CESM, but with few expectations. In many situations, irregular-shaped, non-circumscribed masses and NME with focal, ductal or segmental distribution and heterogeneous or clumped enhancement are the most suggestive descriptors of malignant pathologies. Advances in knowledge: (1) The MRI BI-RADS lexicon morphology descriptors can be applied in the characterization of enhancing lesions on CESM with a few exceptions. (2) Multiple bilateral intensely enhancing foci should not be included under the normal background parenchymal enhancement unless they are proved to be benign by biopsy. (3) Mass lesion features that indicated malignancy were irregular-shaped, spiculated and irregular margins and heterogeneous internal enhancement patterns. The rim enhancement pattern should not be considered as a descriptor of malignant lesions unless CESM is coupled with an ultrasound examination. PMID:27327403

Quantum chemical calculations, molecular dynamic (MD) simulations and experimental studies of using some azo dyes as corrosion inhibitors for iron. Part 2: Bis-azo dye derivatives

NASA Astrophysics Data System (ADS)

Madkour, Loutfy H.; Kaya, Savaş; Guo, Lei; Kaya, Cemal

2018-07-01

The adsorption behavior and inhibition mechanism of five synthesized bis-azo dye (BAD) derivatives on the corrosion of iron in aerated HNO3 and NaOH were investigated by performing potentiostatic polarization, weight loss (WL), thermometric and UV-visible spectra measurements. DFT calculations is applied to study the correlation between corrosion inhibition and global reactivity descriptors such as: EHOMO, ELUMO, molecular gap (ΔE), the dipole moment (μ), the global hardness (η), softness(S), electronegativity (χ), proton affinity (PA), electrophilicity (ω), nucleophilicity (ɛ), electrons transferred from inhibitors to metal surface (ΔN), initial molecule-metal interaction energy (Δψ), total electronic energy (E) and the energy change during electronic back-donation process (ΔEb-d). To mimic the real environment of corrosion inhibition, molecular dynamic (MD) simulations in aqueous phase have also been modelled consisting of all concerned species (inhibitor molecule, H2O, H3O+ ion, NO3- ion, OH- and Fe surface). The results confirmed that BAD molecules inhibit iron by adsorption behavior through donating and accepting electrons together with the formation of [Fe (II) and Fe (III)-BAD] chelate complex compounds. BAD's behavior is mainly chemisorption with some physisorption obeyed Frumkin and that of El-Awady adsorption isotherm. Kinetic parameters such as: (Kb, 1/y, Kads, f, ΔG°ads) have been determined and discussed. Binding energies of BAD molecules on Fe (110) surface followed the order: BAD_ 2 > BAD_ 1 > BAD_ 3 > BAD_ 4 > BAD_ 5. Theoretical results were found to be consistent with the experimental data reported. Our results provide important atomic/molecular insights into the anticorrosive mechanism of inhibitor molecules, which could help in understanding the organic-metal interface and designing more appropriate organic corrosion inhibitors.

Towards the new heterocycle based molecule: Synthesis, characterization and reactivity study

NASA Astrophysics Data System (ADS)

Murthy, P. Krishna; Sheena Mary, Y.; Suneetha, V.; Panicker, C. Yohannan; Armaković, Stevan; Armaković, Sanja J.; Giri, L.; Suchetan, P. A.; Van Alsenoy, C.

2017-06-01

4-Chloro-2-(3-fluorophenyl)-2,3-dihydro-1H-pyrrolo[3,4-c]pyridin-1-one (CFPDPPO) have been synthesized by hydride transfer from Et3SiH to carbenium ions(reduction reaction), which is formed by reaction between 4-chloro-2-(3-fluorophenyl)-3-hydroxy-2,3-dihydro-1H-pyrrolo[3,4-c]pyridin-1-one with TFA, the single crystals were grown in acetonitrile by slow evaporation technique at room temperature and characterized by single crystal X-ray diffraction, FT-IR, FT-Raman, 1H NMR, 13C NMR and ESI-MS. The experimental vibrational spectra were compared with the calculated spectra and each vibrational wavenumber was assigned on the basis of potential energy distribution (PED). Gauge-including atomic orbital 1H NMR and 13C NMR chemical shifts calculations were carried out and compared with experimental data. The HOMO and LUMO analysis is used to determine the charge transfer within the molecule. The stability of the molecule arising from hyper-conjugative interactions and charge delocalization has been analysed using NBO analysis. First hyperpolarizability is calculated in order to find its role in non-linear optics. Besides molecular electrostatic potential (MEP), global reactivity descriptors, thermodynamic properties, and Mullikan charge analysis of the title compound were computed with the same method in gas phase, theoretically. Further, employing combination of DFT calculations and molecular dynamics (MD) simulations, we have investigated in detail reactive properties of the title molecule. Investigation of local reactive properties encompassed calculations of average local ionization energies (ALIE) and Fukui functions. Stability in water has been investigated by calculations of radial distribution functions (RDF), while sensitivity towards the mechanism of autoxidation has been investigated by calculations of bond dissociation energies (BDE). The docked ligand forms a stable complex with human alpha9 nicotinic acetylcholine receptor antagonist and can be a lead compound for developing new anti-cancerous drug.

Development of a model for predicting reaction rate constants of organic chemicals with ozone at different temperatures.

PubMed

Li, Xuehua; Zhao, Wenxing; Li, Jing; Jiang, Jingqiu; Chen, Jianji; Chen, Jingwen

2013-08-01

To assess the persistence and fate of volatile organic compounds in the troposphere, the rate constants for the reaction with ozone (kO3) are needed. As kO3 values are only available for hundreds of compounds, and experimental determination of kO3 is costly and time-consuming, it is of importance to develop predictive models on kO3. In this study, a total of 379 logkO3 values at different temperatures were used to develop and validate a model for the prediction of kO3, based on quantum chemical descriptors, Dragon descriptors and structural fragments. Molecular descriptors were screened by stepwise multiple linear regression, and the model was constructed by partial least-squares regression. The cross validation coefficient QCUM(2) of the model is 0.836, and the external validation coefficient Qext(2) is 0.811, indicating that the model has high robustness and good predictive performance. The most significant descriptor explaining logkO3 is the BELm2 descriptor with connectivity information weighted atomic masses. kO3 increases with increasing BELm2, and decreases with increasing ionization potential. The applicability domain of the proposed model was visualized by the Williams plot. The developed model can be used to predict kO3 at different temperatures for a wide range of organic chemicals, including alkenes, cycloalkenes, haloalkenes, alkynes, oxygen-containing compounds, nitrogen-containing compounds (except primary amines) and aromatic compounds. Copyright © 2013 Elsevier Ltd. All rights reserved.

Fourier-based quantification of renal glomeruli size using Hough transform and shape descriptors.

PubMed

Najafian, Sohrab; Beigzadeh, Borhan; Riahi, Mohammad; Khadir Chamazkoti, Fatemeh; Pouramir, Mahdi

2017-11-01

Analysis of glomeruli geometry is important in histopathological evaluation of renal microscopic images. Due to the shape and size disparity of even glomeruli of same kidney, automatic detection of these renal objects is not an easy task. Although manual measurements are time consuming and at times are not very accurate, it is commonly used in medical centers. In this paper, a new method based on Fourier transform following usage of some shape descriptors is proposed to detect these objects and their geometrical parameters. Reaching the goal, a database of 400 regions are selected randomly. 200 regions of which are part of glomeruli and the other 200 regions are not belong to renal corpuscles. ROC curve is used to decide which descriptor could classify two groups better. f_measure, which is a combination of both tpr (true positive rate) and fpr (false positive rate), is also proposed to select optimal threshold for descriptors. Combination of three parameters (solidity, eccentricity, and also mean squared error of fitted ellipse) provided better result in terms of f_measure to distinguish desired regions. Then, Fourier transform of outer edges is calculated to form a complete curve out of separated region(s). The generality of proposed model is verified by use of cross validation method, which resulted tpr of 94%, and fpr of 5%. Calculation of glomerulus' and Bowman's space with use of the algorithm are also compared with a non-automatic measurement done by a renal pathologist, and errors of 5.9%, 5.4%, and 6.26% are resulted in calculation of Capsule area, Bowman space, and glomeruli area, respectively. Having tested different glomeruli with various shapes, the experimental consequences show robustness and reliability of our method. Therefore, it could be used to illustrate renal diseases and glomerular disorders by measuring the morphological changes accurately and expeditiously. Copyright © 2017 Elsevier B.V. All rights reserved.

Molecular Quantum Similarity, Chemical Reactivity and Database Screening of 3D Pharmacophores of the Protein Kinases A, B and G from Mycobacterium tuberculosis.

PubMed

Morales-Bayuelo, Alejandro

2017-06-21

Mycobacterium tuberculosis remains one of the world's most devastating pathogens. For this reason, we developed a study involving 3D pharmacophore searching, selectivity analysis and database screening for a series of anti-tuberculosis compounds, associated with the protein kinases A, B, and G. This theoretical study is expected to shed some light onto some molecular aspects that could contribute to the knowledge of the molecular mechanics behind interactions of these compounds, with anti-tuberculosis activity. Using the Molecular Quantum Similarity field and reactivity descriptors supported in the Density Functional Theory, it was possible to measure the quantification of the steric and electrostatic effects through the Overlap and Coulomb quantitative convergence (alpha and beta) scales. In addition, an analysis of reactivity indices using global and local descriptors was developed, identifying the binding sites and selectivity on these anti-tuberculosis compounds in the active sites. Finally, the reported pharmacophores to PKn A, B and G, were used to carry out database screening, using a database with anti-tuberculosis drugs from the Kelly Chibale research group (http://www.kellychibaleresearch.uct.ac.za/), to find the compounds with affinity for the specific protein targets associated with PKn A, B and G. In this regard, this hybrid methodology (Molecular Mechanic/Quantum Chemistry) shows new insights into drug design that may be useful in the tuberculosis treatment today.

A systematic approach to prioritize drug targets using machine learning, a molecular descriptor-based classification model, and high-throughput screening of plant derived molecules: a case study in oral cancer.

PubMed

Randhawa, Vinay; Kumar Singh, Anil; Acharya, Vishal

2015-12-01

Systems-biology inspired identification of drug targets and machine learning-based screening of small molecules which modulate their activity have the potential to revolutionize modern drug discovery by complementing conventional methods. To utilize the effectiveness of such pipelines, we first analyzed the dysregulated gene pairs between control and tumor samples and then implemented an ensemble-based feature selection approach to prioritize targets in oral squamous cell carcinoma (OSCC) for therapeutic exploration. Based on the structural information of known inhibitors of CXCR4-one of the best targets identified in this study-a feature selection was implemented for the identification of optimal structural features (molecular descriptor) based on which a classification model was generated. Furthermore, the CXCR4-centered descriptor-based classification model was finally utilized to screen a repository of plant derived small-molecules to obtain potential inhibitors. The application of our methodology may assist effective selection of the best targets which may have previously been overlooked, that in turn will lead to the development of new oral cancer medications. The small molecules identified in this study can be ideal candidates for trials as potential novel anti-oral cancer agents. Importantly, distinct steps of this whole study may provide reference for the analysis of other complex human diseases.

Spectrophores as one-dimensional descriptors calculated from three-dimensional atomic properties: applications ranging from scaffold hopping to multi-target virtual screening.

PubMed

Gladysz, Rafaela; Dos Santos, Fabio Mendes; Langenaeker, Wilfried; Thijs, Gert; Augustyns, Koen; De Winter, Hans

2018-03-07

Spectrophores are novel descriptors that are calculated from the three-dimensional atomic properties of molecules. In our current implementation, the atomic properties that were used to calculate spectrophores include atomic partial charges, atomic lipophilicity indices, atomic shape deviations and atomic softness properties. This approach can easily be widened to also include additional atomic properties. Our novel methodology finds its roots in the experimental affinity fingerprinting technology developed in the 1990's by Terrapin Technologies. Here we have translated it into a purely virtual approach using artificial affinity cages and a simplified metric to calculate the interaction between these cages and the atomic properties. A typical spectrophore consists of a vector of 48 real numbers. This makes it highly suitable for the calculation of a wide range of similarity measures for use in virtual screening and for the investigation of quantitative structure-activity relationships in combination with advanced statistical approaches such as self-organizing maps, support vector machines and neural networks. In our present report we demonstrate the applicability of our novel methodology for scaffold hopping as well as virtual screening.

Morphological and Molecular Descriptors of the Developmental Cycle of Babesia divergens Parasites in Human Erythrocytes.

PubMed

Rossouw, Ingrid; Maritz-Olivier, Christine; Niemand, Jandeli; van Biljon, Riette; Smit, Annel; Olivier, Nicholas A; Birkholtz, Lyn-Marie

2015-05-01

Human babesiosis, especially caused by the cattle derived Babesia divergens parasite, is on the increase, resulting in renewed attentiveness to this potentially life threatening emerging zoonotic disease. The molecular mechanisms underlying the pathophysiology and intra-erythrocytic development of these parasites are poorly understood. This impedes concerted efforts aimed at the discovery of novel anti-babesiacidal agents. By applying sensitive cell biological and molecular functional genomics tools, we describe the intra-erythrocytic development cycle of B. divergens parasites from immature, mono-nucleated ring forms to bi-nucleated paired piriforms and ultimately multi-nucleated tetrads that characterizes zoonotic Babesia spp. This is further correlated for the first time to nuclear content increases during intra-erythrocytic development progression, providing insight into the part of the life cycle that occurs during human infection. High-content temporal evaluation elucidated the contribution of the different stages to life cycle progression. Moreover, molecular descriptors indicate that B. divergens parasites employ physiological adaptation to in vitro cultivation. Additionally, differential expression is observed as the parasite equilibrates its developmental stages during its life cycle. Together, this information provides the first temporal evaluation of the functional transcriptome of B. divergens parasites, information that could be useful in identifying biological processes essential to parasite survival for future anti-babesiacidal discoveries.

QSAR, molecular docking studies of thiophene and imidazopyridine derivatives as polo-like kinase 1 inhibitors

NASA Astrophysics Data System (ADS)

Cao, Shandong

2012-08-01

The purpose of the present study was to develop in silico models allowing for a reliable prediction of polo-like kinase inhibitors based on a large diverse dataset of 136 compounds. As an effective method, quantitative structure activity relationship (QSAR) was applied using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). The proposed QSAR models showed reasonable predictivity of thiophene analogs (Rcv2=0.533, Rpred2=0.845) and included four molecular descriptors, namely IC3, RDF075m, Mor02m and R4e+. The optimal model for imidazopyridine derivatives (Rcv2=0.776, Rpred2=0.876) was shown to perform good in prediction accuracy, using GATS2m and BEHe1 descriptors. Analysis of the contour maps helped to identify structural requirements for the inhibitors and served as a basis for the design of the next generation of the inhibitor analogues. Docking studies were also employed to position the inhibitors into the polo-like kinase active site to determine the most probable binding mode. These studies may help to understand the factors influencing the binding affinity of chemicals and to develop alternative methods for prescreening and designing of polo-like kinase inhibitors.

Solubility of organic compounds in octanol: Improved predictions based on the geometrical fragment approach.

PubMed

Mathieu, Didier

2017-09-01

Two new models are introduced to predict the solubility of chemicals in octanol (S oct ), taking advantage of the extensive character of log(S oct ) through a decomposition of molecules into so-called geometrical fragments (GF). They are extensively validated and their compliance with regulatory requirements is demonstrated. The first model requires just a molecular formula as input. Despite an extreme simplicity, it performs as well as an advanced random forest model involving 86 descriptors, with a root mean square error (RMSE) of 0.64 log units for an external test set of 100 molecules. For the second one, which requires the melting point T m as input, introducing GF descriptors reduces the RMSE from about 0.7 to <0.5 log units, a performance that could previously be obtained only through the use of Abraham descriptors. A script is provided for easy application of the models, taking into account the limits of their applicability domains. Copyright © 2017 Elsevier Ltd. All rights reserved.

Random forest models to predict aqueous solubility.

PubMed

Palmer, David S; O'Boyle, Noel M; Glen, Robert C; Mitchell, John B O

2007-01-01

Random Forest regression (RF), Partial-Least-Squares (PLS) regression, Support Vector Machines (SVM), and Artificial Neural Networks (ANN) were used to develop QSPR models for the prediction of aqueous solubility, based on experimental data for 988 organic molecules. The Random Forest regression model predicted aqueous solubility more accurately than those created by PLS, SVM, and ANN and offered methods for automatic descriptor selection, an assessment of descriptor importance, and an in-parallel measure of predictive ability, all of which serve to recommend its use. The prediction of log molar solubility for an external test set of 330 molecules that are solid at 25 degrees C gave an r2 = 0.89 and RMSE = 0.69 log S units. For a standard data set selected from the literature, the model performed well with respect to other documented methods. Finally, the diversity of the training and test sets are compared to the chemical space occupied by molecules in the MDL drug data report, on the basis of molecular descriptors selected by the regression analysis.

3D-QSAR and docking studies on 4-anilinoquinazoline and 4-anilinoquinoline epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors

NASA Astrophysics Data System (ADS)

Assefa, Haregewein; Kamath, Shantaram; Buolamwini, John K.

2003-08-01

The overexpression and/or mutation of the epidermal growth factor receptor (EGFR) tyrosine kinase has been observed in many human solid tumors, and is under intense investigation as a novel anticancer molecular target. Comparative 3D-QSAR analyses using different alignments were undertaken employing comparative molecular field analysis (CoMFA) and comparative molecular similarity analysis (CoMSIA) for 122 anilinoquinazoline and 50 anilinoquinoline inhibitors of EGFR kinase. The SYBYL multifit alignment rule was applied to three different conformational templates, two obtained from a MacroModel Monte Carlo conformational search, and one from the bound conformation of erlotinib in complex with EGFR in the X-ray crystal structure. In addition, a flexible ligand docking alignment obtained with the GOLD docking program, and a novel flexible receptor-guided consensus dynamics alignment obtained with the DISCOVER program in the INSIGHTII modeling package were also investigated. 3D-QSAR models with q2 values up to 0.70 and r2 values up to 0.97 were obtained. Among the 4-anilinoquinazoline set, the q2 values were similar, but the ability of the different conformational models to predict the activities of an external test set varied considerably. In this regard, the model derived using the X-ray crystallographically determined bioactive conformation of erlotinib afforded the best predictive model. Electrostatic, hydrophobic and H-bond donor descriptors contributed the most to the QSAR models of the 4-anilinoquinazolines, whereas electrostatic, hydrophobic and H-bond acceptor descriptors contributed the most to the 4-anilinoquinoline QSAR, particularly the H-bond acceptor descriptor. A novel receptor-guided consensus dynamics alignment has also been introduced for 3D-QSAR studies. This new alignment method may incorporate to some extent ligand-receptor induced fit effects into 3D-QSAR models.

Compositional descriptor-based recommender system for the materials discovery

NASA Astrophysics Data System (ADS)

Seko, Atsuto; Hayashi, Hiroyuki; Tanaka, Isao

2018-06-01

Structures and properties of many inorganic compounds have been collected historically. However, it only covers a very small portion of possible inorganic crystals, which implies the presence of numerous currently unknown compounds. A powerful machine-learning strategy is mandatory to discover new inorganic compounds from all chemical combinations. Herein we propose a descriptor-based recommender-system approach to estimate the relevance of chemical compositions where crystals can be formed [i.e., chemically relevant compositions (CRCs)]. In addition to data-driven compositional similarity used in the literature, the use of compositional descriptors as a prior knowledge is helpful for the discovery of new compounds. We validate our recommender systems in two ways. First, one database is used to construct a model, while another is used for the validation. Second, we estimate the phase stability for compounds at expected CRCs using density functional theory calculations.

Texture functions in image analysis: A computationally efficient solution

NASA Technical Reports Server (NTRS)

Cox, S. C.; Rose, J. F.

1983-01-01

A computationally efficient means for calculating texture measurements from digital images by use of the co-occurrence technique is presented. The calculation of the statistical descriptors of image texture and a solution that circumvents the need for calculating and storing a co-occurrence matrix are discussed. The results show that existing efficient algorithms for calculating sums, sums of squares, and cross products can be used to compute complex co-occurrence relationships directly from the digital image input.

Human serum albumin binding of certain antimalarials

NASA Astrophysics Data System (ADS)

Marković, Olivera S.; Cvijetić, Ilija N.; Zlatović, Mario V.; Opsenica, Igor M.; Konstantinović, Jelena M.; Terzić Jovanović, Nataša V.; Šolaja, Bogdan A.; Verbić, Tatjana Ž.

2018-03-01

Interactions between eight in-house synthesized aminoquinolines, along with well-known chloroquine, and human serum albumin (HSA) have been studied by fluorescence spectroscopy. The synthesized aminoquinolines, despite being structurally diverse, were found to be very potent antimalarials. Fluorescence measurements indicate that three compounds having additional thiophene or benzothiophene substructure bind more strongly to HSA than other studied compounds. Competitive binding experiments indicate that these three compounds bind significantly stronger to warfarin compared to diazepam binding site. Fluorescence quenching at three temperatures (20, 25, and 37 °C) was analyzed using classical Stern-Volmer equation, and a static quenching mechanism was proposed. The enthalpy and entropy changes upon sulphur-containing compound-HSA interactions were calculated using Van't Hoff equation. Positive values of enthalpy and entropy changes indicate that non-specific, hydrophobic interactions are the main contributors to HSA-compound interaction. Molecular docking and calculated lipophilicity descriptors indicate the same, pointing out that the increased lipophilicity of sulphur-containing compounds might be a reason for their better binding to HSA. Obtained results might contribute to design of novel derivatives with improved pharmacokinetic properties and drug efficacy.

Reactive sites influence in PMMA oligomers reactivity: a DFT study

NASA Astrophysics Data System (ADS)

Paz, C. V.; Vásquez, S. R.; Flores, N.; García, L.; Rico, J. L.

2018-01-01

In this work, we present a theoretical study of methyl methacrylate (MMA) living anionic polymerization. The study was addressed to understanding two important experimental observations made for Michael Szwarc in 1956. The unexpected effect of reactive sites concentration in the propagation rate, and the self-killer behavior of MMA (deactivating of living anionic polymerization). The theoretical calculations were performed by density functional theory (DFT) to obtain the frontier molecular orbitals values. These values were used to calculate and analyze the chemical interaction descriptors in DFT-Koopmans’ theorem. As a result, it was observed that the longest chain-length species (related with low concentration of reactive sites) exhibit the highest reactivity (behavior associated with the increase of the propagation rate). The improvement in this reactivity was attributed to the crosslinking produced in the polymethyl methacrylate chains. Meanwhile, the self-killer behavior was associated with the intermolecular forces present in the reactive sites. This behavior was associated to an obstruction in solvation, since the active sites remained active through all propagation species. The theoretical results were in good agreement with the Szwarc experiments.

Structural characterization, surface characteristics and non covalent interactions of a heterocyclic Schiff base: Evaluation of antioxidant potential by UV-visible spectroscopy and DFT

NASA Astrophysics Data System (ADS)

Chithiraikumar, S.; Gandhimathi, S.; Neelakantan, M. A.

2017-06-01

A heterocyclic Schiff base, (E)-4-(1-((pyridin-2-ylmethyl)imino)ethyl)benzene-1,3-diol (L) was synthesized and isolated as single crystals. Its structure was characterized by FT-IR, UV, 1H and 13C NMR, and further confirmed by X-ray crystallography. Qualitatively and quantitatively the various interactions in the crystal structure of L has been analyzed by Hirshfeld surfaces and 2D fingerprint plots. Non covalent interactions have been studied by electron localization function (ELF) and mapped with reduced density gradient (RDG) analysis. The molecular structure was studied computationally by DFT-B3LYP/6-311G(d,p) calculations. HOMO-LUMO energy levels, chemical reactivity descriptors and thermodynamic parameters have been investigated at the same level of theory. The antioxidant potential of L was evaluated experimentally by measuring DPPH free radical scavenging effect using UV-visible spectroscopy and theoretically by DFT. Theoretical parameters, such as bond dissociation enthalpy (BDE) and spin density calculated suggests that antioxidant potential of L is due to H atom abstraction from the sbnd OH group.

Rapid multi-modality preregistration based on SIFT descriptor.

PubMed

Chen, Jian; Tian, Jie

2006-01-01

This paper describes the scale invariant feature transform (SIFT) method for rapid preregistration of medical image. This technique originates from Lowe's method wherein preregistration is achieved by matching the corresponding keypoints between two images. The computational complexity has been reduced when we applied SIFT preregistration method before refined registration due to its O(n) exponential calculations. The features of SIFT are highly distinctive and invariant to image scaling and rotation, and partially invariant to change in illumination and contrast, it is robust and repeatable for cursorily matching two images. We also altered the descriptor so our method can deal with multimodality preregistration.

Atomic spectral-product representations of molecular electronic structure: metric matrices and atomic-product composition of molecular eigenfunctions.

PubMed

Ben-Nun, M; Mills, J D; Hinde, R J; Winstead, C L; Boatz, J A; Gallup, G A; Langhoff, P W

2009-07-02

Recent progress is reported in development of ab initio computational methods for the electronic structures of molecules employing the many-electron eigenstates of constituent atoms in spectral-product forms. The approach provides a universal atomic-product description of the electronic structure of matter as an alternative to more commonly employed valence-bond- or molecular-orbital-based representations. The Hamiltonian matrix in this representation is seen to comprise a sum over atomic energies and a pairwise sum over Coulombic interaction terms that depend only on the separations of the individual atomic pairs. Overall electron antisymmetry can be enforced by unitary transformation when appropriate, rather than as a possibly encumbering or unnecessary global constraint. The matrix representative of the antisymmetrizer in the spectral-product basis, which is equivalent to the metric matrix of the corresponding explicitly antisymmetric basis, provides the required transformation to antisymmetric or linearly independent states after Hamiltonian evaluation. Particular attention is focused in the present report on properties of the metric matrix and on the atomic-product compositions of molecular eigenstates as described in the spectral-product representations. Illustrative calculations are reported for simple but prototypically important diatomic (H(2), CH) and triatomic (H(3), CH(2)) molecules employing algorithms and computer codes devised recently for this purpose. This particular implementation of the approach combines Slater-orbital-based one- and two-electron integral evaluations, valence-bond constructions of standard tableau functions and matrices, and transformations to atomic eigenstate-product representations. The calculated metric matrices and corresponding potential energy surfaces obtained in this way elucidate a number of aspects of the spectral-product development, including the nature of closure in the representation, the general redundancy or linear dependence of its explicitly antisymmetrized form, the convergence of the apparently disparate atomic-product and explicitly antisymmetrized atomic-product forms to a common invariant subspace, and the nature of a chemical bonding descriptor provided by the atomic-product compositions of molecular eigenstates. Concluding remarks indicate additional studies in progress and the prognosis for performing atomic spectral-product calculations more generally and efficiently.

Introducing new reactivity descriptors: "Bond reactivity indices." Comparison of the new definitions and atomic reactivity indices.

PubMed

Sánchez-Márquez, Jesús

2016-11-21

A new methodology to obtain reactivity indices has been defined. This is based on reactivity functions such as the Fukui function or the dual descriptor and makes it possible to project the information of reactivity functions over molecular orbitals instead of the atoms of the molecule (atomic reactivity indices). The methodology focuses on the molecule's natural bond orbitals (bond reactivity indices) because these orbitals (with physical meaning) have the advantage of being very localized, allowing the reaction site of an electrophile or nucleophile to be determined within a very precise molecular region. This methodology gives a reactivity index for every Natural Bond Orbital (NBO), and we have verified that they have equivalent information to the reactivity functions. A representative set of molecules has been used to test the new definitions. Also, the bond reactivity index has been related with the atomic reactivity one, and complementary information has been obtained from the comparison. Finally, a new atomic reactivity index has been defined and compared with previous definitions.

A combined LS-SVM & MLR QSAR workflow for predicting the inhibition of CXCR3 receptor by quinazolinone analogs.

PubMed

Afantitis, Antreas; Melagraki, Georgia; Sarimveis, Haralambos; Koutentis, Panayiotis A; Igglessi-Markopoulou, Olga; Kollias, George

2010-05-01

A novel QSAR workflow is constructed that combines MLR with LS-SVM classification techniques for the identification of quinazolinone analogs as "active" or "non-active" CXCR3 antagonists. The accuracy of the LS-SVM classification technique for the training set and test was 100% and 90%, respectively. For the "active" analogs a validated MLR QSAR model estimates accurately their I-IP10 IC(50) inhibition values. The accuracy of the QSAR model (R (2) = 0.80) is illustrated using various evaluation techniques, such as leave-one-out procedure (R(LOO2)) = 0.67) and validation through an external test set (R(pred2) = 0.78). The key conclusion of this study is that the selected molecular descriptors, Highest Occupied Molecular Orbital energy (HOMO), Principal Moment of Inertia along X and Y axes PMIX and PMIZ, Polar Surface Area (PSA), Presence of triple bond (PTrplBnd), and Kier shape descriptor ((1) kappa), demonstrate discriminatory and pharmacophore abilities.

QSPR model for bioconcentration factors of nonpolar organic compounds using molecular electronegativity distance vector descriptors.

PubMed

Qin, Li-Tang; Liu, Shu-Shen; Liu, Hai-Ling

2010-02-01

A five-variable model (model M2) was developed for the bioconcentration factors (BCFs) of nonpolar organic compounds (NPOCs) by using molecular electronegativity distance vector (MEDV) to characterize the structures of NPOCs and variable selection and modeling based on prediction (VSMP) to select the optimum descriptors. The estimated correlation coefficient (r (2)) and the leave-one-out cross-validation correlation coefficients (q (2)) of model M2 were 0.9271 and 0.9171, respectively. The model was externally validated by splitting the whole data set into a representative training set of 85 chemicals and a validation set of 29 chemicals. The results show that the main structural factors influencing the BCFs of NPOCs are -cCc, cCcc, -Cl, and -Br (where "-" refers to a single bond and "c" refers to a conjugated bond). The quantitative structure-property relationship (QSPR) model can effectively predict the BCFs of NPOCs, and the predictions of the model can also extend the current BCF database of experimental values.

Prediction on dielectric strength and boiling point of gaseous molecules for replacement of SF6.

PubMed

Yu, Xiaojuan; Hou, Hua; Wang, Baoshan

2017-04-15

Developing the environment-friendly insulation gases to replace sulfur hexafluoride (SF 6 ) has attracted considerable experimental and theoretical attentions but without success. A computational methodology was presented herein for prediction on dielectric strength and boiling point of arbitrary gaseous molecules in the purpose of molecular design and screening. New structure-activity relationship (SAR) models have been established by combining the density-dependent properties of the electrostatic potential surface, including surface area and the statistical variance of the surface potentials, with the molecular properties including polarizability, electronegativity, and hardness. All the descriptors in the SAR models were calculated using density functional theory. The substitution effect of SF 6 by various functional groups was studied systematically. It was found that CF 3 is the most effective functional group to improve the dielectric strength due to the large surface area and polarizability. However, all the substitutes exhibit higher boiling points than SF 6 because the molecular hardness decreases. The balance between E r and T b could be achieved by minimizing the local polarity of the molecules. SF 5 CN and SF 5 CFO were found to be the potent candidates to replace SF 6 in view of their large dielectric strengths and low boiling points. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Ionic liquids at the surface of graphite: Wettability and structure

NASA Astrophysics Data System (ADS)

Bordes, Emilie; Douce, Laurent; Quitevis, Edward L.; Pádua, Agílio A. H.; Costa Gomes, Margarida

2018-05-01

The aim of this work is to provide a better understanding of the interface between graphite and different molecular and ionic liquids. Experimental measurements of the liquid surface tension and of the graphite-liquid contact angle for sixteen ionic liquids and three molecular liquids are reported. These experimental values allowed the calculation of the solid/liquid interfacial energy that varies, for the ionic liquids studied, between 14.5 mN m-1 for 1-ethyl-3-methylimidazolium dicyanamide and 37.8 mN m-1 for 3-dodecyl-1-(naphthalen-1-yl)-1H-imidazol-3-ium tetrafluoroborate. Imidazolium-based ionic liquids with large alkyl side-chains or functionalized with benzyl groups seem to interact more favourably with freshly peeled graphite surfaces. Even if the interfacial energy seems a good descriptor to assess the affinity of a liquid for a carbon-based solid material, we conclude that both the surface tension of the liquid and the contact angle between the liquid and the solid can be significant. Molecular dynamics simulations were used to investigate the ordering of the ions near the graphite surface. We conclude that the presence of large alkyl side-chains in the cations increases the ordering of ions at the graphite surface. Benzyl functional groups in the cations lead to a large affinity towards the graphite surface.

On the molecular origins of biomass recalcitrance: the interaction network and solvation structures of cellulose microfibrils.

PubMed

Gross, Adam S; Chu, Jhih-Wei

2010-10-28

Biomass recalcitrance is a fundamental bottleneck to producing fuels from renewable sources. To understand its molecular origin, we characterize the interaction network and solvation structures of cellulose microfibrils via all-atom molecular dynamics simulations. The network is divided into three components: intrachain, interchain, and intersheet interactions. Analysis of their spatial dependence and interaction energetics indicate that intersheet interactions are the most robust and strongest component and do not display a noticeable dependence on solvent exposure. Conversely, the strength of surface-exposed intrachain and interchain hydrogen bonds is significantly reduced. Comparing the interaction networks of I(β) and I(α) cellulose also shows that the number of intersheet interactions is a clear descriptor that distinguishes the two allomorphs and is consistent with the observation that I(β) is the more stable form. These results highlight the dominant role of the often-overlooked intersheet interactions in giving rise to biomass recalcitrance. We also analyze the solvation structures around the surfaces of microfibrils and show that the structural and chemical features at cellulose surfaces constrict water molecules into specific density profiles and pair correlation functions. Calculations of water density and compressibility in the hydration shell show noticeable but not drastic differences. Therefore, specific solvation structures are more prominent signatures of different surfaces.

Surface similarity-based molecular query-retrieval

PubMed Central

Singh, Rahul

2007-01-01

Background Discerning the similarity between molecules is a challenging problem in drug discovery as well as in molecular biology. The importance of this problem is due to the fact that the biochemical characteristics of a molecule are closely related to its structure. Therefore molecular similarity is a key notion in investigations targeting exploration of molecular structural space, query-retrieval in molecular databases, and structure-activity modelling. Determining molecular similarity is related to the choice of molecular representation. Currently, representations with high descriptive power and physical relevance like 3D surface-based descriptors are available. Information from such representations is both surface-based and volumetric. However, most techniques for determining molecular similarity tend to focus on idealized 2D graph-based descriptors due to the complexity that accompanies reasoning with more elaborate representations. Results This paper addresses the problem of determining similarity when molecules are described using complex surface-based representations. It proposes an intrinsic, spherical representation that systematically maps points on a molecular surface to points on a standard coordinate system (a sphere). Molecular surface properties such as shape, field strengths, and effects due to field super-positioningcan then be captured as distributions on the surface of the sphere. Surface-based molecular similarity is subsequently determined by computing the similarity of the surface-property distributions using a novel formulation of histogram-intersection. The similarity formulation is not only sensitive to the 3D distribution of the surface properties, but is also highly efficient to compute. Conclusion The proposed method obviates the computationally expensive step of molecular pose-optimisation, can incorporate conformational variations, and facilitates highly efficient determination of similarity by directly comparing molecular surfaces and surface-based properties. Retrieval performance, applications in structure-activity modeling of complex biological properties, and comparisons with existing research and commercial methods demonstrate the validity and effectiveness of the approach. PMID:17634096

Chapter 5 Multiple, Localized, and Delocalized/Conjugated Bonds in the Orbital Communication Theory of Molecular Systems

NASA Astrophysics Data System (ADS)

Nalewajski, Roman F.

Information theory (IT) probe of the molecular electronic structure, within the communication theory of chemical bonds (CTCB), uses the standard entropy/information descriptors of the Shannon theory of communication to characterize a scattering of the electronic probabilities and their information content throughout the system chemical bonds generated by the occupied molecular orbitals (MO). These "communications" between the basis-set orbitals are determined by the two-orbital conditional probabilities: one- and two-electron in character. They define the molecular information system, in which the electron-allocation "signals" are transmitted between various orbital "inputs" and "outputs". It is argued, using the quantum mechanical superposition principle, that the one-electron conditional probabilities are proportional to the squares of corresponding elements of the charge and bond-order (CBO) matrix of the standard LCAO MO theory. Therefore, the probability of the interorbital connections in the molecular communication system is directly related to Wiberg's quadratic covalency indices of chemical bonds. The conditional-entropy (communication "noise") and mutual-information (information capacity) descriptors of these molecular channels generate the IT-covalent and IT-ionic bond components, respectively. The former reflects the electron delocalization (indeterminacy) due to the orbital mixing, throughout all chemical bonds in the system under consideration. The latter characterizes the localization (determinacy) in the probability scattering in the molecule. These two IT indices, respectively, indicate a fraction of the input information lost in the channel output, due to the communication noise, and its surviving part, due to deterministic elements in probability scattering in the molecular network. Together, these two components generate the system overall bond index. By a straightforward output reduction (condensation) of the molecular channel, the IT indices of molecular fragments, for example, localized bonds, functional groups, and forward and back donations accompanying the bond formation, and so on, can be extracted. The flow of information in such molecular communication networks is investigated in several prototype molecules. These illustrative (model) applications of the orbital communication theory of chemical bonds (CTCB) deal with several classical issues in the electronic structure theory: atom hybridization/promotion, single and multiple chemical bonds, bond conjugation, and so on. The localized bonds in hydrides and delocalized [pi]-bonds in simple hydrocarbons, as well as the multiple bonds in CO and CO2, are diagnosed using the entropy/information descriptors of CTCB. The atom promotion in hydrides and bond conjugation in [pi]-electron systems are investigated in more detail. A major drawback of the previous two-electron approach to molecular channels, namely, two weak bond differentiation in aromatic systems, has been shown to be remedied in the one-electron approach.

Synthesis of t-butyl 2-(4-hydroxy-3-methoxybenzylidene)hydrazine carboxylate: Experimental and theoretical investigations of its properties

NASA Astrophysics Data System (ADS)

Bhat, Muzzaffar A.; Lone, Shabir H.; Mir, Muzzaffar A.; Majid, Sheikh A.; Bhat, Haroon Mohi-ud-din; Butcher, Raymond J.; Srivastava, Sanjay K.

2018-07-01

A convenient and facile synthesis of t-butyl-2-(4-hydroxy-3-methoxybenzylidene)hydrazine carboxylate (1) was accomplished by refluxing t-butyl carbazate with an appropriate aldehyde in ethanol. The resulting compound was characterized using spectral data analysis augmented by X-ray. Single crystal analysis depicted that compound 1 crystallizes in a monoclinic crystal system with P 21/c space group having trans-geometry at the Cdbnd N bond. The structural and electronic properties of the title compound have been calculated using DFT/B3LYP/6-311G (d,p) level of theory. Theoretically obtained parameters were well compared to the experimentally obtained results which depicted excellent agreement. Molecular electrostatic potential surface, frontier orbital analysis and vibrational analysis were also carried out. HOMO-LUMO energy gap was calculated which allowed the calculation of relative reactivity descriptors like chemical hardness, chemical inertness, chemical potential, nucleophilicity and electrophilicity index of the synthesized product. Pass prediction was carried out which revealed that compound 1 can be highly active against Mcl-1 enzyme, with Pa of 0.544. Based on Pass, molecular docking of compound 1 was carried out against Mcl-1 protein. Compound 1 displayed a binding free energy of -5.22 kcal/mol and inhibition constant of 149.06 μM and 1 depicted only alkyl hydrophobic and mixed pi/alkyl hydrophobic interactions with Mcl-1 enzyme. In short, this study reveals the synthesis of a new schiff base, and unravels the structural, electronic and biological properties of the title compound, paving way for further research in the field of drug development.

Rapid-gradient HPLC method for measuring drug interactions with immobilized artificial membrane: comparison with other lipophilicity measures.

PubMed

Valko, K; Du, C M; Bevan, C D; Reynolds, D P; Abraham, M H

2000-08-01

A fast-gradient high-performance liquid chromatographic (HPLC) method has been suggested to characterize the interactions of drugs with an immobilized artificial membrane (IAM). With a set of standards, the gradient retention times can be converted to Chromatographic Hydrophobicity Index values referring to IAM chromatography (CHI(IAM)) that approximates an acetonitrile concentration with which the equal distribution of compound can be achieved between the mobile phase and IAM. The CHI(IAM) values are more suitable for interlaboratory comparison and for high throughput screening of new molecular entities than the log k(IAM) values (isocratic retention factor on IAM). The fast-gradient method has been validated against the isocratic log k(IAM) values using the linear free energy relationship solvation equations based on the data from 48 compounds. The compound set was selected to provide a wide range and the least cross-correlation between the molecular descriptors in the solvation equation: (2) where SP is a solute property (e.g., logarithm of partition coefficients, reversed-phase (RP)-HPLC retention parameters, such as log k, log k(w), etc.) and the explanatory variables are solute descriptors as follows: R(2) is an excess molar refraction that can be obtained from the measured refractive index of a compound, pi(2)(H) is the solute dipolarity/polarizability, summation operatoralpha(2)(H) and summation operatorbeta(2)(0) are the solute overall or effective hydrogen-bond acidity and basicity, respectively, and V(x) is the McGowan characteristic volume (in cm(3)/100 mol) that can be calculated for any solute simply from molecular structure using a table of atomic constants. It was found that the relative constants of the solvation equation were very similar for the CHI(IAM) and for the log k(IAM). The IAM lipophilicity scale was quite similar to the octanol/water lipophilicity scale for neutral compounds. The effect of charge on the interaction with IAM was studied by varying the mobile phase pH. Copyright 2000 Wiley-Liss, Inc.

A review on principles, theory and practices of 2D-QSAR.

PubMed

Roy, Kunal; Das, Rudra Narayan

2014-01-01

The central axiom of science purports the explanation of every natural phenomenon using all possible logics coming from pure as well as mixed scientific background. The quantitative structure-activity relationship (QSAR) analysis is a study correlating the behavioral manifestation of compounds with their structures employing the interdisciplinary knowledge of chemistry, mathematics, biology as well as physics. Several studies have attempted to mathematically correlate the chemistry and property (physicochemical/ biological/toxicological) of molecules using various computationally or experimentally derived quantitative parameters termed as descriptors. The dimensionality of the descriptors depends on the type of algorithm employed and defines the nature of QSAR analysis. The most interesting feature of predictive QSAR models is that the behavior of any new or even hypothesized molecule can be predicted by the use of the mathematical equations. The phrase "2D-QSAR" signifies development of QSAR models using 2D-descriptors. Such predictor variables are the most widely practised ones because of their simple and direct mathematical algorithmic nature involving no time consuming energy computations and having reproducible operability. 2D-descriptors have a deluge of contributions in extracting chemical attributes and they are also capable of representing the 3D molecular features to some extent; although in no case they should be considered as the ultimate one, since they often suffer from the problems of intercorrelation, insufficient chemical information as well as lack of interpretation. However, by following rational approaches, novel 2D-descriptors may be developed to obviate various existing problems giving potential 2D-QSAR equations, thereby solving the innumerable chemical mysteries still unexplored.

Rate constants of hydroxyl radical oxidation of polychlorinated biphenyls in the gas phase: A single-descriptor based QSAR and DFT study.

PubMed

Yang, Zhihui; Luo, Shuang; Wei, Zongsu; Ye, Tiantian; Spinney, Richard; Chen, Dong; Xiao, Ruiyang

2016-04-01

The second-order rate constants (k) of hydroxyl radical (·OH) with polychlorinated biphenyls (PCBs) in the gas phase are of scientific and regulatory importance for assessing their global distribution and fate in the atmosphere. Due to the limited number of measured k values, there is a need to model the k values for unknown PCBs congeners. In the present study, we developed a quantitative structure-activity relationship (QSAR) model with quantum chemical descriptors using a sequential approach, including correlation analysis, principal component analysis, multi-linear regression, validation, and estimation of applicability domain. The result indicates that the single descriptor, polarizability (α), plays an important role in determining the reactivity with a global standardized function of lnk = -0.054 × α ‒ 19.49 at 298 K. In order to validate the QSAR predicted k values and expand the current k value database for PCBs congeners, an independent method, density functional theory (DFT), was employed to calculate the kinetics and thermodynamics of the gas-phase ·OH oxidation of 2,4',5-trichlorobiphenyl (PCB31), 2,2',4,4'-tetrachlorobiphenyl (PCB47), 2,3,4,5,6-pentachlorobiphenyl (PCB116), 3,3',4,4',5,5'-hexachlorobiphenyl (PCB169), and 2,3,3',4,5,5',6-heptachlorobiphenyl (PCB192) at 298 K at B3LYP/6-311++G**//B3LYP/6-31 + G** level of theory. The QSAR predicted and DFT calculated k values for ·OH oxidation of these PCB congeners exhibit excellent agreement with the experimental k values, indicating the robustness and predictive power of the single-descriptor based QSAR model we developed. Copyright © 2015 Elsevier Ltd. All rights reserved.

Design Principles for Covalent Organic Frameworks as Efficient Electrocatalysts in Clean Energy Conversion and Green Oxidizer Production.

PubMed

Lin, Chun-Yu; Zhang, Lipeng; Zhao, Zhenghang; Xia, Zhenhai

2017-05-01

Covalent organic frameworks (COFs), an emerging class of framework materials linked by covalent bonds, hold potential for various applications such as efficient electrocatalysts, photovoltaics, and sensors. To rationally design COF-based electrocatalysts for oxygen reduction and evolution reactions in fuel cells and metal-air batteries, activity descriptors, derived from orbital energy and bonding structures, are identified with the first-principle calculations for the COFs, which correlate COF structures with their catalytic activities. The calculations also predict that alkaline-earth metal-porphyrin COFs could catalyze the direct production of H 2 O 2 , a green oxidizer and an energy carrier. These predictions are supported by experimental data, and the design principles derived from the descriptors provide an approach for rational design of new electrocatalysts for both clean energy conversion and green oxidizer production. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A method for real-time implementation of HOG feature extraction

NASA Astrophysics Data System (ADS)

Luo, Hai-bo; Yu, Xin-rong; Liu, Hong-mei; Ding, Qing-hai

2011-08-01

Histogram of oriented gradient (HOG) is an efficient feature extraction scheme, and HOG descriptors are feature descriptors which is widely used in computer vision and image processing for the purpose of biometrics, target tracking, automatic target detection(ATD) and automatic target recognition(ATR) etc. However, computation of HOG feature extraction is unsuitable for hardware implementation since it includes complicated operations. In this paper, the optimal design method and theory frame for real-time HOG feature extraction based on FPGA were proposed. The main principle is as follows: firstly, the parallel gradient computing unit circuit based on parallel pipeline structure was designed. Secondly, the calculation of arctangent and square root operation was simplified. Finally, a histogram generator based on parallel pipeline structure was designed to calculate the histogram of each sub-region. Experimental results showed that the HOG extraction can be implemented in a pixel period by these computing units.

Computational study of frontier orbitals, moments, chemical reactivity and thermodynamic parameters of sildenafil

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sachdeva, Ritika, E-mail: ritika.sachdeva21@gmail.com; Kaur, Prabhjot; Singh, V. P.

2016-05-06

Analysis of frontier orbitals of sildenafil has been carried using Density Functional Theory. On the basis of HOMO-LUMO energy, values of global chemical reactivity descriptors such as electronegativity, chemical hardness, softness, chemical potential, electrophilicity index have been calculated. Calculated values of dipole moment, polarizability, hyperpolarizability have also been reported for sildenafil along with its thermodynamic parameters.

The two faces of hydrogen-bond strength on triple AAA-DDD arrays.

PubMed

Lopez, Alfredo Henrique Duarte; Caramori, Giovanni Finoto; Coimbra, Daniel Fernando; Parreira, Renato Luis Tame; da Silva, Éder Henrique

2013-12-02

Systems that are connected through multiple hydrogen bonds are the cornerstone of molecular recognition processes in biology, and they are increasingly being employed in supramolecular chemistry, specifically in molecular self-assembly processes. For this reason, the effects of different substituents (NO2, CN, F, Cl, Br, OCH3 and NH2) on the electronic structure, and consequently on the magnitude of hydrogen bonds in triple AAA-DDD arrays (A=acceptor, D=donor) were evaluated in the light of topological [electron localization function (ELF) and quantum theory of atoms in molecules (QTAIM)], energetic [Su-Li energy-decomposition analysis (EDA) and natural bond orbital analysis (NBO)], and geometrical analysis. The results based on local H-bond descriptors (geometries, QTAIM, ELF, and NBO) indicate that substitutions with electron-withdrawing groups on the AAA module tend to strengthen, whereas electron-donating substituents tend to weaken the covalent character of the AAA-DDD intermolecular H-bonds, and also indicate that the magnitude of the effect is dependent on the position of substitution. In contrast, Su-Li EDA results show an opposite behavior when compared to local H-bond descriptors, indicating that electron-donating substituents tend to increase the magnitude of H-bonds in AAA-DDD arrays, and thus suggesting that the use of local H-bond descriptors describes the nature of H bonds only partially, not providing enough insight about the strength of such H bonds. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A novel prediction approach for antimalarial activities of Trimethoprim, Pyrimethamine, and Cycloguanil analogues using extremely randomized trees.

PubMed

Nattee, Cholwich; Khamsemanan, Nirattaya; Lawtrakul, Luckhana; Toochinda, Pisanu; Hannongbua, Supa

2017-01-01

Malaria is still one of the most serious diseases in tropical regions. This is due in part to the high resistance against available drugs for the inhibition of parasites, Plasmodium, the cause of the disease. New potent compounds with high clinical utility are urgently needed. In this work, we created a novel model using a regression tree to study structure-activity relationships and predict the inhibition constant, K i of three different antimalarial analogues (Trimethoprim, Pyrimethamine, and Cycloguanil) based on their molecular descriptors. To the best of our knowledge, this work is the first attempt to study the structure-activity relationships of all three analogues combined. The most relevant descriptors and appropriate parameters of the regression tree are harvested using extremely randomized trees. These descriptors are water accessible surface area, Log of the aqueous solubility, total hydrophobic van der Waals surface area, and molecular refractivity. Out of all possible combinations of these selected parameters and descriptors, the tree with the strongest coefficient of determination is selected to be our prediction model. Predicted K i values from the proposed model show a strong coefficient of determination, R 2 =0.996, to experimental K i values. From the structure of the regression tree, compounds with high accessible surface area of all hydrophobic atoms (ASA_H) and low aqueous solubility of inhibitors (Log S) generally possess low K i values. Our prediction model can also be utilized as a screening test for new antimalarial drug compounds which may reduce the time and expenses for new drug development. New compounds with high predicted K i should be excluded from further drug development. It is also our inference that a threshold of ASA_H greater than 575.80 and Log S less than or equal to -4.36 is a sufficient condition for a new compound to possess a low K i . Copyright © 2016 Elsevier Inc. All rights reserved.

Structural alignment of protein descriptors - a combinatorial model.

PubMed

Antczak, Maciej; Kasprzak, Marta; Lukasiak, Piotr; Blazewicz, Jacek

2016-09-17

Structural alignment of proteins is one of the most challenging problems in molecular biology. The tertiary structure of a protein strictly correlates with its function and computationally predicted structures are nowadays a main premise for understanding the latter. However, computationally derived 3D models often exhibit deviations from the native structure. A way to confirm a model is a comparison with other structures. The structural alignment of a pair of proteins can be defined with the use of a concept of protein descriptors. The protein descriptors are local substructures of protein molecules, which allow us to divide the original problem into a set of subproblems and, consequently, to propose a more efficient algorithmic solution. In the literature, one can find many applications of the descriptors concept that prove its usefulness for insight into protein 3D structures, but the proposed approaches are presented rather from the biological perspective than from the computational or algorithmic point of view. Efficient algorithms for identification and structural comparison of descriptors can become crucial components of methods for structural quality assessment as well as tertiary structure prediction. In this paper, we propose a new combinatorial model and new polynomial-time algorithms for the structural alignment of descriptors. The model is based on the maximum-size assignment problem, which we define here and prove that it can be solved in polynomial time. We demonstrate suitability of this approach by comparison with an exact backtracking algorithm. Besides a simplification coming from the combinatorial modeling, both on the conceptual and complexity level, we gain with this approach high quality of obtained results, in terms of 3D alignment accuracy and processing efficiency. All the proposed algorithms were developed and integrated in a computationally efficient tool descs-standalone, which allows the user to identify and structurally compare descriptors of biological molecules, such as proteins and RNAs. Both PDB (Protein Data Bank) and mmCIF (macromolecular Crystallographic Information File) formats are supported. The proposed tool is available as an open source project stored on GitHub ( https://github.com/mantczak/descs-standalone ).

QSPR models for half-wave reduction potential of steroids: a comparative study between feature selection and feature extraction from subsets of or entire set of descriptors.

PubMed

Hemmateenejad, Bahram; Yazdani, Mahdieh

2009-02-16

Steroids are widely distributed in nature and are found in plants, animals, and fungi in abundance. A data set consists of a diverse set of steroids have been used to develop quantitative structure-electrochemistry relationship (QSER) models for their half-wave reduction potential. Modeling was established by means of multiple linear regression (MLR) and principle component regression (PCR) analyses. In MLR analysis, the QSPR models were constructed by first grouping descriptors and then stepwise selection of variables from each group (MLR1) and stepwise selection of predictor variables from the pool of all calculated descriptors (MLR2). Similar procedure was used in PCR analysis so that the principal components (or features) were extracted from different group of descriptors (PCR1) and from entire set of descriptors (PCR2). The resulted models were evaluated using cross-validation, chance correlation, application to prediction reduction potential of some test samples and accessing applicability domain. Both MLR approaches represented accurate results however the QSPR model found by MLR1 was statistically more significant. PCR1 approach produced a model as accurate as MLR approaches whereas less accurate results were obtained by PCR2 approach. In overall, the correlation coefficients of cross-validation and prediction of the QSPR models resulted from MLR1, MLR2 and PCR1 approaches were higher than 90%, which show the high ability of the models to predict reduction potential of the studied steroids.

Synthesis of novel anthraquinones: Molecular structure, molecular chemical reactivity descriptors and interactions with DNA as antibiotic and anti-cancer drugs

NASA Astrophysics Data System (ADS)

Al-Otaibi, Jamelah S.; EL Gogary, Tarek M.

2017-02-01

Anthraquinones are well-known anticancer drugs. Anthraquinones anticancer drugs carry out their cytotoxic activities through their interaction with DNA, and inhibition of topoisomerase II activity. Anthraquinones (AQ5 and AQ5H) were synthesized and studied with 1,5-DAAQ by computational and experimental tools. The purpose of this study is to shade more light on mechanism of interaction between anthraquinone DNA affinic agents and different types of DNA. This study will lead to gain of information useful for drug design and development. Molecular structures were optimized using DFT B3LYP/6-31 + G(d). Depending on intramolecular hydrogen bonding interactions four conformers of AQ5 were detected within the range of about 42 kcal/mol. Molecular reactivity of the anthraquinone compounds was explored using global and condensed descriptors (electrophilicity and Fukui functions). NMR and UV-VIS electronic absorption spectra of anthraquinones/DNA were investigated at the physiological pH. The interaction of the anthraquinones (AQ5 and AQ5H) were studied with different DNA namely, calf thymus DNA, (Poly[dA].Poly[dT]) and (Poly[dG].Poly[dC]). UV-VIS electronic absorption spectral data were employed to measure the affinity constants of drug/DNA binding using Scatchard analysis. NMR study confirms qualitatively the drug/DNA interaction in terms of peak shift and broadening.

Predictive Signatures from ToxCast Data for Chronic, Developmental and Reproductive Toxicity Endpoints

EPA Science Inventory

The EPA ToxCast program is using in vitro assay data and chemical descriptors to build predictive models for in vivo toxicity endpoints. In vitro assays measure activity of chemicals against molecular targets such as enzymes and receptors (measured in cell-free and cell-based sys...

Chemistry explained by topology: an alternative approach.

PubMed

Galvez, Jorge; Villar, Vincent M; Galvez-Llompart, Maria; Amigó, José M

2011-05-01

Molecular topology can be considered an application of graph theory in which the molecular structure is characterized through a set of graph-theoretical descriptors called topological indices. Molecular topology has found applications in many different fields, particularly in biology, chemistry, and pharmacology. The first topological index was introduced by H. Wiener in 1947 [1]. Although its very first application was the prediction of the boiling points of the alkanes, the Wiener index has demonstrated since then a predictive capability far beyond that. Along with the Wiener index, in this paper we focus on a few pioneering topological indices, just to illustrate the connection between physicochemical properties and molecular connectivity.

Chemometric modeling of 5-Phenylthiophenecarboxylic acid derivatives as anti-rheumatic agents.

PubMed

Adhikari, Nilanjan; Jana, Dhritiman; Halder, Amit K; Mondal, Chanchal; Maiti, Milan K; Jha, Tarun

2012-09-01

Arthritis involves joint inflammation, synovial proliferation and damage of cartilage. Interleukin-1 undergoes acute and chronic inflammatory mechanisms of arthritis. Non-steroidal anti-inflammatory drugs can produce symptomatic relief but cannot act through mechanisms of arthritis. Diseases modifying anti-rheumatoid drugs reduce the symptoms of arthritis like decrease in pain and disability score, reduction of swollen joints, articular index and serum concentration of acute phage proteins. Recently, some literature references are obtained on molecular modeling of antirheumatic agents. We have tried chemometric modeling through 2D-QSAR studies on a dataset of fifty-one compounds out of which forty-four 5-Phenylthiophenecarboxylic acid derivatives have IL-1 inhibitory activity and forty-six 5-Phenylthiophenecarboxylic acid derivatives have %AIA suppressive activity. The work was done to find out the structural requirements of these anti-rheumatic agents. 2D QSAR models were generated by 2D and 3D descriptors by using multiple linear regression and partial least square method where IL-1 antagonism was considered as the biological activity parameter. Statistically significant models were developed on the training set developed by k-means cluster analysis. Sterimol parameters, electronic interaction at atom number 9, 2D autocorrelation descriptors, information content descriptor, average connectivity index chi-3, radial distribution function, Balaban 3D index and 3D-MoRSE descriptors were found to play crucial roles to modulate IL-1 inhibitory activity. 2D autocorrelation descriptors like Broto-Moreau autocorrelation of topological structure-lag 3 weighted by atomic van der Waals volumes, Geary autocorrelation-lag 7 associated with weighted atomic Sanderson electronegativities and 3D-MoRSE descriptors like 3D-MoRSE-signal 22 related to atomic van der Waals volumes, 3D-MoRSE-signal 28 related to atomic van der Waals volumes and 3D-MoRSE-signal 9 which was unweighted, were found to play important roles to model %AIA suppressive activity.

Mass Spectrometry Based Identification of Geometric Isomers during Metabolic Stability Study of a New Cytotoxic Sulfonamide Derivatives Supported by Quantitative Structure-Retention Relationships

PubMed Central

Belka, Mariusz; Hewelt-Belka, Weronika; Sławiński, Jarosław; Bączek, Tomasz

2014-01-01

A set of 15 new sulphonamide derivatives, presenting antitumor activity have been subjected to a metabolic stability study. The results showed that besides products of biotransformation, some additional peaks occurred in chromatograms. Tandem mass spectrometry revealed the same mass and fragmentation pathway, suggesting that geometric isomerization occurred. Thus, to support this hypothesis, quantitative structure-retention relationships were applied. Human liver microsomes were used as an in vitro model of metabolism. The biotransformation reactions were tracked by liquid chromatography assay and additionally, fragmentation mass spectra were recorded. In silico molecular modeling at a semi-empirical level was conducted as a starting point for molecular descriptor calculations. A quantitative structure-retention relationship model was built applying multiple linear regression based on selected three-dimensional descriptors. The studied compounds revealed high metabolic stability, with a tendency to form hydroxylated biotransformation products. However, significant chemical instability in conditions simulating human body fluids was noticed. According to literature and MS data geometrical isomerization was suggested. The developed in sillico model was able to describe the relationship between the geometry of isomer pairs and their chromatographic retention properties, thus it supported the hypothesis that the observed pairs of peaks are most likely geometric isomers. However, extensive structural investigations are needed to fully identify isomers’ geometry. An effort to describe MS fragmentation pathways of novel chemical structures is often not enough to propose structures of potent metabolites and products of other chemical reactions that can be observed in compound solutions at early drug discovery studies. The results indicate that the relatively non-expensive and not time- and labor-consuming in sillico approach could be a good supportive tool assisting the identification of cis-trans isomers based on retention data. This methodology can be helpful during the structural identification of biotransformation and degradation products of new chemical entities - potential new drugs. PMID:24893169

Calculations of the Electric Fields in Liquid Solutions

PubMed Central

Fried, Stephen D.; Wang, Lee-Ping; Boxer, Steven G.; Ren, Pengyu; Pande, Vijay S.

2014-01-01

The electric field created by a condensed phase environment is a powerful and convenient descriptor for intermolecular interactions. Not only does it provide a unifying language to compare many different types of interactions, but it also possesses clear connections to experimental observables, such as vibrational Stark effects. We calculate here the electric fields experienced by a vibrational chromophore (the carbonyl group of acetophenone) in an array of solvents of diverse polarities using molecular dynamics simulations with the AMOEBA polarizable force field. The mean and variance of the calculated electric fields correlate well with solvent-induced frequency shifts and band broadening, suggesting Stark effects as the underlying mechanism of these key solution phase spectral effects. Compared to fixed-charge and continuum models, AMOEBA was the only model examined that could describe non-polar, polar, and hydrogen bonding environments in a consistent fashion. Nevertheless, we found that fixed-charge force fields and continuum models were able to replicate some results of the polarizable simulations accurately, allowing us to clearly identify which properties and situations require explicit polarization and/or atomistic representations to be modeled properly, and for which properties and situations simpler models are sufficient. We also discuss the ramifications of these results for modeling electrostatics in complex environments, such as proteins. PMID:24304155

QSRR modeling for diverse drugs using different feature selection methods coupled with linear and nonlinear regressions.

PubMed

Goodarzi, Mohammad; Jensen, Richard; Vander Heyden, Yvan

2012-12-01

A Quantitative Structure-Retention Relationship (QSRR) is proposed to estimate the chromatographic retention of 83 diverse drugs on a Unisphere poly butadiene (PBD) column, using isocratic elutions at pH 11.7. Previous work has generated QSRR models for them using Classification And Regression Trees (CART). In this work, Ant Colony Optimization is used as a feature selection method to find the best molecular descriptors from a large pool. In addition, several other selection methods have been applied, such as Genetic Algorithms, Stepwise Regression and the Relief method, not only to evaluate Ant Colony Optimization as a feature selection method but also to investigate its ability to find the important descriptors in QSRR. Multiple Linear Regression (MLR) and Support Vector Machines (SVMs) were applied as linear and nonlinear regression methods, respectively, giving excellent correlation between the experimental, i.e. extrapolated to a mobile phase consisting of pure water, and predicted logarithms of the retention factors of the drugs (logk(w)). The overall best model was the SVM one built using descriptors selected by ACO. Copyright © 2012 Elsevier B.V. All rights reserved.

Integrative Approaches for Predicting in vivo Effects of Chemicals from their Structural Descriptors and the Results of Short-term Biological Assays

PubMed Central

Low, Yen S.; Sedykh, Alexander; Rusyn, Ivan; Tropsha, Alexander

2017-01-01

Cheminformatics approaches such as Quantitative Structure Activity Relationship (QSAR) modeling have been used traditionally for predicting chemical toxicity. In recent years, high throughput biological assays have been increasingly employed to elucidate mechanisms of chemical toxicity and predict toxic effects of chemicals in vivo. The data generated in such assays can be considered as biological descriptors of chemicals that can be combined with molecular descriptors and employed in QSAR modeling to improve the accuracy of toxicity prediction. In this review, we discuss several approaches for integrating chemical and biological data for predicting biological effects of chemicals in vivo and compare their performance across several data sets. We conclude that while no method consistently shows superior performance, the integrative approaches rank consistently among the best yet offer enriched interpretation of models over those built with either chemical or biological data alone. We discuss the outlook for such interdisciplinary methods and offer recommendations to further improve the accuracy and interpretability of computational models that predict chemical toxicity. PMID:24805064

QSTR of the toxicity of some organophosphorus compounds by using the quantum chemical and topological descriptors.

PubMed

Senior, Samir A; Madbouly, Magdy D; El massry, Abdel-Moneim

2011-09-01

Quantum chemical and topological descriptors of some organophosphorus compounds (OP) were correlated with their toxicity LD(50) as a dermal. The quantum chemical parameters were obtained using B3LYP/LANL2DZdp-ECP optimization. Using linear regression analysis, equations were derived to calculate the theoretical LD(50) of the studied compounds. The inclusion of quantum parameters, having both charge indices and topological indices, affects the toxicity of the studied compounds resulting in high correlation coefficient factors for the obtained equations. Two of the new four firstly supposed descriptors give higher correlation coefficients namely the Heteroatom Corrected Extended Connectivity Randic index ((1)X(HCEC)) and the Density Randic index ((1)X(Den)). The obtained linear equations were applied to predict the toxicity of some related structures. It was found that the sulfur atoms in these compounds must be replaced by oxygen atoms to achieve improved toxicity. Copyright © 2011 Elsevier Ltd. All rights reserved.

The QSAR study of flavonoid-metal complexes scavenging rad OH free radical

NASA Astrophysics Data System (ADS)

Wang, Bo-chu; Qian, Jun-zhen; Fan, Ying; Tan, Jun

2014-10-01

Flavonoid-metal complexes have antioxidant activities. However, quantitative structure-activity relationships (QSAR) of flavonoid-metal complexes and their antioxidant activities has still not been tackled. On the basis of 21 structures of flavonoid-metal complexes and their antioxidant activities for scavenging rad OH free radical, we optimised their structures using Gaussian 03 software package and we subsequently calculated and chose 18 quantum chemistry descriptors such as dipole, charge and energy. Then we chose several quantum chemistry descriptors that are very important to the IC50 of flavonoid-metal complexes for scavenging rad OH free radical through method of stepwise linear regression, Meanwhile we obtained 4 new variables through the principal component analysis. Finally, we built the QSAR models based on those important quantum chemistry descriptors and the 4 new variables as the independent variables and the IC50 as the dependent variable using an Artificial Neural Network (ANN), and we validated the two models using experimental data. These results show that the two models in this paper are reliable and predictable.

Method of data communications with reduced latency

DOEpatents

Blocksome, Michael A; Parker, Jeffrey J

2013-11-05

Data communications with reduced latency, including: writing, by a producer, a descriptor and message data into at least two descriptor slots of a descriptor buffer, the descriptor buffer comprising allocated computer memory segmented into descriptor slots, each descriptor slot having a fixed size, the descriptor buffer having a header pointer that identifies a next descriptor slot to be processed by a DMA controller, the descriptor buffer having a tail pointer that identifies a descriptor slot for entry of a next descriptor in the descriptor buffer; recording, by the producer, in the descriptor a value signifying that message data has been written into descriptor slots; and setting, by the producer, in dependence upon the recorded value, a tail pointer to point to a next open descriptor slot.

A reactive, scalable, and transferable model for molecular energies from a neural network approach based on local information

NASA Astrophysics Data System (ADS)

Unke, Oliver T.; Meuwly, Markus

2018-06-01

Despite the ever-increasing computer power, accurate ab initio calculations for large systems (thousands to millions of atoms) remain infeasible. Instead, approximate empirical energy functions are used. Most current approaches are either transferable between different chemical systems, but not particularly accurate, or they are fine-tuned to a specific application. In this work, a data-driven method to construct a potential energy surface based on neural networks is presented. Since the total energy is decomposed into local atomic contributions, the evaluation is easily parallelizable and scales linearly with system size. With prediction errors below 0.5 kcal mol-1 for both unknown molecules and configurations, the method is accurate across chemical and configurational space, which is demonstrated by applying it to datasets from nonreactive and reactive molecular dynamics simulations and a diverse database of equilibrium structures. The possibility to use small molecules as reference data to predict larger structures is also explored. Since the descriptor only uses local information, high-level ab initio methods, which are computationally too expensive for large molecules, become feasible for generating the necessary reference data used to train the neural network.

DFT analysis and spectral characteristics of Celecoxib a potent COX-2 inhibitor

NASA Astrophysics Data System (ADS)

Vijayakumar, B.; Kannappan, V.; Sathyanarayanamoorthi, V.

2016-10-01

Extensive quantum mechanical studies are carried out on Celecoxib (CXB), a new generation drug to understand the vibrational and electronic spectral characteristics of the molecule. The vibrational frequencies of CXB are computed by HF and B3LYP methods with 6-311++G (d, p) basis set. The theoretical scaled vibrational frequencies have been assigned and they agreed satisfactorily with experimental FT-IR and Raman frequencies. The theoretical maximum wavelength of absorption of CXB are calculated in water and ethanol by TD-DFT method and these values are compared with experimentally determined λmax values. The spectral and Natural bonds orbital (NBO) analysis in conjunction with spectral data established the presence of intra molecular interactions such as mesomeric, hyperconjugative and steric effects in CXB. The electron density at various positions and reactivity descriptors of CXB indicate that the compound functions as a nucleophile and establish that aromatic ring system present in the molecule is the site of drug action. Electronic distribution and HOMO - LUMO energy values of CXB are discussed in terms of intra-molecular interactions. Computed values of Mulliken charges and thermodynamic properties of CXB are reported.

Quantitative structure-property relationship (correlation analysis) of phosphonic acid-based chelates in design of MRI contrast agent.

PubMed

Tiwari, Anjani K; Ojha, Himanshu; Kaul, Ankur; Dutta, Anupama; Srivastava, Pooja; Shukla, Gauri; Srivastava, Rakesh; Mishra, Anil K

2009-07-01

Nuclear magnetic resonance imaging is a very useful tool in modern medical diagnostics, especially when gadolinium (III)-based contrast agents are administered to the patient with the aim of increasing the image contrast between normal and diseased tissues. With the use of soft modelling techniques such as quantitative structure-activity relationship/quantitative structure-property relationship after a suitable description of their molecular structure, we have studied a series of phosphonic acid for designing new MRI contrast agent. Quantitative structure-property relationship studies with multiple linear regression analysis were applied to find correlation between different calculated molecular descriptors of the phosphonic acid-based chelating agent and their stability constants. The final quantitative structure-property relationship mathematical models were found as--quantitative structure-property relationship Model for phosphonic acid series (Model 1)--log K(ML) = {5.00243(+/-0.7102)}- MR {0.0263(+/-0.540)}n = 12 l r l = 0.942 s = 0.183 F = 99.165 quantitative structure-property relationship Model for phosphonic acid series (Model 2)--log K(ML) = {5.06280(+/-0.3418)}- MR {0.0252(+/- .198)}n = 12 l r l = 0.956 s = 0.186 F = 99.256.

Corneal and conjunctival drug permeability: Systematic comparison and pharmacokinetic impact in the eye.

PubMed

Ramsay, Eva; Del Amo, Eva M; Toropainen, Elisa; Tengvall-Unadike, Unni; Ranta, Veli-Pekka; Urtti, Arto; Ruponen, Marika

2018-07-01

On the surface of the eye, both the cornea and conjunctiva are restricting ocular absorption of topically applied drugs, but barrier contributions of these two membranes have not been systemically compared. Herein, we studied permeability of 32 small molecular drug compounds across an isolated porcine cornea and built a quantitative structure-property relationship (QSPR) model for the permeability. Corneal drug permeability (data obtained for 25 drug molecules) showed a 52-fold range in permeability (0.09-4.70 × 10 -6  cm/s) and the most important molecular descriptors in predicting the permeability were hydrogen bond donor, polar surface area and halogen ratio. Corneal permeability values were compared to their conjunctival drug permeability values. Ocular drug bioavailability and systemic absorption via conjunctiva were predicted for this drug set with pharmacokinetic calculations. Drug bioavailability in the aqueous humour was simulated to be <5% and trans-conjunctival systemic absorption was 34-79% of the dose. Loss of drug across the conjunctiva to the blood circulation restricts significantly ocular drug bioavailability and, therefore, ocular absorption does not increase proportionally with the increasing corneal drug permeability. Copyright © 2018 Elsevier B.V. All rights reserved.

QSPR models of n-octanol/water partition coefficients and aqueous solubility of halogenated methyl-phenyl ethers by DFT method.

PubMed

Zeng, Xiao-Lan; Wang, Hong-Jun; Wang, Yan

2012-02-01

The possible molecular geometries of 134 halogenated methyl-phenyl ethers were optimized at B3LYP/6-31G(*) level with Gaussian 98 program. The calculated structural parameters were taken as theoretical descriptors to establish two new novel QSPR models for predicting aqueous solubility (-lgS(w,l)) and n-octanol/water partition coefficient (lgK(ow)) of halogenated methyl-phenyl ethers. The two models achieved in this work both contain three variables: energy of the lowest unoccupied molecular orbital (E(LUMO)), most positive atomic partial charge in molecule (q(+)), and quadrupole moment (Q(yy) or Q(zz)), of which R values are 0.992 and 0.970 respectively, their standard errors of estimate in modeling (SD) are 0.132 and 0.178, respectively. The results of leave-one-out (LOO) cross-validation for training set and validation with external test sets both show that the models obtained exhibited optimum stability and good predictive power. We suggests that two QSPR models derived here can be used to predict S(w,l) and K(ow) accurately for non-tested halogenated methyl-phenyl ethers congeners. Copyright © 2011 Elsevier Ltd. All rights reserved.

Density functional theory and surface reactivity study of bimetallic AgnYm (n+m = 10) clusters

NASA Astrophysics Data System (ADS)

Hussain, Riaz; Hussain, Abdullah Ijaz; Chatha, Shahzad Ali Shahid; Hussain, Riaz; Hanif, Usman; Ayub, Khurshid

2018-06-01

Density functional theory calculations have been performed on pure silver (Agn), yttrium (Ym) and bimetallic silver yttrium clusters AgnYm (n + m = 2-10) for reactivity descriptors in order to realize sites for nucleophilic and electrophilic attack. The reactivity descriptors of the clusters, studied as a function of cluster size and shape, reveal the presence of different type of reactive sites in a cluster. The size and shape of the pure silver, yttrium and bimetallic silver yttrium cluster (n = 2-10) strongly influences the number and position of active sites for an electrophilic and/or nucleophilic attack. The trends of reactivities through reactivity descriptors are confirmed through comparison with experimental data for CO binding with silver clusters. Moreover, the adsorption of CO on bimetallic silver yttrium clusters is also evaluated. The trends of binding energies support the reactivity descriptors values. Doping of pure cluster with the other element also influence the hardness, softness and chemical reactivity of the clusters. The softness increases as we increase the number of silver atoms in the cluster, whereas the hardness decreases. The chemical reactivity increases with silver doping whereas it decreases by increasing yttrium concentration. Silver atoms are nucleophilic in small clusters but changed to electrophilic in large clusters.

QSAR, QSPR and QSRR in Terms of 3-D-MoRSE Descriptors for In Silico Screening of Clofibric Acid Analogues.

PubMed

Di Tullio, Maurizio; Maccallini, Cristina; Ammazzalorso, Alessandra; Giampietro, Letizia; Amoroso, Rosa; De Filippis, Barbara; Fantacuzzi, Marialuigia; Wiczling, Paweł; Kaliszan, Roman

2012-07-01

A series of 27 analogues of clofibric acid, mostly heteroarylalkanoic derivatives, have been analyzed by a novel high-throughput reversed-phase HPLC method employing combined gradient of eluent's pH and organic modifier content. The such determined hydrophobicity (lipophilicity) parameters, log kw , and acidity constants, pKa , were subjected to multiple regression analysis to get a QSRR (Quantitative StructureRetention Relationships) and a QSPR (Quantitative Structure-Property Relationships) equation, respectively, describing these pharmacokinetics-determining physicochemical parameters in terms of the calculation chemistry derived structural descriptors. The previously determined in vitro log EC50 values - transactivation activity towards PPARα (human Peroxisome Proliferator-Activated Receptor α) - have also been described in a QSAR (Quantitative StructureActivity Relationships) equation in terms of the 3-D-MoRSE descriptors (3D-Molecule Representation of Structures based on Electron diffraction descriptors). The QSAR model derived can serve for an a priori prediction of bioactivity in vitro of any designed analogue, whereas the QSRR and the QSPR models can be used to evaluate lipophilicity and acidity, respectively, of the compounds, and hence to rational guide selection of structures of proper pharmacokinetics. Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Predicting Cell Association of Surface-Modified Nanoparticles Using Protein Corona Structure - Activity Relationships (PCSAR).

PubMed

Kamath, Padmaja; Fernandez, Alberto; Giralt, Francesc; Rallo, Robert

2015-01-01

Nanoparticles are likely to interact in real-case application scenarios with mixtures of proteins and biomolecules that will absorb onto their surface forming the so-called protein corona. Information related to the composition of the protein corona and net cell association was collected from literature for a library of surface-modified gold and silver nanoparticles. For each protein in the corona, sequence information was extracted and used to calculate physicochemical properties and statistical descriptors. Data cleaning and preprocessing techniques including statistical analysis and feature selection methods were applied to remove highly correlated, redundant and non-significant features. A weighting technique was applied to construct specific signatures that represent the corona composition for each nanoparticle. Using this basic set of protein descriptors, a new Protein Corona Structure-Activity Relationship (PCSAR) that relates net cell association with the physicochemical descriptors of the proteins that form the corona was developed and validated. The features that resulted from the feature selection were in line with already published literature, and the computational model constructed on these features had a good accuracy (R(2)LOO=0.76 and R(2)LMO(25%)=0.72) and stability, with the advantage that the fingerprints based on physicochemical descriptors were independent of the specific proteins that form the corona.

Genetic algorithm applied to the selection of factors in principal component-artificial neural networks: application to QSAR study of calcium channel antagonist activity of 1,4-dihydropyridines (nifedipine analogous).

PubMed

Hemmateenejad, Bahram; Akhond, Morteza; Miri, Ramin; Shamsipur, Mojtaba

2003-01-01

A QSAR algorithm, principal component-genetic algorithm-artificial neural network (PC-GA-ANN), has been applied to a set of newly synthesized calcium channel blockers, which are of special interest because of their role in cardiac diseases. A data set of 124 1,4-dihydropyridines bearing different ester substituents at the C-3 and C-5 positions of the dihydropyridine ring and nitroimidazolyl, phenylimidazolyl, and methylsulfonylimidazolyl groups at the C-4 position with known Ca(2+) channel binding affinities was employed in this study. Ten different sets of descriptors (837 descriptors) were calculated for each molecule. The principal component analysis was used to compress the descriptor groups into principal components. The most significant descriptors of each set were selected and used as input for the ANN. The genetic algorithm (GA) was used for the selection of the best set of extracted principal components. A feed forward artificial neural network with a back-propagation of error algorithm was used to process the nonlinear relationship between the selected principal components and biological activity of the dihydropyridines. A comparison between PC-GA-ANN and routine PC-ANN shows that the first model yields better prediction ability.

Lagrangian descriptors in dissipative systems.

PubMed

Junginger, Andrej; Hernandez, Rigoberto

2016-11-09

The reaction dynamics of time-dependent systems can be resolved through a recrossing-free dividing surface associated with the transition state trajectory-that is, the unique trajectory which is bound to the barrier region for all time in response to a given time-dependent potential. A general procedure based on the minimization of Lagrangian descriptors has recently been developed by Craven and Hernandez [Phys. Rev. Lett., 2015, 115, 148301] to construct this particular trajectory without requiring perturbative expansions relative to the naive transition state point at the top of the barrier. The extension of the method to account for dissipation in the equations of motion requires additional considerations established in this paper because the calculation of the Lagrangian descriptor involves the integration of trajectories in forward and backward time. The two contributions are in general very different because the friction term can act as a source (in backward time) or sink (in forward time) of energy, leading to the possibility that information about the phase space structure may be lost due to the dominance of only one of the terms. To compensate for this effect, we introduce a weighting scheme within the Lagrangian descriptor and demonstrate that for thermal Langevin dynamics it preserves the essential phase space structures, while they are lost in the nonweighted case.

Physicochemical properties/descriptors governing the solubility and partitioning of chemicals in water-solvent-gas systems. Part 1. Partitioning between octanol and air.

PubMed

Raevsky, O A; Grigor'ev, V J; Raevskaja, O E; Schaper, K-J

2006-06-01

QSPR analyses of a data set containing experimental partition coefficients in the three systems octanol-water, water-gas, and octanol-gas for 98 chemicals have shown that it is possible to calculate any partition coefficient in the system 'gas phase/octanol/water' by three different approaches: (1) from experimental partition coefficients obtained in the corresponding two other subsystems. However, in many cases these data may not be available. Therefore, a solution may be approached (2), a traditional QSPR analysis based on e.g. HYBOT descriptors (hydrogen bond acceptor and donor factors, SigmaCa and SigmaCd, together with polarisability alpha, a steric bulk effect descriptor) and supplemented with substructural indicator variables. (3) A very promising approach which is a combination of the similarity concept and QSPR based on HYBOT descriptors. In this approach observed partition coefficients of structurally nearest neighbours of a compound-of-interest are used. In addition, contributions arising from differences in alpha, SigmaCa, and SigmaCd values between the compound-of-interest and its nearest neighbour(s), respectively, are considered. In this investigation highly significant relationships were obtained by approaches (1) and (3) for the octanol/gas phase partition coefficient (log Log).

Evidence for a strong sulfur-aromatic interaction derived from crystallographic data.

PubMed

Zauhar, R J; Colbert, C L; Morgan, R S; Welsh, W J

2000-03-01

We have uncovered new evidence for a significant interaction between divalent sulfur atoms and aromatic rings. Our study involves a statistical analysis of interatomic distances and other geometric descriptors derived from entries in the Cambridge Crystallographic Database (F. H. Allen and O. Kennard, Chem. Design Auto. News, 1993, Vol. 8, pp. 1 and 31-37). A set of descriptors was defined sufficient in number and type so as to elucidate completely the preferred geometry of interaction between six-membered aromatic carbon rings and divalent sulfurs for all crystal structures of nonmetal-bearing organic compounds present in the database. In order to test statistical significance, analogous probability distributions for the interaction of the moiety X-CH(2)-X with aromatic rings were computed, and taken a priori to correspond to the null hypothesis of no significant interaction. Tests of significance were carried our pairwise between probability distributions of sulfur-aromatic interaction descriptors and their CH(2)-aromatic analogues using the Smirnov-Kolmogorov nonparametric test (W. W. Daniel, Applied Nonparametric Statistics, Houghton-Mifflin: Boston, New York, 1978, pp. 276-286), and in all cases significance at the 99% confidence level or better was observed. Local maxima of the probability distributions were used to define a preferred geometry of interaction between the divalent sulfur moiety and the aromatic ring. Molecular mechanics studies were performed in an effort to better understand the physical basis of the interaction. This study confirms observations based on statistics of interaction of amino acids in protein crystal structures (R. S. Morgan, C. E. Tatsch, R. H. Gushard, J. M. McAdon, and P. K. Warme, International Journal of Peptide Protein Research, 1978, Vol. 11, pp. 209-217; R. S. Morgan and J. M. McAdon, International Journal of Peptide Protein Research, 1980, Vol. 15, pp. 177-180; K. S. C. Reid, P. F. Lindley, and J. M. Thornton, FEBS Letters, 1985, Vol. 190, pp. 209-213), as well as studies involving molecular mechanics (G. Nemethy and H. A. Scheraga, Biochemistry and Biophysics Research Communications, 1981, Vol. 98, pp. 482-487) and quantum chemical calculations (B. V. Cheney, M. W. Schulz, and J. Cheney, Biochimica Biophysica Acta, 1989, Vol. 996, pp.116-124; J. Pranata, Bioorganic Chemistry, 1997, Vol. 25, pp. 213-219)-all of which point to the possible importance of the sulfur-aromatic interaction. However, the preferred geometry of the interaction, as determined from our analysis of the small-molecule crystal data, differs significantly from that found by other approaches. Copyright 2000 John Wiley & Sons, Inc.

Novel naïve Bayes classification models for predicting the carcinogenicity of chemicals.

PubMed

Zhang, Hui; Cao, Zhi-Xing; Li, Meng; Li, Yu-Zhi; Peng, Cheng

2016-11-01

The carcinogenicity prediction has become a significant issue for the pharmaceutical industry. The purpose of this investigation was to develop a novel prediction model of carcinogenicity of chemicals by using a naïve Bayes classifier. The established model was validated by the internal 5-fold cross validation and external test set. The naïve Bayes classifier gave an average overall prediction accuracy of 90 ± 0.8% for the training set and 68 ± 1.9% for the external test set. Moreover, five simple molecular descriptors (e.g., AlogP, Molecular weight (M W ), No. of H donors, Apol and Wiener) considered as important for the carcinogenicity of chemicals were identified, and some substructures related to the carcinogenicity were achieved. Thus, we hope the established naïve Bayes prediction model could be applied to filter early-stage molecules for this potential carcinogenicity adverse effect; and the identified five simple molecular descriptors and substructures of carcinogens would give a better understanding of the carcinogenicity of chemicals, and further provide guidance for medicinal chemists in the design of new candidate drugs and lead optimization, ultimately reducing the attrition rate in later stages of drug development. Copyright © 2016 Elsevier Ltd. All rights reserved.

Reactivities of some thiol collectors and their interactions with Ag (+1) ion by molecular modeling

NASA Astrophysics Data System (ADS)

Yekeler, Hulya; Yekeler, Meftuni

2004-09-01

The most commonly used collectors for sulfide minerals in the mining industry are the thiol collectors for the recovery of these minerals from their associated gangues by froth flotation. For this reason, a great deal of attention has been paid to understand the attachment mechanism of thiol collectors to metal sulfide surfaces. The density functional theory (DFT) calculations at the B3LYP/3-21G* and B3LYP/6-31++G** levels were employed to propose the flotation responses of these thiol collectors, namely, diethyl dithiocarbamate, ethyl dithiocarbamate, ethyl dithiocarbonate, ethyl trithiocarbonate and ethyl dithiophosphate ions, and to study the interaction energies of these collectors with Ag (+1) ion in connection to acanthite (Ag 2S) mineral. The calculated interaction energies, Δ E, were interpreted in terms of the highest occupied molecular orbital (HOMO) energies of the isolated collector ions. The results show that the HOMOs are strongly localized to the sulfur atoms and the HOMO energies can be used as a reactivity descriptor for the flotation ability of the thiol collectors. Using the HOMO and Δ E energies, the reactivity order of the collectors is found to be (C 2H 5) 2NCS 2- > C 2H 5NHCS 2- > C 2H 5OCS 2- > C 2H 5SCS 2- > (C 2H 5O)(OH)PS 2-. The theoretically obtained results are in good agreement with the experimental data reported.

Facile synthesis, single crystal analysis, and computational studies of sulfanilamide derivatives

NASA Astrophysics Data System (ADS)

Tahir, Muhammad Nawaz; Khalid, Muhammad; Islam, Ayesha; Ali Mashhadi, Syed Muddassir; Braga, Ataualpa A. C.

2017-01-01

Antibacterial resistance is a worldwide problem. Sulfanilamide is widely used antibacterial. For the first time, we report here a simple method for the derivative synthesis of the title drugs, single crystal XRD and density functional theory (DFT) studies. The optimized molecular structure, natural bond orbital (NBO), frontier molecular orbitals (FMOs) molecular electrostatic potential studies (MEP) and Mulliken population analysis (MPA) have been performed using M06-2X/6-31G(d, p). The FT-IR spectra and thermodynamic parameters were calculated at M06-2X/6-311 + G(2d,p) and B3LYP/6-31G(d, p) levels respectively, while, the UV-Vis analysis was performed using TD-DFT/B3LYP/6-31G(d, p) method. The experimental FT-IR spectra of both compounds were also carried out to reconfirm sbnd H⋯Osbnd hydrogen bonds. The DFT optimized parameters exhibiting good agreement with the experimental data. NBO analysis explored the hyper conjugative interaction and stability of title crystals, especially, reconfirmed the existence of sbnd H⋯Osbnd hydrogen bonds between the dimers. The FT-IR, thermodynamic parameters, MEP and MPA also revealed the hydrogen bonding detail is harmonious to XRD data. As a matter of the fact, the hydrogen bonding is a significant parameter for the understanding and design of molecular crystals, subsequently; it can also play a vital role in the supramolecular chemistry. Moreover, the global reactivity descriptors suggest that title compounds might be bioactive.

Synthesis, X-ray crystallography, spectroscopic (FT-IR, 1H &13C NMR and UV), computational (DFT/B3LYP) and enzymes inhibitory studies of 7-hydroximinocholest-5-en-3-ol acetate

NASA Astrophysics Data System (ADS)

Ahmad, Faheem; Parveen, Mehtab; Alam, Mahboob; Azaz, Shaista; Malla, Ali Mohammed; Alam, Mohammad Jane; Lee, Dong-Ung; Ahmad, Shabbir

2016-07-01

The present study reports the synthesis of 7-Hydroximinocholest-5-en-3-ol acetate (syn. 3β-acetoxycholest-5-en-7-one oxime; in general, steroidal oxime). The identity of steroidal molecule was confirmed by NMR, FT-IR, MS, CHN microanalysis and X-ray crystallography. DFT calculations on the titled molecule have been performed. The molecular structure and spectra interpreted by Gaussian hybrid computational analysis theory (B3LYP) are found to be in good correlation with the experimental data obtained from the various spectrophotometric techniques. The vibrational bands appearing in the FTIR are assigned with great accuracy using harmonic frequencies along with intensities and animated modes. Molecular properties like HOMO-LUMO analysis, chemical reactivity descriptors, MEP mapping, dipole moment and natural atomic charges have been presented at the same level of theory. Moreover, the Hirshfeld analysis was carried out to ascertain the secondary interactions and associated 2D fingerprint plots. The percentages of various interactions are pictorialized by fingerprint plots of Hirshfeld surface. Steroidal oxime exhibited promising inhibitory activity against acetylcholinesterase (AChE) as compared to the reference drug, tacrine. Molecular docking was performed to introduce steroidal molecules into the X-ray crystal structures of acetylcholinesterase at the active site to find out the probable binding mode. The results of molecular docking admitted that steroidal oxime may exhibit enzyme inhibitor activity.

Analyzing the substitution effect on the CoMFA results within the framework of density functional theory (DFT).

PubMed

Morales-Bayuelo, Alejandro

2016-07-01

Though QSAR was originally developed in the context of physical organic chemistry, it has been applied very extensively to chemicals (drugs) which act on biological systems, in this idea one of the most important QSAR methods is the 3D QSAR model. However, due to the complexity of understanding the results it is necessary to postulate new methodologies to highlight their physical-chemical meaning. In this sense, this work postulates new insights to understand the CoMFA results using molecular quantum similarity and chemical reactivity descriptors within the framework of density functional theory. To obtain these insights a simple theoretical scheme involving quantum similarity (overlap, coulomb operators, their euclidean distances) and chemical reactivity descriptors such as chemical potential (μ), hardness (ɳ), softness (S), electrophilicity (ω), and the Fukui functions, was used to understand the substitution effect. In this sense, this methodology can be applied to analyze the biological activity and the stabilization process in the non-covalent interactions on a particular molecular set taking a reference compound.

Variability among Capsicum baccatum accessions from Goiás, Brazil, assessed by morphological traits and molecular markers.

PubMed

Martinez, A L A; Araújo, J S P; Ragassi, C F; Buso, G S C; Reifschneider, F J B

2017-07-06

Capsicum peppers are native to the Americas, with Brazil being a significant diversity center. Capsicum baccatum accessions at Instituto Federal (IF) Goiano represent a portion of the species genetic resources from central Brazil. We aimed to characterize a C. baccatum working collection comprising 27 accessions and 3 commercial cultivars using morphological traits and molecular markers to describe its genetic and morphological variability and verify the occurrence of duplicates. This set included 1 C. baccatum var. praetermissum and 29 C. baccatum var. pendulum with potential for use in breeding programs. Twenty-two morphological descriptors, 57 inter-simple sequence repeat, and 34 random amplified polymorphic DNA markers were used. Genetic distance was calculated through the Jaccard similarity index and genetic variability through cluster analysis using the unweighted pair group method with arithmetic mean, resulting in dendrograms for both morphological analysis and molecular analysis. Genetic variability was found among C. baccatum var. pendulum accessions, and the distinction between the two C. baccatum varieties was evident in both the morphological and molecular analyses. The 29 C. baccatum var. pendulum genotypes clustered in four groups according to fruit type in the morphological analysis. They formed seven groups in the molecular analysis, without a clear correspondence with morphology. No duplicates were found. The results describe the genetic and morphological variability, provide a detailed characterization of genotypes, and discard the possibility of duplicates within the IF Goiano C. baccatum L. collection. This study will foment the use of this germplasm collection in C. baccatum breeding programs.

Derivatives in discrete mathematics: a novel graph-theoretical invariant for generating new 2/3D molecular descriptors. I. Theory and QSPR application

NASA Astrophysics Data System (ADS)

Marrero-Ponce, Yovani; Santiago, Oscar Martínez; López, Yoan Martínez; Barigye, Stephen J.; Torrens, Francisco

2012-11-01

In this report, we present a new mathematical approach for describing chemical structures of organic molecules at atomic-molecular level, proposing for the first time the use of the concept of the derivative ( partial ) of a molecular graph (MG) with respect to a given event ( E), to obtain a new family of molecular descriptors (MDs). With this purpose, a new matrix representation of the MG, which generalizes graph's theory's traditional incidence matrix, is introduced. This matrix, denominated the generalized incidence matrix, Q, arises from the Boolean representation of molecular sub- graphs that participate in the formation of the graph molecular skeleton MG and could be complete (representing all possible connected sub-graphs) or constitute sub-graphs of determined orders or types as well as a combination of these. The Q matrix is a non-quadratic and unsymmetrical in nature, its columns ( n) and rows ( m) are conditions (letters) and collection of conditions (words) with which the event occurs. This non-quadratic and unsymmetrical matrix is transformed, by algebraic manipulation, to a quadratic and symmetric matrix known as relations frequency matrix, F, which characterizes the participation intensity of the conditions (letters) in the events (words). With F, we calculate the derivative over a pair of atomic nuclei. The local index for the atomic nuclei i, Δ i , can therefore be obtained as a linear combination of all the pair derivatives of the atomic nuclei i with all the rest of the j's atomic nuclei. Here, we also define new strategies that generalize the present form of obtaining global or local (group or atom-type) invariants from atomic contributions (local vertex invariants, LOVIs). In respect to this, metric (norms), means and statistical invariants are introduced. These invariants are applied to a vector whose components are the values Δ i for the atomic nuclei of the molecule or its fragments. Moreover, with the purpose of differentiating among different atoms, an atomic weighting scheme (atom-type labels) is used in the formation of the matrix Q or in LOVIs state. The obtained indices were utilized to describe the partition coefficient (Log P) and the reactivity index (Log K) of the 34 derivatives of 2-furylethylenes. In all the cases, our MDs showed better statistical results than those previously obtained using some of the most used families of MDs in chemometric practice. Therefore, it has been demonstrated to that the proposed MDs are useful in molecular design and permit obtaining easier and robust mathematical models than the majority of those reported in the literature. All this range of mentioned possibilities open "the doors" to the creation of a new family of MDs, using the graph derivative, and avail a new tool for QSAR/QSPR and molecular diversity/similarity studies.

Derivatives in discrete mathematics: a novel graph-theoretical invariant for generating new 2/3D molecular descriptors. I. Theory and QSPR application.

PubMed

Marrero-Ponce, Yovani; Santiago, Oscar Martínez; López, Yoan Martínez; Barigye, Stephen J; Torrens, Francisco

2012-11-01

In this report, we present a new mathematical approach for describing chemical structures of organic molecules at atomic-molecular level, proposing for the first time the use of the concept of the derivative ([Formula: see text]) of a molecular graph (MG) with respect to a given event (E), to obtain a new family of molecular descriptors (MDs). With this purpose, a new matrix representation of the MG, which generalizes graph's theory's traditional incidence matrix, is introduced. This matrix, denominated the generalized incidence matrix, Q, arises from the Boolean representation of molecular sub-graphs that participate in the formation of the graph molecular skeleton MG and could be complete (representing all possible connected sub-graphs) or constitute sub-graphs of determined orders or types as well as a combination of these. The Q matrix is a non-quadratic and unsymmetrical in nature, its columns (n) and rows (m) are conditions (letters) and collection of conditions (words) with which the event occurs. This non-quadratic and unsymmetrical matrix is transformed, by algebraic manipulation, to a quadratic and symmetric matrix known as relations frequency matrix, F, which characterizes the participation intensity of the conditions (letters) in the events (words). With F, we calculate the derivative over a pair of atomic nuclei. The local index for the atomic nuclei i, Δ(i), can therefore be obtained as a linear combination of all the pair derivatives of the atomic nuclei i with all the rest of the j's atomic nuclei. Here, we also define new strategies that generalize the present form of obtaining global or local (group or atom-type) invariants from atomic contributions (local vertex invariants, LOVIs). In respect to this, metric (norms), means and statistical invariants are introduced. These invariants are applied to a vector whose components are the values Δ(i) for the atomic nuclei of the molecule or its fragments. Moreover, with the purpose of differentiating among different atoms, an atomic weighting scheme (atom-type labels) is used in the formation of the matrix Q or in LOVIs state. The obtained indices were utilized to describe the partition coefficient (Log P) and the reactivity index (Log K) of the 34 derivatives of 2-furylethylenes. In all the cases, our MDs showed better statistical results than those previously obtained using some of the most used families of MDs in chemometric practice. Therefore, it has been demonstrated to that the proposed MDs are useful in molecular design and permit obtaining easier and robust mathematical models than the majority of those reported in the literature. All this range of mentioned possibilities open "the doors" to the creation of a new family of MDs, using the graph derivative, and avail a new tool for QSAR/QSPR and molecular diversity/similarity studies.

Synthesis and quantitative structure-activity relationship (QSAR) study of novel isoxazoline and oxime derivatives of podophyllotoxin as insecticidal agents.

PubMed

Wang, Yi; Shao, Yonghua; Wang, Yangyang; Fan, Lingling; Yu, Xiang; Zhi, Xiaoyan; Yang, Chun; Qu, Huan; Yao, Xiaojun; Xu, Hui

2012-08-29

In continuation of our program aimed at the discovery and development of natural-product-based insecticidal agents, 33 isoxazoline and oxime derivatives of podophyllotoxin modified in the C and D rings were synthesized and their structures were characterized by Proton nuclear magnetic resonance ((1)H NMR), high-resolution mass spectrometry (HRMS), electrospray ionization-mass spectrometry (ESI-MS), optical rotation, melting point (mp), and infrared (IR) spectroscopy. The stereochemical configurations of compounds 5e, 5f, and 9f were unambiguously determined by X-ray crystallography. Their insecticidal activity was evaluated against the pre-third-instar larvae of northern armyworm, Mythimna separata (Walker), in vivo. Compounds 5e, 9c, 11g, and 11h especially exhibited more promising insecticidal activity than toosendanin, a commercial botanical insecticide extracted from Melia azedarach . A genetic algorithm combined with multiple linear regression (GA-MLR) calculation is performed by the MOBY DIGS package. Five selected descriptors are as follows: one two-dimensional (2D) autocorrelation descriptor (GATS4e), one edge adjacency indice (EEig06x), one RDF descriptor (RDF080v), one three-dimensional (3D) MoRSE descriptor (Mor09v), and one atom-centered fragment (H-052) descriptor. Quantitative structure-activity relationship studies demonstrated that the insecticidal activity of these compounds was mainly influenced by many factors, such as electronic distribution, steric factors, etc. For this model, the standard deviation error in prediction (SDEP) is 0.0592, the correlation coefficient (R(2)) is 0.861, and the leave-one-out cross-validation correlation coefficient (Q(2)loo) is 0.797.

Electrospray Ionization Efficiency Is Dependent on Different Molecular Descriptors with Respect to Solvent pH and Instrumental Configuration

PubMed Central

Kiontke, Andreas; Oliveira-Birkmeier, Ariana; Opitz, Andreas

2016-01-01

Over the past decades, electrospray ionization for mass spectrometry (ESI-MS) has become one of the most commonly employed techniques in analytical chemistry, mainly due to its broad applicability to polar and semipolar compounds and the superior selectivity which is achieved in combination with high resolution separation techniques. However, responsiveness of an analytical method also determines its suitability for the quantitation of chemical compounds; and in electrospray ionization for mass spectrometry, it can vary significantly among different analytes with identical solution concentrations. Therefore, we investigated the ESI-response behavior of 56 nitrogen-containing compounds including aromatic amines and pyridines, two compound classes of high importance to both, synthetic organic chemistry as well as to pharmaceutical sciences. These compounds are increasingly analyzed employing ESI mass spectrometry detection due to their polar, basic character. Signal intensities of the peaks from the protonated molecular ion (MH+) were acquired under different conditions and related to compound properties such as basicity, polarity, volatility and molecular size exploring their quantitative impact on ionization efficiency. As a result, we found that though solution basicity of a compound is the main factor initially determining the ESI response of the protonated molecular ion, other factors such as polarity and vaporability become more important under acidic solvent conditions and may nearly outweigh the importance of basicity under these conditions. Moreover, we show that different molecular descriptors may become important when using different types of instruments for such investigations, a fact not detailed so far in the available literature. PMID:27907110

ToxiM: A Toxicity Prediction Tool for Small Molecules Developed Using Machine Learning and Chemoinformatics Approaches.

PubMed

Sharma, Ashok K; Srivastava, Gopal N; Roy, Ankita; Sharma, Vineet K

2017-01-01

The experimental methods for the prediction of molecular toxicity are tedious and time-consuming tasks. Thus, the computational approaches could be used to develop alternative methods for toxicity prediction. We have developed a tool for the prediction of molecular toxicity along with the aqueous solubility and permeability of any molecule/metabolite. Using a comprehensive and curated set of toxin molecules as a training set, the different chemical and structural based features such as descriptors and fingerprints were exploited for feature selection, optimization and development of machine learning based classification and regression models. The compositional differences in the distribution of atoms were apparent between toxins and non-toxins, and hence, the molecular features were used for the classification and regression. On 10-fold cross-validation, the descriptor-based, fingerprint-based and hybrid-based classification models showed similar accuracy (93%) and Matthews's correlation coefficient (0.84). The performances of all the three models were comparable (Matthews's correlation coefficient = 0.84-0.87) on the blind dataset. In addition, the regression-based models using descriptors as input features were also compared and evaluated on the blind dataset. Random forest based regression model for the prediction of solubility performed better ( R 2 = 0.84) than the multi-linear regression (MLR) and partial least square regression (PLSR) models, whereas, the partial least squares based regression model for the prediction of permeability (caco-2) performed better ( R 2 = 0.68) in comparison to the random forest and MLR based regression models. The performance of final classification and regression models was evaluated using the two validation datasets including the known toxins and commonly used constituents of health products, which attests to its accuracy. The ToxiM web server would be a highly useful and reliable tool for the prediction of toxicity, solubility, and permeability of small molecules.

ToxiM: A Toxicity Prediction Tool for Small Molecules Developed Using Machine Learning and Chemoinformatics Approaches

PubMed Central

Sharma, Ashok K.; Srivastava, Gopal N.; Roy, Ankita; Sharma, Vineet K.

2017-01-01

The experimental methods for the prediction of molecular toxicity are tedious and time-consuming tasks. Thus, the computational approaches could be used to develop alternative methods for toxicity prediction. We have developed a tool for the prediction of molecular toxicity along with the aqueous solubility and permeability of any molecule/metabolite. Using a comprehensive and curated set of toxin molecules as a training set, the different chemical and structural based features such as descriptors and fingerprints were exploited for feature selection, optimization and development of machine learning based classification and regression models. The compositional differences in the distribution of atoms were apparent between toxins and non-toxins, and hence, the molecular features were used for the classification and regression. On 10-fold cross-validation, the descriptor-based, fingerprint-based and hybrid-based classification models showed similar accuracy (93%) and Matthews's correlation coefficient (0.84). The performances of all the three models were comparable (Matthews's correlation coefficient = 0.84–0.87) on the blind dataset. In addition, the regression-based models using descriptors as input features were also compared and evaluated on the blind dataset. Random forest based regression model for the prediction of solubility performed better (R2 = 0.84) than the multi-linear regression (MLR) and partial least square regression (PLSR) models, whereas, the partial least squares based regression model for the prediction of permeability (caco-2) performed better (R2 = 0.68) in comparison to the random forest and MLR based regression models. The performance of final classification and regression models was evaluated using the two validation datasets including the known toxins and commonly used constituents of health products, which attests to its accuracy. The ToxiM web server would be a highly useful and reliable tool for the prediction of toxicity, solubility, and permeability of small molecules. PMID:29249969

Insights into geometries, stabilities, electronic structures, reactivity descriptors, and magnetic properties of bimetallic Nim Cun-m (m = 1, 2; n = 3-13) clusters: Comparison with pure copper clusters.

PubMed

Singh, Raman K; Iwasa, Takeshi; Taketsugu, Tetsuya

2018-05-25

A long-range corrected density functional theory (LC-DFT) was applied to study the geometric structures, relative stabilities, electronic structures, reactivity descriptors and magnetic properties of the bimetallic NiCu n -1 and Ni 2 Cu n -2 (n = 3-13) clusters, obtained by doping one or two Ni atoms to the lowest energy structures of Cu n , followed by geometry optimizations. The optimized geometries revealed that the lowest energy structures of the NiCu n -1 and Ni 2 Cu n -2 clusters favor the Ni atom(s) situated at the most highly coordinated position of the host copper clusters. The averaged binding energy, the fragmentation energies and the second-order energy differences signified that the Ni doped clusters can continue to gain an energy during the growth process. The electronic structures revealed that the highest occupied molecular orbital and the lowest unoccupied molecular orbital energies of the LC-DFT are reliable and can be used to predict the vertical ionization potential and the vertical electron affinity of the systems. The reactivity descriptors such as the chemical potential, chemical hardness and electrophilic power, and the reactivity principle such as the minimum polarizability principle are operative for characterizing and rationalizing the electronic structures of these clusters. Moreover, doping of Ni atoms into the copper clusters carry most of the total spin magnetic moment. © 2018 Wiley Periodicals, Inc. © 2018 Wiley Periodicals, Inc.

Molecular and agronomic analysis of intraspecific variability in Capsicum baccatum var. pendulum accessions.

PubMed

Leite, P S S; Rodrigues, R; Silva, R N O; Pimenta, S; Medeiros, A M; Bento, C S; Gonçalves, L S A

2016-10-05

Capsicum baccatum is one of the most important chili peppers in South America, since this region is considered to be the center of origin and diversity of this species. In Brazil, C. baccatum has been widely explored by family farmers and there are different local names for each fruit phenotype, such as cambuci and dedo-de-moça (lady's finger). Although very popular among farmers and consumers, C. baccatum has been less extensively studied than other Capsicum species. This study describes the phenotypic and genotypic variability in C. baccatum var. pendulum accessions. Twenty-nine accessions from the Universidade Estadual do Norte Fluminense Darcy Ribeiro gene bank, and one commercial genotype ('BRS-Mari') were evaluated for 53 morphoagronomic descriptors (31 qualitative and 22 quantitative traits). In addition, accessions were genotyped using 30 microsatellite primers. Three accessions from the C. annuum complex were included in the molecular characterization. Nine of 31 qualitative descriptors were monomorphic, while all quantitative descriptors were highly significant different between accessions (P < 0.01). Using the unweighted pair group method using arithmetic averages, four groups were obtained based on multicategoric variables and five groups were obtained based on quantitative variables. In the genotyping analysis, 12 polymorphic simple sequence repeat primers amplified in C. baccatum with dissimilarity between accessions ranging from 0.13 to 0.91, permitting the formation of two distinct groups for Bayesian analysis. These results indicate wide variability among the accessions comparing phenotypic and genotypic data and revealed distinct patterns of dissimilarity between matrices, indicating that both steps are valuable for the characterization of C. baccatum var. pendulum accessions.

Evaluation of estimation methods for organic carbon normalized sorption coefficients

USGS Publications Warehouse

Baker, James R.; Mihelcic, James R.; Luehrs, Dean C.; Hickey, James P.

1997-01-01

A critically evaluated set of 94 soil water partition coefficients normalized to soil organic carbon content (Koc) is presented for 11 classes of organic chemicals. This data set is used to develop and evaluate Koc estimation methods using three different descriptors. The three types of descriptors used in predicting Koc were octanol/water partition coefficient (Kow), molecular connectivity (mXt) and linear solvation energy relationships (LSERs). The best results were obtained estimating Koc from Kow, though a slight improvement in the correlation coefficient was obtained by using a two-parameter regression with Kow and the third order difference term from mXt. Molecular connectivity correlations seemed to be best suited for use with specific chemical classes. The LSER provided a better fit than mXt but not as good as the correlation with Koc. The correlation to predict Koc from Kow was developed for 72 chemicals; log Koc = 0.903* log Kow + 0.094. This correlation accounts for 91% of the variability in the data for chemicals with log Kow ranging from 1.7 to 7.0. The expression to determine the 95% confidence interval on the estimated Koc is provided along with an example for two chemicals of different hydrophobicity showing the confidence interval of the retardation factor determined from the estimated Koc. The data showed that Koc is not likely to be applicable for chemicals with log Kow < 1.7. Finally, the Koc correlation developed using Kow as a descriptor was compared with three nonclass-specific correlations and two 'commonly used' class-specific correlations to determine which method(s) are most suitable.

Experimental and theoretical distribution of electron density and thermopolimerization in crystals of Ph3Sb(O2CCH=CH2)2 complex

NASA Astrophysics Data System (ADS)

Fukin, Georgy K.; Samsonov, Maxim A.; Arapova, Alla V.; Mazur, Anton S.; Artamonova, Tatiana O.; Khodorkovskiy, Mikhail A.; Vasilyev, Aleksander V.

2017-10-01

In this paper we present the results of a high-resolution single crystal X-ray diffraction experiment of a triphenylantimony diacrylate (Ph3Sb(O2CCH=CH2)2 (1)) and a subsequent charge density study based on a topological analysis according to quantum theory of atoms in molecules (QTAIM) together with density functional theory (DFT) calculation of isolated molecule. The QTAIM was used to investigate nature of the chemical bonds and molecular graph of Ph3Sb(O2CCH=CH2)2 complex. The molecular graph shows that only in one acrylate group there is an evidence of bonding between antimony and carbonyl oxygen atom in terms of the presence of a bond path. Thus the molecular graph for this class of compounds does not provide a definitive picture of the chemical bonding and should be complemented with other descriptors, such as and a source function (SF), noncovalent interaction (NCI) index and delocalization index (DI). Moreover the realization of π…π interactions between double bonds of acrylate groups in adjacent molecules allowed us to carry out a thermopolimerization reaction in crystals of Ph3Sb(O2CCH=CH2)2 complex and to determine a probable structure of polymer by solid state CP/MAS 13C NMR.

Evaluation of molecular assembly, spectroscopic interpretation, intra-/inter molecular hydrogen bonding and chemical reactivity of two pyrrole precursors

NASA Astrophysics Data System (ADS)

Rawat, Poonam; Singh, R. N.

2014-10-01

This paper describes the evaluation of conformational, spectroscopic, hydrogen bonding and chemical reactivity of pyrrole precursor: ethyl 3,5 dimethyl-1H-pyrrole-2-carboxylate (EDPC) and ethyl 3,4-dimethyl-4-acetyl-1H-pyrrole-2-carboxylate (EDAPC) for the convenient characterization, synthetic usefulness and comparative evaluations. All experimental spectral values of 1H NMR, UV-Vis and FT-IR spectra coincide well with calculated values by DFT. The orbital interactions in EDPC and EDAPC are found to lengthen their Nsbnd H and Cdbnd O bonds and lowers their vibrational frequencies (red shift) resulting to dimer formation. The QTAIM and NBO analyses provide the strength of interactions and charge transfer in the hydrogen bonding unit and stability of dimers. The binding energy of EDPC and EDPAC dimer are found to be 9.92, 10.22 kcal/mol, respectively. In EDPAC and EDPC dimer, hyperconjugative interactions between monomer units is due to n1(O) → σ*(Nsbnd H) that stabilize the molecule up to 9.7 and 9.3 kcal/mol, respectively. On evaluation of molecular electrostatic potential (MEP) and electronic descriptors for EDPC it has been found that it is a good precursor for synthesis of formyl and acetyl derivatives whereas EDAPC has been found to be a good precursor for synthesis of schiff base, hydrazones, hydrazide-hydrazones and chalcones.

Exploration of cell cycle regulation and modulation of the DNA methylation mechanism of pelargonidin: Insights from the molecular modeling approach.

PubMed

Karthi, Natesan; Karthiga, Arumugasamy; Kalaiyarasu, Thangaraj; Stalin, Antony; Manju, Vaiyapuri; Singh, Sanjeev Kumar; Cyril, Ravi; Lee, Sang-Myeong

2017-10-01

Pelargonidin is an anthocyanidin isolated from plant resources. It shows strong cytotoxicity toward various cancer cell lines, even though the carcinogenesis-modulating pathway of pelargonidin is not yet known. One of our previous reports showed that pelargonidin arrests the cell cycle and induces apoptosis in HT29 cells. Flowcytometry and immunoblot analysis confirmed that pelargonidin specifically inhibits the activation of CDK1 and blocks the G2-M transition of the cell cycle. In addition, DNA fragmentation was observed along with induction of cytochrome c release-mediated apoptosis. Hence, the aim of the present study was to investigate the molecular mechanism of pelargonidin's action on cell cycle regulators CDK1, CDK4, and CDK6 as well as the substrate-binding domain of DNMT1 and DNMT3A, which regulate the epigenetic signals related to DNA methylation. The results of docking analysis, binding free energy calculation, and molecular dynamics simulation correlated with the experimental results, and pelargonidin showed a specific interaction with CDK1. In this context, pelargonidin may also inhibit the recognition of DNA and catalytic binding by DNMT1 and DNMT3A. The HOMO-LUMO analysis mapped the functional groups of pelargonidin. Prediction of pharmacological descriptors suggested that pelargonidin can serve as a multitarget inhibitor for cancer treatment. Copyright © 2017 Elsevier Ltd. All rights reserved.

Synthesis, crystal structure analysis, spectral (NMR, FT-IR, FT-Raman and UV-Vis) investigations, molecular docking studies, antimicrobial studies and quantum chemical calculations of a novel 4-chloro-8-methoxyquinoline-2(1H)-one: An effective antimicrobial agent and an inhibition of DNA gyrase and lanosterol-14α-demethylase enzymes

NASA Astrophysics Data System (ADS)

Murugavel, S.; Sundramoorthy, S.; Lakshmanan, D.; Subashini, R.; Pavan Kumar, P.

2017-03-01

The novel title compound 4-chloro-8-methoxyquinoline-2(1H)-one (4CMOQ) has been synthesized by slow evaporation solution growth technique at room temperature. The synthesized 4CMOQ molecule was characterized experimentally by FT-IR, FT-Raman, UV-Vis, NMR and single crystal diffraction (XRD) and theoretically by quantum chemical calculations. The molecular geometry was also optimized using density functional theory (DFT/B3LYP) method with the 6-311++G (d,p) basis set in ground state and compared with the experimental data. The entire vibrational assignments of wave numbers were made on the basis of potential energy distribution (PED) by VEDA 4 programme. The nuclear magnetic resonance spectra (1H and 13C NMR) are obtained by using the gauge-invariant atomic orbital (GIAO) method. The change in electron density (ED) in the antibonding orbital's and stabilization energies E(2) of the molecule have been evaluated by natural bond orbital (NBO) analysis to give clear evidence of stabilization. Moreover, electronic characteristics such as HOMO and LUMO energies, Mulliken atomic charges and molecular electrostatic potential surface are investigated. Absorption spectrum analysis, nonlinear optical properties, chemical reactivity descriptors and thermodynamic features are also outlined theoretically. Molecular docking studies were executed to understand the inhibitory activity of 4CMOQ against DNA gyrase and Lanosterol 14 α-demethylase. The antimicrobial activity of 4CMOQ was determined against bacterial strains such as Escherichia coli, Staphylococcus aureus and Pseudomonas aeruginosa and fungal strains such as Aspergillus niger, Monascus purpureus and Penicillium citrinum. The obtained results show that the compound exhibited good to moderate antimicrobial activity.

Cinfony – combining Open Source cheminformatics toolkits behind a common interface

PubMed Central

O'Boyle, Noel M; Hutchison, Geoffrey R

2008-01-01

Background Open Source cheminformatics toolkits such as OpenBabel, the CDK and the RDKit share the same core functionality but support different sets of file formats and forcefields, and calculate different fingerprints and descriptors. Despite their complementary features, using these toolkits in the same program is difficult as they are implemented in different languages (C++ versus Java), have different underlying chemical models and have different application programming interfaces (APIs). Results We describe Cinfony, a Python module that presents a common interface to all three of these toolkits, allowing the user to easily combine methods and results from any of the toolkits. In general, the run time of the Cinfony modules is almost as fast as accessing the underlying toolkits directly from C++ or Java, but Cinfony makes it much easier to carry out common tasks in cheminformatics such as reading file formats and calculating descriptors. Conclusion By providing a simplified interface and improving interoperability, Cinfony makes it easy to combine complementary features of OpenBabel, the CDK and the RDKit. PMID:19055766

A Method for Automatic Surface Inspection Using a Model-Based 3D Descriptor.

PubMed

Madrigal, Carlos A; Branch, John W; Restrepo, Alejandro; Mery, Domingo

2017-10-02

Automatic visual inspection allows for the identification of surface defects in manufactured parts. Nevertheless, when defects are on a sub-millimeter scale, detection and recognition are a challenge. This is particularly true when the defect generates topological deformations that are not shown with strong contrast in the 2D image. In this paper, we present a method for recognizing surface defects in 3D point clouds. Firstly, we propose a novel 3D local descriptor called the Model Point Feature Histogram (MPFH) for defect detection. Our descriptor is inspired from earlier descriptors such as the Point Feature Histogram (PFH). To construct the MPFH descriptor, the models that best fit the local surface and their normal vectors are estimated. For each surface model, its contribution weight to the formation of the surface region is calculated and from the relative difference between models of the same region a histogram is generated representing the underlying surface changes. Secondly, through a classification stage, the points on the surface are labeled according to five types of primitives and the defect is detected. Thirdly, the connected components of primitives are projected to a plane, forming a 2D image. Finally, 2D geometrical features are extracted and by a support vector machine, the defects are recognized. The database used is composed of 3D simulated surfaces and 3D reconstructions of defects in welding, artificial teeth, indentations in materials, ceramics and 3D models of defects. The quantitative and qualitative results showed that the proposed method of description is robust to noise and the scale factor, and it is sufficiently discriminative for detecting some surface defects. The performance evaluation of the proposed method was performed for a classification task of the 3D point cloud in primitives, reporting an accuracy of 95%, which is higher than for other state-of-art descriptors. The rate of recognition of defects was close to 94%.

A Method for Automatic Surface Inspection Using a Model-Based 3D Descriptor

PubMed Central

Branch, John W.

2017-01-01

Automatic visual inspection allows for the identification of surface defects in manufactured parts. Nevertheless, when defects are on a sub-millimeter scale, detection and recognition are a challenge. This is particularly true when the defect generates topological deformations that are not shown with strong contrast in the 2D image. In this paper, we present a method for recognizing surface defects in 3D point clouds. Firstly, we propose a novel 3D local descriptor called the Model Point Feature Histogram (MPFH) for defect detection. Our descriptor is inspired from earlier descriptors such as the Point Feature Histogram (PFH). To construct the MPFH descriptor, the models that best fit the local surface and their normal vectors are estimated. For each surface model, its contribution weight to the formation of the surface region is calculated and from the relative difference between models of the same region a histogram is generated representing the underlying surface changes. Secondly, through a classification stage, the points on the surface are labeled according to five types of primitives and the defect is detected. Thirdly, the connected components of primitives are projected to a plane, forming a 2D image. Finally, 2D geometrical features are extracted and by a support vector machine, the defects are recognized. The database used is composed of 3D simulated surfaces and 3D reconstructions of defects in welding, artificial teeth, indentations in materials, ceramics and 3D models of defects. The quantitative and qualitative results showed that the proposed method of description is robust to noise and the scale factor, and it is sufficiently discriminative for detecting some surface defects. The performance evaluation of the proposed method was performed for a classification task of the 3D point cloud in primitives, reporting an accuracy of 95%, which is higher than for other state-of-art descriptors. The rate of recognition of defects was close to 94%. PMID:28974037

Scaling Relations for Acidity and Reactivity of Zeolites

PubMed Central

2017-01-01

Zeolites are widely applied as solid acid catalysts in various technological processes. In this work we have computationally investigated how catalytic reactivity scales with acidity for a range of zeolites with different topologies and chemical compositions. We found that straightforward correlations are limited to zeolites with the same topology. The adsorption energies of bases such as carbon monoxide (CO), acetonitrile (CH3CN), ammonia (NH3), trimethylamine (N(CH3)3), and pyridine (C5H5N) give the same trend of acid strength for FAU zeolites with varying composition. Crystal orbital Hamilton populations (COHP) analysis provides a detailed molecular orbital picture of adsorbed base molecules on the Brønsted acid sites (BAS). Bonding is dominated by strong σ donation from guest molecules to the BAS for the adsorbed CO and CH3CN complexes. An electronic descriptor of acid strength is constructed based on the bond order calculations, which is an intrinsic parameter rather than adsorption energy that contains additional contributions due to secondary effects such as van der Waals interactions with the zeolite walls. The bond order parameter derived for the CH3CN adsorption complex represents a useful descriptor for the intrinsic acid strength of FAU zeolites. For FAU zeolites the activation energy for the conversion of π-adsorbed isobutene into alkoxy species correlates well with the acid strength determined by the NH3 adsorption energies. Other zeolites such as MFI and CHA do not follow the scaling relations obtained for FAU; we ascribe this to the different van der Waals interactions and steric effects induced by zeolite framework topology. PMID:29142616

A Bayesian network model for predicting aquatic toxicity mode of action using two dimensional theoretical molecular descriptors

EPA Science Inventory

The mode of toxic action (MoA) has been recognized as a key determinant of chemical toxicity, but development of predictive MoA classification models in aquatic toxicology has been limited. We developed a Bayesian network model to classify aquatic toxicity MoA using a recently pu...

A Bayesian network model for predicting aquatic toxicity mode of action using two dimensional theoretical molecular descriptors-abstract

EPA Science Inventory

The mode of toxic action (MoA) has been recognized as a key determinant of chemical toxicity but MoA classification in aquatic toxicology has been limited. We developed a Bayesian network model to classify aquatic toxicity mode of action using a recently published dataset contain...

Structural parameterization and functional prediction of antigenic polypeptome sequences with biological activity through quantitative sequence-activity models (QSAM) by molecular electronegativity edge-distance vector (VMED).

PubMed

Li, ZhiLiang; Wu, ShiRong; Chen, ZeCong; Ye, Nancy; Yang, ShengXi; Liao, ChunYang; Zhang, MengJun; Yang, Li; Mei, Hu; Yang, Yan; Zhao, Na; Zhou, Yuan; Zhou, Ping; Xiong, Qing; Xu, Hong; Liu, ShuShen; Ling, ZiHua; Chen, Gang; Li, GenRong

2007-10-01

Only from the primary structures of peptides, a new set of descriptors called the molecular electronegativity edge-distance vector (VMED) was proposed and applied to describing and characterizing the molecular structures of oligopeptides and polypeptides, based on the electronegativity of each atom or electronic charge index (ECI) of atomic clusters and the bonding distance between atom-pairs. Here, the molecular structures of antigenic polypeptides were well expressed in order to propose the automated technique for the computerized identification of helper T lymphocyte (Th) epitopes. Furthermore, a modified MED vector was proposed from the primary structures of polypeptides, based on the ECI and the relative bonding distance of the fundamental skeleton groups. The side-chains of each amino acid were here treated as a pseudo-atom. The developed VMED was easy to calculate and able to work. Some quantitative model was established for 28 immunogenic or antigenic polypeptides (AGPP) with 14 (1-14) A(d) and 14 other restricted activities assigned as "1"(+) and "0"(-), respectively. The latter comprised 6 A(b)(15-20), 3 A(k)(21-23), 2 E(k)(24-26), 2 H-2(k)(27 and 28) restricted sequences. Good results were obtained with 90% correct classification (only 2 wrong ones for 20 training samples) and 100% correct prediction (none wrong for 8 testing samples); while contrastively 100% correct classification (none wrong for 20 training samples) and 88% correct classification (1 wrong for 8 testing samples). Both stochastic samplings and cross validations were performed to demonstrate good performance. The described method may also be suitable for estimation and prediction of classes I and II for major histocompatibility antigen (MHC) epitope of human. It will be useful in immune identification and recognition of proteins and genes and in the design and development of subunit vaccines. Several quantitative structure activity relationship (QSAR) models were developed for various oligopeptides and polypeptides including 58 dipeptides and 31 pentapeptides with angiotensin converting enzyme (ACE) inhibition by multiple linear regression (MLR) method. In order to explain the ability to characterize molecular structure of polypeptides, a molecular modeling investigation on QSAR was performed for functional prediction of polypeptide sequences with antigenic activity and heptapeptide sequences with tachykinin activity through quantitative sequence-activity models (QSAMs) by the molecular electronegativity edge-distance vector (VMED). The results showed that VMED exhibited both excellent structural selectivity and good activity prediction. Moreover, the results showed that VMED behaved quite well for both QSAR and QSAM of poly-and oligopeptides, which exhibited both good estimation ability and prediction power, equal to or better than those reported in the previous references. Finally, a preliminary conclusion was drawn: both classical and modified MED vectors were very useful structural descriptors. Some suggestions were proposed for further studies on QSAR/QSAM of proteins in various fields.

Molecular structure, spectral investigation (1H NMR, 13C NMR, UV-Visible, FT-IR, FT-Raman), NBO, intramolecular hydrogen bonding, chemical reactivity and first hyperpolarizability analysis of formononetin [7-hydroxy-3(4-methoxyphenyl)chromone]: A quantum chemical study

NASA Astrophysics Data System (ADS)

Srivastava, Anubha; Mishra, Rashmi; Kumar, Sudhir; Dev, Kapil; Tandon, Poonam; Maurya, Rakesh

2015-03-01

Formononetin [7-hydroxy-3(4-methoxyphenyl)chromone or 4‧-methoxy daidzein] is a soy isoflavonoid that is found abundantly in traditional Chinese medicine Astragalus mongholicus (Bunge) and Trifolium pretense L. (red clover), and in an Indian medicinal plant, Butea (B.) monosperma. Crude extract of B.monosperma is used for rapid healing of fracture in Indian traditional medicine. In this study, a combined theoretical and experimental approach is used to study the properties of formononetin. The optimized geometry was calculated by B3LYP method using 6-311++G(d,p) as a large basis set. The FT-Raman and FT-IR spectra were recorded in the solid phase, and interpreted in terms of potential energy distribution (PED) analysis. Density functional theory (DFT) is applied to explore the nonlinear optical properties of the molecule. Good consistency is found between the calculated results and observed data for the electronic absorption, IR and Raman spectra. The solvent effects have been calculated using time-dependent density functional theory in combination with the integral equation formalism polarized continuum model, and the results are in good agreement with observed measurements. The double well potential energy curve of the molecule about the respective bonds, have been plotted, as obtained from DFT/6-31G basis set. The computational results diagnose the most stable conformer of formononetin. The HOMO-LUMO energy gap of possible conformers has been calculated for comparing their chemical activity. Chemical reactivity has been measured by reactivity descriptors and molecular electrostatic potential surface (MEP). The 1H and 13C NMR chemical shifts of the molecule were calculated by the Gauge including atomic orbital (GIAO) method. Furthermore, the role of CHsbnd O intramolecular hydrogen bond in the stability of molecule is investigated on the basis of the results of topological properties of AIM theory and NBO analysis. The calculated first hyperpolarizability shows that the molecule is an attractive molecule for future applications in non-linear optics.

Fragment-based prediction of skin sensitization using recursive partitioning

NASA Astrophysics Data System (ADS)

Lu, Jing; Zheng, Mingyue; Wang, Yong; Shen, Qiancheng; Luo, Xiaomin; Jiang, Hualiang; Chen, Kaixian

2011-09-01

Skin sensitization is an important toxic endpoint in the risk assessment of chemicals. In this paper, structure-activity relationships analysis was performed on the skin sensitization potential of 357 compounds with local lymph node assay data. Structural fragments were extracted by GASTON (GrAph/Sequence/Tree extractiON) from the training set. Eight fragments with accuracy significantly higher than 0.73 ( p < 0.1) were retained to make up an indicator descriptor fragment. The fragment descriptor and eight other physicochemical descriptors closely related to the endpoint were calculated to construct the recursive partitioning tree (RP tree) for classification. The balanced accuracy of the training set, test set I, and test set II in the leave-one-out model were 0.846, 0.800, and 0.809, respectively. The results highlight that fragment-based RP tree is a preferable method for identifying skin sensitizers. Moreover, the selected fragments provide useful structural information for exploring sensitization mechanisms, and RP tree creates a graphic tree to identify the most important properties associated with skin sensitization. They can provide some guidance for designing of drugs with lower sensitization level.

The implementation of contour-based object orientation estimation algorithm in FPGA-based on-board vision system

NASA Astrophysics Data System (ADS)

Alpatov, Boris; Babayan, Pavel; Ershov, Maksim; Strotov, Valery

2016-10-01

This paper describes the implementation of the orientation estimation algorithm in FPGA-based vision system. An approach to estimate an orientation of objects lacking axial symmetry is proposed. Suggested algorithm is intended to estimate orientation of a specific known 3D object based on object 3D model. The proposed orientation estimation algorithm consists of two stages: learning and estimation. Learning stage is devoted to the exploring of studied object. Using 3D model we can gather set of training images by capturing 3D model from viewpoints evenly distributed on a sphere. Sphere points distribution is made by the geosphere principle. Gathered training image set is used for calculating descriptors, which will be used in the estimation stage of the algorithm. The estimation stage is focusing on matching process between an observed image descriptor and the training image descriptors. The experimental research was performed using a set of images of Airbus A380. The proposed orientation estimation algorithm showed good accuracy in all case studies. The real-time performance of the algorithm in FPGA-based vision system was demonstrated.

Contour-based object orientation estimation

NASA Astrophysics Data System (ADS)

Alpatov, Boris; Babayan, Pavel

2016-04-01

Real-time object orientation estimation is an actual problem of computer vision nowadays. In this paper we propose an approach to estimate an orientation of objects lacking axial symmetry. Proposed algorithm is intended to estimate orientation of a specific known 3D object, so 3D model is required for learning. The proposed orientation estimation algorithm consists of 2 stages: learning and estimation. Learning stage is devoted to the exploring of studied object. Using 3D model we can gather set of training images by capturing 3D model from viewpoints evenly distributed on a sphere. Sphere points distribution is made by the geosphere principle. It minimizes the training image set. Gathered training image set is used for calculating descriptors, which will be used in the estimation stage of the algorithm. The estimation stage is focusing on matching process between an observed image descriptor and the training image descriptors. The experimental research was performed using a set of images of Airbus A380. The proposed orientation estimation algorithm showed good accuracy (mean error value less than 6°) in all case studies. The real-time performance of the algorithm was also demonstrated.

Graphic matching based on shape contexts and reweighted random walks

NASA Astrophysics Data System (ADS)

Zhang, Mingxuan; Niu, Dongmei; Zhao, Xiuyang; Liu, Mingjun

2018-04-01

Graphic matching is a very critical issue in all aspects of computer vision. In this paper, a new graphics matching algorithm combining shape contexts and reweighted random walks was proposed. On the basis of the local descriptor, shape contexts, the reweighted random walks algorithm was modified to possess stronger robustness and correctness in the final result. Our main process is to use the descriptor of the shape contexts for the random walk on the iteration, of which purpose is to control the random walk probability matrix. We calculate bias matrix by using descriptors and then in the iteration we use it to enhance random walks' and random jumps' accuracy, finally we get the one-to-one registration result by discretization of the matrix. The algorithm not only preserves the noise robustness of reweighted random walks but also possesses the rotation, translation, scale invariance of shape contexts. Through extensive experiments, based on real images and random synthetic point sets, and comparisons with other algorithms, it is confirmed that this new method can produce excellent results in graphic matching.

Rotation and scale invariant shape context registration for remote sensing images with background variations

NASA Astrophysics Data System (ADS)

Jiang, Jie; Zhang, Shumei; Cao, Shixiang

2015-01-01

Multitemporal remote sensing images generally suffer from background variations, which significantly disrupt traditional region feature and descriptor abstracts, especially between pre and postdisasters, making registration by local features unreliable. Because shapes hold relatively stable information, a rotation and scale invariant shape context based on multiscale edge features is proposed. A multiscale morphological operator is adapted to detect edges of shapes, and an equivalent difference of Gaussian scale space is built to detect local scale invariant feature points along the detected edges. Then, a rotation invariant shape context with improved distance discrimination serves as a feature descriptor. For a distance shape context, a self-adaptive threshold (SAT) distance division coordinate system is proposed, which improves the discriminative property of the feature descriptor in mid-long pixel distances from the central point while maintaining it in shorter ones. To achieve rotation invariance, the magnitude of Fourier transform in one-dimension is applied to calculate angle shape context. Finally, the residual error is evaluated after obtaining thin-plate spline transformation between reference and sensed images. Experimental results demonstrate the robustness, efficiency, and accuracy of this automatic algorithm.

The importance of molecular structures, endpoints' values, and predictivity parameters in QSAR research: QSAR analysis of a series of estrogen receptor binders.

PubMed

Li, Jiazhong; Gramatica, Paola

2010-11-01

Quantitative structure-activity relationship (QSAR) methodology aims to explore the relationship between molecular structures and experimental endpoints, producing a model for the prediction of new data; the predictive performance of the model must be checked by external validation. Clearly, the qualities of chemical structure information and experimental endpoints, as well as the statistical parameters used to verify the external predictivity have a strong influence on QSAR model reliability. Here, we emphasize the importance of these three aspects by analyzing our models on estrogen receptor binders (Endocrine disruptor knowledge base (EDKB) database). Endocrine disrupting chemicals, which mimic or antagonize the endogenous hormones such as estrogens, are a hot topic in environmental and toxicological sciences. QSAR shows great values in predicting the estrogenic activity and exploring the interactions between the estrogen receptor and ligands. We have verified our previously published model for additional external validation on new EDKB chemicals. Having found some errors in the used 3D molecular conformations, we redevelop a new model using the same data set with corrected structures, the same method (ordinary least-square regression, OLS) and DRAGON descriptors. The new model, based on some different descriptors, is more predictive on external prediction sets. Three different formulas to calculate correlation coefficient for the external prediction set (Q2 EXT) were compared, and the results indicated that the new proposal of Consonni et al. had more reasonable results, consistent with the conclusions from regression line, Williams plot and root mean square error (RMSE) values. Finally, the importance of reliable endpoints values has been highlighted by comparing the classification assignments of EDKB with those of another estrogen receptor binders database (METI): we found that 16.1% assignments of the common compounds were opposite (20 among 124 common compounds). In order to verify the real assignments for these inconsistent compounds, we predicted these samples, as a blind external set, by our regression models and compared the results with the two databases. The results indicated that most of the predictions were consistent with METI. Furthermore, we built a kNN classification model using the 104 consistent compounds to predict those inconsistent ones, and most of the predictions were also in agreement with METI database.

Theoretical Probing of Weak Anion-Cation Interactions in Certain Pyridinium-Based Ionic Liquid Ion Pairs and the Application of Molecular Electrostatic Potential in Their Ionic Crystal Density Determination: A Comparative Study Using Density Functional Approach.

PubMed

Joseph, Aswathy; Thomas, Vibin Ipe; Żyła, Gaweł; Padmanabhan, A S; Mathew, Suresh

2018-01-11

A comprehensive study on the structure, nature of interaction, and properties of six ionic pairs of 1-butylpyridinium and 1-butyl-4-methylpyridinium cations in combination with tetrafluoroborate (BF 4 - ), chloride (Cl - ), and bromide (Br - ) anions have been carried out using density functional theory (DFT). The anion-cation interaction energy (ΔE int ), thermochemistry values, theoretical band gap, molecular orbital energy order, DFT-based chemical activity descriptors [chemical potential (μ), chemical hardness (η), and electrophilicity index (ω)], and distribution of density of states (DOS) of these ion pairs were investigated. The ascendancy of the -CH 3 substituent at the fourth position of the 1-butylpyridinium cation ring on the values of ΔE int , theoretical band gap and chemical activity descriptors was evaluated. The ΔE int values were negative for all six ion pairs and were highest for Cl - containing ion pairs. The theoretical band gap value after -CH 3 substitution increased from 3.78 to 3.96 eV (for Cl - ) and from 2.74 to 2.88 eV (for Br - ) and decreased from 4.9 to 4.89 eV (for BF 4 - ). Ion pairs of BF 4 - were more susceptible to charge transfer processes as inferred from their significantly high η values and comparatively small difference in ω value after -CH 3 substitution. The change in η and μ values due to the -CH 3 substituent is negligibly small in all cases except for the ion pairs of Cl - . Critical-point (CP) analyses were carried out to investigate the AIM topological parameters at the interionic bond critical points (BCPs). The RDG isosurface analysis indicated that the anion-cation interaction was dominated by strong H cat ···X ani and C cat ···X ani interactions in ion pairs of Cl - and Br - whereas a weak van der Waal's effect dominated in ion pairs of BF 4 - . The molecular electrostatic potential (MESP)-based parameter ΔΔV min measuring the anion-cation interaction strength showed a good linear correlation with ΔE int for all 1-butylpyridinium ion pairs (R 2 = 0.9918). The ionic crystal density values calculated by using DFT-based MESP showed only slight variations from experimentally reported values.

Structure, spectroscopic analyses (FT-IR and NMR), vibrational study, chemical reactivity and molecular docking study on 3,3'-((4-(trifluoromethyl)phenyl)methylene)bis(2-hydroxynaphthalene-1,4-dione), a promising anticancerous bis-lawsone derivative

NASA Astrophysics Data System (ADS)

Yadav, Krishna Kant; Kumar, Abhishek; Kumar, Amarendra; Misra, Neeraj; Brahmachari, Goutam

2018-02-01

Lawsone (2-hydroxy-1,4-naphthoquinone)has been evaluated to possess a wide range of biological and pharmacological activities. The interesting structural pattern of lawsone coupled with its so-called multifaceted pharmacological potential have made this scaffolds useful in certain chemical processes, particularly in synthesizing ligands for metal complexations, and also few of its derivatives have shown a number of biological activities. The equilibrium geometry of 3,3‧-((4-(trifluoromethyl)phenyl)methylene)bis(2-hydroxynaphthalene-1,4-dione) (1; TPMHD), a promising anticancerous lawsone derivative, has been determined and analyzed at DFT method employingB3LYP/6-311++G(d,p) level of theory. The reactivity descriptors such as Fukui functions and HOMO-LUMO gap are calculated and discussed. The infrared spectra of TPMHD(1) are calculated and compared with the experimentally observed ones. Moreover, 1H and 13C NMR spectra have been calculated by using the gauge independent atomic orbital method. The docking studies reveal that the TPMHD has strong binding affinity toward target protein 2SHP. Thus the compound has a possible use as a drug in cancer therapy. The study suggests further investigation on TPMHD for their in-depth biological and pharmaceutical importance.

Prediction of anticancer property of bowsellic acid derivatives by quantitative structure activity relationship analysis and molecular docking study.

PubMed

Satpathy, Raghunath; Guru, R K; Behera, R; Nayak, B

2015-01-01

Boswellic acid consists of a series of pentacyclic triterpene molecules that are produced by the plant Boswellia serrata. The potential applications of Bowsellic acid for treatment of cancer have been focused here. To predict the property of the bowsellic acid derivatives as anticancer compounds by various computational approaches. In this work, all total 65 derivatives of bowsellic acids from the PubChem database were considered for the study. After energy minimization of the ligands various types of molecular descriptors were computed and corresponding two-dimensional quantitative structure activity relationship (QSAR) models were obtained by taking Andrews coefficient as the dependent variable. Different types of comparative analysis were used for QSAR study are multiple linear regression, partial least squares, support vector machines and artificial neural network. From the study geometrical descriptors shows the highest correlation coefficient, which indicates the binding factor of the compound. To evaluate the anticancer property molecular docking study of six selected ligands based on Andrews affinity were performed with nuclear factor-kappa protein kinase (Protein Data Bank ID 4G3D), which is an established therapeutic target for cancers. Along with QSAR study and docking result, it was predicted that bowsellic acid can also be treated as a potential anticancer compound. Along with QSAR study and docking result, it was predicted that bowsellic acid can also be treated as a potential anticancer compound.

Quantum Mechanical Study of γ-Fe2O3 Nanoparticle as a Nanocarrier for Anticancer Drug Delivery

NASA Astrophysics Data System (ADS)

Lari, Hadi; Morsali, Ali; Heravi, Mohammad Momen

2018-05-01

Using density functional theory (DFT), noncovalent interactions and four mechanisms of covalent functionalization of melphalan anticancer drug onto γ-Fe2O3 nanoparticles have been studied. Quantum molecular descriptors of noncovalent configurations were investigated. It was specified that binding of melphalan onto γ-Fe2O3 nanoparticles is thermodynamically suitable. Hardness and the gap of energy between LUMO and HOMO of melphalan are higher than the noncovalent configurations, showing the reactivity of drug increases in the presence of γ-Fe2O3 nanoparticles. Melphalan can bond to γ-Fe2O3 nanoparticles through NH2 (k1 mechanism), OH (k2 mechanism), C=O (k3 mechanism) and Cl (k4 mechanism) groups. The activation energies, the activation enthalpies and the activation Gibbs free energies of these reactions were calculated. Thermodynamic data indicate that k3 mechanism is exothermic and spontaneous and can take place at room temperature. These results could be generalized to other similar drugs.

Tackling sampling challenges in biomolecular simulations.

PubMed

Barducci, Alessandro; Pfaendtner, Jim; Bonomi, Massimiliano

2015-01-01

Molecular dynamics (MD) simulations are a powerful tool to give an atomistic insight into the structure and dynamics of proteins. However, the time scales accessible in standard simulations, which often do not match those in which interesting biological processes occur, limit their predictive capabilities. Many advanced sampling techniques have been proposed over the years to overcome this limitation. This chapter focuses on metadynamics, a method based on the introduction of a time-dependent bias potential to accelerate sampling and recover equilibrium properties of a few descriptors that are able to capture the complexity of a process at a coarse-grained level. The theory of metadynamics and its combination with other popular sampling techniques such as the replica exchange method is briefly presented. Practical applications of these techniques to the study of the Trp-Cage miniprotein folding are also illustrated. The examples contain a guide for performing these calculations with PLUMED, a plugin to perform enhanced sampling simulations in combination with many popular MD codes.

Covalent functionalization of octagraphene with magnetic octahedral B6- and non-planar C6- clusters

NASA Astrophysics Data System (ADS)

Chigo-Anota, E.; Cárdenas-Jirón, G.; Salazar Villanueva, M.; Bautista Hernández, A.; Castro, M.

2017-10-01

The interaction between the magnetic boron octahedral (B6-) and non-planar (C6-) carbon clusters with semimetal nano-sheet of octa-graphene (C64H24) in the gas phase is studied by means of DFT calculations. These results reveal that non-planar-1 (anion) carbon cluster exhibits structural stability, low chemical reactivity, magnetic (1.0 magneton bohr) and semiconductor behavior. On the other hand, there is chemisorption phenomena when the stable B6- and C6- clusters are absorbed on octa-graphene nanosheets. Such absorption generates high polarity and the low-reactivity remains as on the individual pristine cases. Electronic charge transference occurs from the clusters toward the nanosheets, producing a reduction of the work function for the complexes and also induces a magnetic behavior on the functionalized sheets. The quantum descriptors obtained for these systems reveal that they are feasible candidates for the design of molecular circuits, magnetic devices, and nano-vehicles for drug delivery.

Molecular basis of LFER. Modeling of the electronic substituent effect using fragment quantum self-similarity measures.

PubMed

Gironés, Xavier; Carbó-Dorca, Ramon; Ponec, Robert

2003-01-01

A new approach allowing the theoretical modeling of the electronic substituent effect is proposed. The approach is based on the use of fragment Quantum Self-Similarity Measures (MQS-SM) calculated from domain averaged Fermi Holes as new theoretical descriptors allowing for the replacement of Hammett sigma constants in QSAR models. To demonstrate the applicability of this new approach its formalism was applied to the description of the substituent effect on the dissociation of a broad series of meta and para substituted benzoic acids. The accuracy and the predicting power of this new approach was tested on the comparison with a recent exhaustive study by Sullivan et al. It has been shown that the accuracy and the predicting power of both procedures is comparable, but, in contrast to a five-parameter correlation equation necessary to describe the data in the study, our approach is more simple and, in fact, only a simple one-parameter correlation equation is required.

Facile synthesis of corticosteroids prodrugs from isolated hydrocortisone acetate and their quantum chemical calculations

NASA Astrophysics Data System (ADS)

Sethi, Arun; Singh, Ranvijay Pratap; Prakash, Rohit; Amandeep

2017-02-01

In the present research paper corticosteroids prodrugs of hydrocortisone acetate (1) have been synthesized, which was isolated from the flowers of Allamanda Violacea. The hydrocortisone acetate (1) was hydrolyzed to hydrocortisone (2) which was subsequently converted to prednisolone (3). Both the hydrocortisone (1) and prednisolone (2) underwent Steglich esterification with naproxen and Ibuprofen yielding compounds 11, 17 dihydroxy-21-(2-(6-methoxynaphthalene-2yl) propionoxy)-pregn-4-ene-3, 20-dione (4), 11, 17-dihydroxy-21-(2-(4-isobutylphenyl) propionoxy)-pregn-4-ene-3, 20-dione (5), 21-(2-(6-methoxynaphthalene-2-yl) propionoxy) 11,17-di-hydroxy-3,20-diketo-pregn-1,4-diene (6) and 11,17-di-hydroxy-3,20-diketo-pregn-1,4-diene-21-yl-2-(4-isobutylphenyl) propanoate (7). The synthesized compounds have been characterized with the help of spectroscopic techniques like 1H, 13C NMR, FT-IR spectroscopy and mass spectrometry. Density functional theory (DFT) with B3LYP functional and 6-31G (d, p) basis set has been used for the Quantum chemical calculations. The electronic properties such as frontier orbitals and band gap energies were calculated by TD-DFT approach. Intramolecular interactions have been identified by AIM (Atoms in Molecule) approach and vibrational wavenumbers have been calculated using DFT method. The reactivity and reactive site within the synthesized prodrugs have been examined with the help of reactivity descriptors. Dipole moment, polarizability and first static hyperpolarizability have been calculated to get a better insight of the properties of synthesized prodrugs. The molecular electrostatic potential (MEP) surface analysis has also been carried out.

Free energy force field (FEFF) 3D-QSAR analysis of a set of Plasmodium falciparum dihydrofolate reductase inhibitors

NASA Astrophysics Data System (ADS)

Santos-Filho, Osvaldo A.; Mishra, Rama K.; Hopfinger, A. J.

2001-09-01

Free energy force field (FEFF) 3D-QSAR analysis was used to construct ligand-receptor binding models for a set of 18 structurally diverse antifolates including pyrimethamine, cycloguanil, methotrexate, aminopterin and trimethoprim, and 13 pyrrolo[2,3-d]pyrimidines. The molecular target (`receptor') used was a 3D-homology model of a specific mutant type of Plasmodium falciparum (Pf) dihydrofolate reductase (DHFR). The dependent variable of the 3D-QSAR models is the IC50 inhibition constant for the specific mutant type of PfDHFR. The independent variables of the 3D-QSAR models (the descriptors) are scaled energy terms of a modified first-generation AMBER force field combined with a hydration shell aqueous solvation model and a collection of 2D-QSAR descriptors often used in QSAR studies. Multiple temperature molecular dynamics simulation (MDS) and the genetic function approximation (GFA) were employed using partial least square (PLS) and multidimensional linear regressions as the fitting functions to develop FEFF 3D-QSAR models for the binding process. The significant FEFF energy terms in the best 3D-QSAR models include energy contributions of the direct ligand-receptor interaction. Some changes in conformational energy terms of the ligand due to binding to the enzyme are also found to be important descriptors. The FEFF 3D-QSAR models indicate some structural features perhaps relevant to the mechanism of resistance of the PfDHFR to current antimalarials. The FEFF 3D-QSAR models are also compared to receptor-independent (RI) 4D-QSAR models developed in an earlier study and subsequently refined using recently developed generalized alignment rules.

A 3D QSAR CoMFA study of non-peptide angiotensin II receptor antagonists

NASA Astrophysics Data System (ADS)

Belvisi, Laura; Bravi, Gianpaolo; Catalano, Giovanna; Mabilia, Massimo; Salimbeni, Aldo; Scolastico, Carlo

1996-12-01

A series of non-peptide angiotensin II receptor antagonists was investigated with the aim of developing a 3D QSAR model using comparative molecular field analysis descriptors and approaches. The main goals of the study were dictated by an interest in methodologies and an understanding of the binding requirements to the AT1 receptor. Consistency with the previously derived activity models was always checked to contemporarily test the validity of the various hypotheses. The specific conformations chosen for the study, the procedures invoked to superimpose all structures, the conditions employed to generate steric and electrostatic field values and the various PCA/PLS runs are discussed in detail. The effect of experimental design techniques to select objects (molecules) and variables (descriptors) with respect to the predictive power of the QSAR models derived was especially analysed.

The Chemistry Development Kit (CDK) v2.0: atom typing, depiction, molecular formulas, and substructure searching.

PubMed

Willighagen, Egon L; Mayfield, John W; Alvarsson, Jonathan; Berg, Arvid; Carlsson, Lars; Jeliazkova, Nina; Kuhn, Stefan; Pluskal, Tomáš; Rojas-Chertó, Miquel; Spjuth, Ola; Torrance, Gilleain; Evelo, Chris T; Guha, Rajarshi; Steinbeck, Christoph

2017-06-06

The Chemistry Development Kit (CDK) is a widely used open source cheminformatics toolkit, providing data structures to represent chemical concepts along with methods to manipulate such structures and perform computations on them. The library implements a wide variety of cheminformatics algorithms ranging from chemical structure canonicalization to molecular descriptor calculations and pharmacophore perception. It is used in drug discovery, metabolomics, and toxicology. Over the last 10 years, the code base has grown significantly, however, resulting in many complex interdependencies among components and poor performance of many algorithms. We report improvements to the CDK v2.0 since the v1.2 release series, specifically addressing the increased functional complexity and poor performance. We first summarize the addition of new functionality, such atom typing and molecular formula handling, and improvement to existing functionality that has led to significantly better performance for substructure searching, molecular fingerprints, and rendering of molecules. Second, we outline how the CDK has evolved with respect to quality control and the approaches we have adopted to ensure stability, including a code review mechanism. This paper highlights our continued efforts to provide a community driven, open source cheminformatics library, and shows that such collaborative projects can thrive over extended periods of time, resulting in a high-quality and performant library. By taking advantage of community support and contributions, we show that an open source cheminformatics project can act as a peer reviewed publishing platform for scientific computing software. Graphical abstract CDK 2.0 provides new features and improved performance.

Merging Applicability Domains for in Silico Assessment of Chemical Mutagenicity

DTIC Science & Technology

2014-02-04

molecular fingerprints as descriptors for developing quantitative structure−activity relationship ( QSAR ) models and defining applicability domains with...used to define and quantify an applicability domain for either method. The importance of using applicability domains in QSAR modeling cannot be...domain from roughly 80% to 90%. These results indicated that the proposed QSAR protocol constituted a highly robust chemical mutagenicity prediction

Predictive and mechanistic multivariate linear regression models for reaction development

PubMed Central

Santiago, Celine B.; Guo, Jing-Yao

2018-01-01

Multivariate Linear Regression (MLR) models utilizing computationally-derived and empirically-derived physical organic molecular descriptors are described in this review. Several reports demonstrating the effectiveness of this methodological approach towards reaction optimization and mechanistic interrogation are discussed. A detailed protocol to access quantitative and predictive MLR models is provided as a guide for model development and parameter analysis. PMID:29719711

Antitumor activity of newly synthesized oxo and ethylidene derivatives of bile acids and their amides and oxazolines.

PubMed

Bjedov, Srđan; Jakimov, Dimitar; Pilipović, Ana; Poša, Mihalj; Sakač, Marija

2017-04-01

Bile acid derivatives with modifications in side chain and modifications on steroid skeleton were synthetized and their antitumor activity against five human cancer cell lines was investigated. Modifications in side chain include amid group, formed in reaction with 2-amino-2-methylpropanol, and 4,4-dimethyloxazoline group, obtained after cyclization of amides. In the steroid skeleton oxo groups were introduced in position 7 (2, 2a, 2b) and 7,12 (3, 3a, 3b). Ethylidene groups were introduced regio- and stereoselectively on C-7, and/or without stereoselectivity on C-3 by Wittig reaction. By combination of these modifications, a series of 19 bile acid derivatives were synthesized. Compounds containing both C-7 ethylidene and C-12 carbonyl groups (6, 6a, 6b) shown very good antitumor activity with IC 50 <5µM. Altering carboxylic group to amide or oxazoline group has positive effect on cytotoxicity. Different molecular descriptors were determined in silico and after principal component analysis was found that molecular descriptor BLTF96 can be used for fast assessment of experimental cytotoxicity of bile acid derivatives. Copyright © 2017 Elsevier Inc. All rights reserved.

Prioritization of in silico models and molecular descriptors for the assessment of ready biodegradability

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fernández, Alberto; Rallo, Robert; Giralt, Francesc

2015-10-15

Ready biodegradability is a key property for evaluating the long-term effects of chemicals on the environment and human health. As such, it is used as a screening test for the assessment of persistent, bioaccumulative and toxic substances. Regulators encourage the use of non-testing methods, such as in silico models, to save money and time. A dataset of 757 chemicals was collected to assess the performance of four freely available in silico models that predict ready biodegradability. They were applied to develop a new consensus method that prioritizes the use of each individual model according to its performance on chemical subsetsmore » driven by the presence or absence of different molecular descriptors. This consensus method was capable of almost eliminating unpredictable chemicals, while the performance of combined models was substantially improved with respect to that of the individual models. - Highlights: • Consensus method to predict ready biodegradability by prioritizing multiple QSARs. • Consensus reduced the amount of unpredictable chemicals to less than 2%. • Performance increased with the number of QSAR models considered. • The absence of 2D atom pairs contributed significantly to the consensus model.« less

QSAR models based on quantum topological molecular similarity.

PubMed

Popelier, P L A; Smith, P J

2006-07-01

A new method called quantum topological molecular similarity (QTMS) was fairly recently proposed [J. Chem. Inf. Comp. Sc., 41, 2001, 764] to construct a variety of medicinal, ecological and physical organic QSAR/QSPRs. QTMS method uses quantum chemical topology (QCT) to define electronic descriptors drawn from modern ab initio wave functions of geometry-optimised molecules. It was shown that the current abundance of computing power can be utilised to inject realistic descriptors into QSAR/QSPRs. In this article we study seven datasets of medicinal interest : the dissociation constants (pK(a)) for a set of substituted imidazolines , the pK(a) of imidazoles , the ability of a set of indole derivatives to displace [(3)H] flunitrazepam from binding to bovine cortical membranes , the influenza inhibition constants for a set of benzimidazoles , the interaction constants for a set of amides and the enzyme liver alcohol dehydrogenase , the natriuretic activity of sulphonamide carbonic anhydrase inhibitors and the toxicity of a series of benzyl alcohols. A partial least square analysis in conjunction with a genetic algorithm delivered excellent models. They are also able to highlight the active site, of the ligand or the molecule whose structure determines the activity. The advantages and limitations of QTMS are discussed.

Intuitive Density Functional Theory-Based Energy Decomposition Analysis for Protein-Ligand Interactions.

PubMed

Phipps, M J S; Fox, T; Tautermann, C S; Skylaris, C-K

2017-04-11

First-principles quantum mechanical calculations with methods such as density functional theory (DFT) allow the accurate calculation of interaction energies between molecules. These interaction energies can be dissected into chemically relevant components such as electrostatics, polarization, and charge transfer using energy decomposition analysis (EDA) approaches. Typically EDA has been used to study interactions between small molecules; however, it has great potential to be applied to large biomolecular assemblies such as protein-protein and protein-ligand interactions. We present an application of EDA calculations to the study of ligands that bind to the thrombin protein, using the ONETEP program for linear-scaling DFT calculations. Our approach goes beyond simply providing the components of the interaction energy; we are also able to provide visual representations of the changes in density that happen as a result of polarization and charge transfer, thus pinpointing the functional groups between the ligand and protein that participate in each kind of interaction. We also demonstrate with this approach that we can focus on studying parts (fragments) of ligands. The method is relatively insensitive to the protocol that is used to prepare the structures, and the results obtained are therefore robust. This is an application to a real protein drug target of a whole new capability where accurate DFT calculations can produce both energetic and visual descriptors of interactions. These descriptors can be used to provide insights for tailoring interactions, as needed for example in drug design.

Structural and spectroscopic characterization, reactivity study and charge transfer analysis of the newly synthetized 2-(6-hydroxy-1-benzofuran-3-yl) acetic acid

NASA Astrophysics Data System (ADS)

Murthy, P. Krishna; Krishnaswamy, G.; Armaković, Stevan; Armaković, Sanja J.; Suchetan, P. A.; Desai, Nivedita R.; Suneetha, V.; SreenivasaRao, R.; Bhargavi, G.; Aruna Kumar, D. B.

2018-06-01

The title compound 2-(6-hydroxy-1-benzofuran-3-yl) acetic acid (abbreviated as HBFAA) has been synthetized and characterized by FT-IR, FT-Raman and NMR spectroscopic techniques. Solid state crystal structure of HBFAA has been determined by single crystal X-ray diffraction technique. The crystal structure features O-H⋯O and C-H⋯O intermolecular interactions resulting in a two dimensional supramolecular architecture. The presence of various intermolecular interactions is well supported by the Hirshfeld surface analysis. The molecular properties of HBFAA were performed by Density functional theory (DFT) using B3LYP/6-311G++(d,p) method at ground state in gas phase, compile these results with experimental values and shows mutual agreement. The vibrational spectral analysis were carried out using FT-IR and FT-Raman spectroscopic techniques and assignment of each vibrational wavenumber made on the basis of potential energy distribution (PED). And also frontier orbital analysis (FMOs), global reactivity descriptors, non-linear optical properties (NLO) and natural bond orbital analysis (NBO) of HBFAA were computed with same method. Efforts were made in order to understand global and local reactivity properties of title compound by calculations of MEP, ALIE, BDE and Fukui function surfaces in gas phase, together with thermodynamic properties. Molecular dynamics simulation and radial distribution functions were also used in order to understand the influence of water to the stability of title compound. Charge transfer between molecules of HBFAA has been investigated thanks to the combination of MD simulations and DFT calculations.

Predicting phenolic acid absorption in Caco-2 cells: a theoretical permeability model and mechanistic study.

PubMed

Farrell, Tracy L; Poquet, Laure; Dew, Tristan P; Barber, Stuart; Williamson, Gary

2012-02-01

There is a considerable need to rationalize the membrane permeability and mechanism of transport for potential nutraceuticals. The aim of this investigation was to develop a theoretical permeability equation, based on a reported descriptive absorption model, enabling calculation of the transcellular component of absorption across Caco-2 monolayers. Published data for Caco-2 permeability of 30 drugs transported by the transcellular route were correlated with the descriptors 1-octanol/water distribution coefficient (log D, pH 7.4) and size, based on molecular mass. Nonlinear regression analysis was used to derive a set of model parameters a', β', and b' with an integrated molecular mass function. The new theoretical transcellular permeability (TTP) model obtained a good fit of the published data (R² = 0.93) and predicted reasonably well (R² = 0.86) the experimental apparent permeability coefficient (P(app)) for nine non-training set compounds reportedly transported by the transcellular route. For the first time, the TTP model was used to predict the absorption characteristics of six phenolic acids, and this original investigation was supported by in vitro Caco-2 cell mechanistic studies, which suggested that deviation of the P(app) value from the predicted transcellular permeability (P(app)(trans)) may be attributed to involvement of active uptake, efflux transporters, or paracellular flux.

Quantitative structure-cytotoxicity relationship of piperic acid amides.

PubMed

Shimada, Chiyako; Uesawa, Yoshihiro; Ishihara, Mariko; Kagaya, Hajime; Kanamoto, Taisei; Terakubo, Shigemi; Nakashima, Hideki; Takao, Koichi; Miyashiro, Takaki; Sugita, Yoshiaki; Sakagami, Hiroshi

2014-09-01

A total of 12 piperic acid amides, including piperine, were subjected to quantitative structure-activity relationship (QSAR) analysis, based on their cytotoxicity, tumor selectivity and anti-HIV activity, in order to find new biological activities. Cytotoxicity against four human oral squamous cell carcinoma (OSCC) cell lines and three human oral normal cells was determined by the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) method. Tumor selectivity was evaluated by the ratio of the mean 50% cytotoxic concentration (CC50) against normal oral cells to that against OSCC cell lines. Anti-HIV activity was evaluated by the ratio of the CC50 to 50% HIV infection-cytoprotective concentration (EC50). Physicochemical, structural, and quantum-chemical parameters were calculated based on the conformations optimized by LowModeMD method followed by density functional theory method. All compounds showed low-to-moderate tumor selectivity, but no anti-HIV activity. N-Piperoyldopamine ( 8: ) which has a catechol moiety, showed the highest tumor selectivity, possibly due to its unique molecular shape and electrostatic interaction, especially its largest partial equalization of orbital electronegativities and vsurf descriptors. The present study suggests that molecular shape and ability for electrostatic interaction are useful parameters for estimating the tumor selectivity of piperic acid amides. Copyright© 2014 International Institute of Anticancer Research (Dr. John G. Delinassios), All rights reserved.

Machine Learning of Parameters for Accurate Semiempirical Quantum Chemical Calculations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dral, Pavlo O.; von Lilienfeld, O. Anatole; Thiel, Walter

2015-05-12

We investigate possible improvements in the accuracy of semiempirical quantum chemistry (SQC) methods through the use of machine learning (ML) models for the parameters. For a given class of compounds, ML techniques require sufficiently large training sets to develop ML models that can be used for adapting SQC parameters to reflect changes in molecular composition and geometry. The ML-SQC approach allows the automatic tuning of SQC parameters for individual molecules, thereby improving the accuracy without deteriorating transferability to molecules with molecular descriptors very different from those in the training set. The performance of this approach is demonstrated for the semiempiricalmore » OM2 method using a set of 6095 constitutional isomers C7H10O2, for which accurate ab initio atomization enthalpies are available. The ML-OM2 results show improved average accuracy and a much reduced error range compared with those of standard OM2 results, with mean absolute errors in atomization enthalpies dropping from 6.3 to 1.7 kcal/mol. They are also found to be superior to the results from specific OM2 reparameterizations (rOM2) for the same set of isomers. The ML-SQC approach thus holds promise for fast and reasonably accurate high-throughput screening of materials and molecules.« less

A comprehensive physiologically based pharmacokinetic ...

EPA Pesticide Factsheets

Published physiologically based pharmacokinetic (PBPK) models from peer-reviewed articles are often well-parameterized, thoroughly-vetted, and can be utilized as excellent resources for the construction of models pertaining to related chemicals. Specifically, chemical-specific parameters and in vivo pharmacokinetic data used to calibrate these published models can act as valuable starting points for model development of new chemicals with similar molecular structures. A knowledgebase for published PBPK-related articles was compiled to support PBPK model construction for new chemicals based on their close analogues within the knowledgebase, and a web-based interface was developed to allow users to query those close analogues. A list of 689 unique chemicals and their corresponding 1751 articles was created after analysis of 2,245 PBPK-related articles. For each model, the PMID, chemical name, major metabolites, species, gender, life stages and tissue compartments were extracted from the published articles. PaDEL-Descriptor, a Chemistry Development Kit based software, was used to calculate molecular fingerprints. Tanimoto index was implemented in the user interface as measurement of structural similarity. The utility of the PBPK knowledgebase and web-based user interface was demonstrated using two case studies with ethylbenzene and gefitinib. Our PBPK knowledgebase is a novel tool for ranking chemicals based on similarities to other chemicals associated with existi

Molecular design of novel fullerene-based acceptors for enhancing the open circuit voltage in polymer solar cells

NASA Astrophysics Data System (ADS)

Tajbakhsh, Mahmood; Kariminasab, Mohaddeseh; Ganji, Masoud Darvish; Alinezhad, Heshmatollah

2017-12-01

Organic solar cells, especially bulk hetero-junction polymer solar cells (PSCs), are the most successful structures for applications in renewable energy. The dramatic improvement in the performance of PSCs has increased demand for new conjugated polymer donors and fullerene derivative acceptors. In the present study, quantum chemical calculations were performed for several representative fullerene derivatives in order to determine their frontier orbital energy levels and electronic structures, thereby helping to enhance their performance in PSC devices. We found correlations between the theoretical lowest unoccupied molecular orbital levels and electrophilicity index of various fullerenes with the experimental open circuit voltage of photovoltaic devices according to the poly(3-hexylthiophene) (P3HT):fullerene blend. The correlations between the structure and descriptors may facilitate screening of the best fullerene acceptor for the P3HT donor. Thus, we considered fullerenes with new functional groups and we predicted the output factors for the corresponding P3HT:fullerene blend devices. The results showed that fullerene derivatives based on thieno-o-quinodimethane-C60 with a methoxy group will have enhanced photovoltaic properties. Our results may facilitate the design of new fullerenes and the development of favorable acceptors for use in photovoltaic applications.

Synthesis, characterization and computational study of the newly synthetized sulfonamide molecule

NASA Astrophysics Data System (ADS)

Murthy, P. Krishna; Suneetha, V.; Armaković, Stevan; Armaković, Sanja J.; Suchetan, P. A.; Giri, L.; Rao, R. Sreenivasa

2018-02-01

A new compound N-(2,5-dimethyl-4-nitrophenyl)-4-methylbenzenesulfonamide (NDMPMBS) has been derived from 2,5-dimethyl-4-nitroaniline and 4-methylbenzene-1-sulfonyl chloride. Structure was characterized by SCXRD studies and spectroscopic tools. Compound crystallized in the monoclinic crystal system with P21/c space group a = 10.0549, b = 18.967, c = 8.3087, β = 103.18 and Z = 4. Type and nature of intermolecular interaction in crystal state investigated by 3D-Hirshfeld surface and 2D-finger print plots revealed that title compound stabilized by several interactions. The structural and electronic properties of title compound have been calculated at DFT/B3LYP/6-311G++(d,p) level of theory. Computationally obtained spectral data was compared with experimental results, showing excellent mutual agreement. Assignment of each vibrational wave number was done on the basis of potential energy distribution (PED). Investigation of local reactivity descriptors encompassed visualization of molecular electrostatic potential (MEP) and average local ionization energy (ALIE) surfaces, visualization of Fukui functions, natural bond order (NBO) analysis, bond dissociation energies for hydrogen abstraction (H-BDE) and radial distribution functions (RDF) after molecular dynamics (MD) simulations. MD simulations were also used in order to investigate interaction of NDMPMBS molecule with 1WKR and 3ETT proteins protein.

Machine learning of parameters for accurate semiempirical quantum chemical calculations

DOE PAGES

Dral, Pavlo O.; von Lilienfeld, O. Anatole; Thiel, Walter

2015-04-14

We investigate possible improvements in the accuracy of semiempirical quantum chemistry (SQC) methods through the use of machine learning (ML) models for the parameters. For a given class of compounds, ML techniques require sufficiently large training sets to develop ML models that can be used for adapting SQC parameters to reflect changes in molecular composition and geometry. The ML-SQC approach allows the automatic tuning of SQC parameters for individual molecules, thereby improving the accuracy without deteriorating transferability to molecules with molecular descriptors very different from those in the training set. The performance of this approach is demonstrated for the semiempiricalmore » OM2 method using a set of 6095 constitutional isomers C 7H 10O 2, for which accurate ab initio atomization enthalpies are available. The ML-OM2 results show improved average accuracy and a much reduced error range compared with those of standard OM2 results, with mean absolute errors in atomization enthalpies dropping from 6.3 to 1.7 kcal/mol. They are also found to be superior to the results from specific OM2 reparameterizations (rOM2) for the same set of isomers. The ML-SQC approach thus holds promise for fast and reasonably accurate high-throughput screening of materials and molecules.« less

Analysis of organophosphate-Zn metalloporphyrin interactions via UV-vis spectroscopy and molecular modeling.

PubMed

Rompoti, A; Dalal, N; Athanasopoulos, D; Rutan, S; Helburn, R

2015-01-25

UV-vis absorption spectra of zinc tetraphenylporphine (ZnTPP) on interaction with six organophosphorus (OP) compounds in cyclohexane were compared using ab initio methods and the molecular and solvation ligand descriptors π(*), Vx, and σ. OPs with polarizable hydrocarbon substituents in the homologous series tri-ethyl, -pentyl, -octyl, and -phenyl phosphates and the toxicologically relevant methyl paraoxon (1a-e) each gave a red shift in the Soret band (λsor) of ZnTPP in the range of 8-10 nm. Sensitivity as ΔAsor-b/Δug OP for the spectral band of the ligand bound ZnTPP (λsor-b) decreased with increasing extent of alkyl and aromatic substitution. Calculated and combined energies for OP and ZnTPP examined as a function of distance (Å) between ligand and porphyrin center suggest increased steric crowding with increasing Vx, and aromatic content of the OP. Spectrally fitted K1:1 and ΔAsor-b/ug OP each vary exponentially with Vx/σ. Lack of a red shift in λsor-b where ZnTPP was titrated with the toxic diethyl chlorophosphate (1g) is consistent with a model in which the magnitude of ΔEsor is proportional to the donor capacity of the phosphoryl-O ligand. Copyright © 2014 Elsevier B.V. All rights reserved.

Characterizing Changes in the Rate of Protein-Protein Dissociation upon Interface Mutation Using Hotspot Energy and Organization

PubMed Central

Agius, Rudi; Torchala, Mieczyslaw; Moal, Iain H.; Fernández-Recio, Juan; Bates, Paul A.

2013-01-01

Predicting the effects of mutations on the kinetic rate constants of protein-protein interactions is central to both the modeling of complex diseases and the design of effective peptide drug inhibitors. However, while most studies have concentrated on the determination of association rate constants, dissociation rates have received less attention. In this work we take a novel approach by relating the changes in dissociation rates upon mutation to the energetics and architecture of hotspots and hotregions, by performing alanine scans pre- and post-mutation. From these scans, we design a set of descriptors that capture the change in hotspot energy and distribution. The method is benchmarked on 713 kinetically characterized mutations from the SKEMPI database. Our investigations show that, with the use of hotspot descriptors, energies from single-point alanine mutations may be used for the estimation of off-rate mutations to any residue type and also multi-point mutations. A number of machine learning models are built from a combination of molecular and hotspot descriptors, with the best models achieving a Pearson's Correlation Coefficient of 0.79 with experimental off-rates and a Matthew's Correlation Coefficient of 0.6 in the detection of rare stabilizing mutations. Using specialized feature selection models we identify descriptors that are highly specific and, conversely, broadly important to predicting the effects of different classes of mutations, interface regions and complexes. Our results also indicate that the distribution of the critical stability regions across protein-protein interfaces is a function of complex size more strongly than interface area. In addition, mutations at the rim are critical for the stability of small complexes, but consistently harder to characterize. The relationship between hotregion size and the dissociation rate is also investigated and, using hotspot descriptors which model cooperative effects within hotregions, we show how the contribution of hotregions of different sizes, changes under different cooperative effects. PMID:24039569

The application of feature selection to the development of Gaussian process models for percutaneous absorption.

PubMed

Lam, Lun Tak; Sun, Yi; Davey, Neil; Adams, Rod; Prapopoulou, Maria; Brown, Marc B; Moss, Gary P

2010-06-01

The aim was to employ Gaussian processes to assess mathematically the nature of a skin permeability dataset and to employ these methods, particularly feature selection, to determine the key physicochemical descriptors which exert the most significant influence on percutaneous absorption, and to compare such models with established existing models. Gaussian processes, including automatic relevance detection (GPRARD) methods, were employed to develop models of percutaneous absorption that identified key physicochemical descriptors of percutaneous absorption. Using MatLab software, the statistical performance of these models was compared with single linear networks (SLN) and quantitative structure-permeability relationships (QSPRs). Feature selection methods were used to examine in more detail the physicochemical parameters used in this study. A range of statistical measures to determine model quality were used. The inherently nonlinear nature of the skin data set was confirmed. The Gaussian process regression (GPR) methods yielded predictive models that offered statistically significant improvements over SLN and QSPR models with regard to predictivity (where the rank order was: GPR > SLN > QSPR). Feature selection analysis determined that the best GPR models were those that contained log P, melting point and the number of hydrogen bond donor groups as significant descriptors. Further statistical analysis also found that great synergy existed between certain parameters. It suggested that a number of the descriptors employed were effectively interchangeable, thus questioning the use of models where discrete variables are output, usually in the form of an equation. The use of a nonlinear GPR method produced models with significantly improved predictivity, compared with SLN or QSPR models. Feature selection methods were able to provide important mechanistic information. However, it was also shown that significant synergy existed between certain parameters, and as such it was possible to interchange certain descriptors (i.e. molecular weight and melting point) without incurring a loss of model quality. Such synergy suggested that a model constructed from discrete terms in an equation may not be the most appropriate way of representing mechanistic understandings of skin absorption.

A new Schiff base compound N,N'-(2,2-dimetylpropane)-bis(dihydroxylacetophenone): synthesis, experimental and theoretical studies on its crystal structure, FTIR, UV-visible, 1H NMR and 13C NMR spectra.

PubMed

Saheb, Vahid; Sheikhshoaie, Iran

2011-10-15

The Schiff base compound, N,N'-(2,2-dimetylpropane)-bis(dihydroxylacetophenone) (NDHA) is synthesized through the condensation of 2-hydroxylacetophenone and 2,2-dimethyl 1,3-amino propane in methanol at ambient temperature. The yellow crystalline precipitate is used for X-ray single-crystal determination and measuring Fourier transform infrared (FTIR), UV-visible, (1)H NMR and (13)C NMR spectra. Electronic structure calculations at the B3LYP, PBEPBE and PW91PW91 levels of theory are performed to optimize the molecular geometry and to calculate the FTIR, (1)H NMR and (13)C NMR spectra of the compound. Time-dependent density functional theory (TDDFT) method is used to calculate the UV-visible spectrum of NDHA. Vibrational frequencies are determined experimentally and compared with those obtained theoretically. Vibrational assignments and analysis of the fundamental modes of the compound are also performed. All theoretical methods can well reproduce the structure of the compound. The (1)H NMR and (13)C NMR chemical shifts calculated by all DFT methods are consistent with the experimental data. However, the NMR shielding tensors computed at the B3LYP/6-31+G(d,p) level of theory are in better agreement with experimental (1)H NMR and (13)C NMR spectra. The electronic absorption spectrum calculated at the B3LYP/6-31+G(d,p) level by using TD-DFT method is in accordance with the observed UV-visible spectrum of NDHA. In addition, some quantum descriptors of the molecule are calculated and conformational analysis is performed and the results were compared with the crystallographic data. Copyright © 2011 Elsevier B.V. All rights reserved.

The symmetry of single-molecule conduction.

PubMed

Solomon, Gemma C; Gagliardi, Alessio; Pecchia, Alessandro; Frauenheim, Thomas; Di Carlo, Aldo; Reimers, Jeffrey R; Hush, Noel S

2006-11-14

We introduce the conductance point group which defines the symmetry of single-molecule conduction within the nonequilibrium Green's function formalism. It is shown, either rigorously or to within a very good approximation, to correspond to a molecular-conductance point group defined purely in terms of the properties of the conducting molecule. This enables single-molecule conductivity to be described in terms of key qualitative chemical descriptors that are independent of the nature of the molecule-conductor interfaces. We apply this to demonstrate how symmetry controls the conduction through 1,4-benzenedithiol chemisorbed to gold electrodes as an example system, listing also the molecular-conductance point groups for a range of molecules commonly used in molecular electronics research.

Metabolic biotransformation half-lives in fish: QSAR modeling and consensus analysis.

PubMed

Papa, Ester; van der Wal, Leon; Arnot, Jon A; Gramatica, Paola

2014-02-01

Bioaccumulation in fish is a function of competing rates of chemical uptake and elimination. For hydrophobic organic chemicals bioconcentration, bioaccumulation and biomagnification potential are high and the biotransformation rate constant is a key parameter. Few measured biotransformation rate constant data are available compared to the number of chemicals that are being evaluated for bioaccumulation hazard and for exposure and risk assessment. Three new Quantitative Structure-Activity Relationships (QSARs) for predicting whole body biotransformation half-lives (HLN) in fish were developed and validated using theoretical molecular descriptors that seek to capture structural characteristics of the whole molecule and three data set splitting schemes. The new QSARs were developed using a minimal number of theoretical descriptors (n=9) and compared to existing QSARs developed using fragment contribution methods that include up to 59 descriptors. The predictive statistics of the models are similar thus further corroborating the predictive performance of the different QSARs; Q(2)ext ranges from 0.75 to 0.77, CCCext ranges from 0.86 to 0.87, RMSE in prediction ranges from 0.56 to 0.58. The new QSARs provide additional mechanistic insights into the biotransformation capacity of organic chemicals in fish by including whole molecule descriptors and they also include information on the domain of applicability for the chemical of interest. Advantages of consensus modeling for improving overall prediction and minimizing false negative errors in chemical screening assessments, for identifying potential sources of residual error in the empirical HLN database, and for identifying structural features that are not well represented in the HLN dataset to prioritize future testing needs are illustrated. © 2013.

Beware of ligand efficiency (LE): understanding LE data in modeling structure-activity and structure-economy relationships.

PubMed

Polanski, Jaroslaw; Tkocz, Aleksandra; Kucia, Urszula

2017-09-11

On the one hand, ligand efficiency (LE) and the binding efficiency index (BEI), which are binding properties (B) averaged versus the heavy atom count (HAC: LE) or molecular weight (MW: BEI), have recently been declared a novel universal tool for drug design. On the other hand, questions have been raised about the mathematical validity of the LE approach. In fact, neither the critics nor the advocates are precise enough to provide a generally understandable and accepted chemistry of the LE metrics. In particular, this refers to the puzzle of the LE trends for small and large molecules. In this paper, we explain the chemistry and mathematics of the LE type of data. Because LE is a weight metrics related to binding per gram, its hyperbolic decrease with an increasing number of heavy atoms can be easily understood by its 1/MW dependency. Accordingly, we analyzed how this influences the LE trends for ligand-target binding, economic big data or molecular descriptor data. In particular, we compared the trends for the thermodynamic ∆G data of a series of ligands that interact with 14 different target classes, which were extracted from the BindingDB database with the market prices of a commercial compound library of ca. 2.5 mln synthetic building blocks. An interpretation of LE and BEI that clearly explains the observed trends for these parameters are presented here for the first time. Accordingly, we show that the main misunderstanding of the chemical meaning of the BEI and LE parameters is their interpretation as molecular descriptors that are connected with a single molecule, while binding is a statistical effect in which a population of ligands limits the formation of ligand-receptor complexes. Therefore, LE (BEI) should not be interpreted as a molecular (physicochemical) descriptor that is connected with a single molecule but as a property (binding per gram). Accordingly, the puzzle of the surprising behavior of LE is explained by the 1/MW dependency. This effect clearly explains the hyperbolic LE trend not as a real increase in binding potency but as a physical limitation due to the different population of ligands with different MWs in a 1 g sample available for the formation of ligand-receptor complexes. Graphical abstract .

A Computational and Theoretical Study of Conductance in Hydrogen-bonded Molecular Junctions

NASA Astrophysics Data System (ADS)

Wimmer, Michael

This thesis is devoted to the theoretical and computational study of electron transport in molecular junctions where one or more hydrogen bonds are involved in the process. While electron transport through covalent bonds has been extensively studied, in recent work the focus has been shifted towards hydrogen-bonded systems due to their ubiquitous presence in biological systems and their potential in forming nano-junctions between molecular electronic devices and biological systems. This analysis allows us to significantly expand our comprehension of the experimentally observed result that the inclusion of hydrogen bonding in a molecular junction significantly impacts its transport properties, a fact that has important implications for our understanding of transport through DNA, and nano-biological interfaces in general. In part of this work I have explored the implications of quasiresonant transport in short chains of weakly-bonded molecular junctions involving hydrogen bonds. I used theoretical and computational analysis to interpret recent experiments and explain the role of Fano resonances in the transmission properties of the junction. In a different direction, I have undertaken the study of the transversal conduction through nucleotide chains that involve a variable number of different hydrogen bonds, e.g. NH˙˙˙O, OH˙˙˙O, and NH˙˙˙N, which are the three most prevalent hydrogen bonds in biological systems and organic electronics. My effort here has focused on the analysis of electronic descriptors that allow a simplified conceptual and computational understanding of transport properties. Specifically, I have expanded our previous work where the molecular polarizability was used as a conductance descriptor to include the possibility of atomic and bond partitions of the molecular polarizability. This is important because it affords an alternative molecular description of conductance that is not based on the conventional view of molecular orbitals as transport channels. My findings suggest that the hydrogen-bond networks are crucial in understanding the conductance of these junctions. A broader impact of this work pertains the fact that characterizing transport through hydrogen bonding networks may help in developing faster and cost-effective approaches to personalized medicine, to advance DNA sequencing and implantable electronics, and to progress in the design and application of new drugs.

Synthesis, crystal structure analysis, spectral investigations, DFT computations, Biological activities and molecular docking of methyl(2E)-2-{[N-(2-formylphenyl)(4-methylbenzene) sulfonamido]methyl}-3-(4-fluorophenyl)prop-2-enoate, a potential bioactive agent

NASA Astrophysics Data System (ADS)

Murugavel, S.; Vetri Velan, V.; Kannan, Damodharan; Bakthadoss, Manickam

2016-03-01

The title compound methyl(2E)-2-{[N-(2-formylphenyl) (4-methylbenzene)sulfonamido]methyl}-3-(4-fluorophenyl) prop-2-enoate (MFMSF) has been synthesized and single crystals were grown by slow evaporation solution growth technique at room temperature. The grown crystals were characterized by FTIR, 1H NMR, 13C NMR, and single crystal X-ray diffraction. In the crystal, molecules are linked by intermolecular C-H…O hydrogen bonds forming a two-dimensional supramolecular network along [110] direction. The molecular geometry was also optimized using density functional theory (DFT/B3LYP) method with the 6-311G (d,p) basis set in ground state and compared with the experimental data. The entire vibrational assignments of wave numbers were made on the basis of potential energy distribution (PED) by VEDA 4 programme. Stability of the molecule arising from hyper conjugative interactions, charge delocalization has been analyzed using natural bond orbital (NBO) analysis. In addition, NLO, MEP, Mulliken, thermodynamic properties, HOMO and LUMO energy gap were theoretically predicted. The global chemical reactivity descriptors are calculated for MFMSF and used to predict their relative stability and reactivity. The antibacterial activity of the compound was also tested against various pathogens. The molecular docking studies concede that title compound may exhibit PBP-2X inhibitor activity.

Molecular structure, vibrational spectra and DFT computational studies of melaminium N-acetylglycinate dihydrate

NASA Astrophysics Data System (ADS)

Tanak, H.; Pawlus, K.; Marchewka, M. K.

2016-10-01

Melaminium N-acetylglycinate dihydrate, an organic material has been synthesized and characterized by X-ray diffraction, FT-IR, and FT-Raman spectroscopies for the protiated and deuteriated crystals. The title complex crystallizes in the triclinic system, and the space group is P-1 with a = 5.642(1) Å, b = 7.773(2) Å, c = 15.775(3) Å, α = 77.28(1)°, β = 84.00(1)°, γ = 73.43(1)° and Z = 2. The molecular geometry, vibrational frequencies and intensity of the vibrational bands have been interpreted with the aid of structure optimization based on density functional method (B3LYP) with the 6-311++G(d,p) basis set. The obtained vibrational wavenumbers and optimized geometric parameters were seen to be in good agreement with the experimental data. The intermolecular hydrogen bonding interactions of the title compound have been investigated using the natural bonding orbital analysis. It reveals that the O-H···O, N-H···N and N-H···O intermolecular interactions significantly influence crystal packing of this molecule. The non-linear optical properties are also addressed theoretically. The predicted NLO properties of the title compound are much greater than ones of urea. In addition, DFT calculations of the title compound, molecular electrostatic potential, thermodynamic properties, frontier orbitals and chemical reactivity descriptors were also performed at 6-311++G(d,p) level of theory.

Some connectivity indices and zagreb index of polyhex nanotubes.

PubMed

Farahani, Mohammad Reza

2012-12-01

Several topological indices are investigated in polyhex nanotubes: Randić connectivity index, sum-connectivity index, atom-bond connectivity index, geometric-arithmetic index, First and Second Zagreb indices and Zagreb polynomials. Formulas for calculating the above topological descriptors in polyhex zigzag TUZC6[m,n] and armchair TUAC6[m,n] nanotube families are given.

A Critical Review of Air Pollution Index Systems in the United States and Canada

ERIC Educational Resources Information Center

Ott, Wayne R.; Thom, Gary C.

1976-01-01

An extensive survey of air pollution indices reveals great diversity in calculation and descriptor categories. This lack of uniformity creates confusion, suggests questionable technical validity, and discourages a national picture. The authors recombined indices currently in use to develop a Standardized Urban Air Quality Index for national use.…

Effective mass and Fermi surface complexity factor from ab initio band structure calculations

NASA Astrophysics Data System (ADS)

Gibbs, Zachary M.; Ricci, Francesco; Li, Guodong; Zhu, Hong; Persson, Kristin; Ceder, Gerbrand; Hautier, Geoffroy; Jain, Anubhav; Snyder, G. Jeffrey

2017-02-01

The effective mass is a convenient descriptor of the electronic band structure used to characterize the density of states and electron transport based on a free electron model. While effective mass is an excellent first-order descriptor in real systems, the exact value can have several definitions, each of which describe a different aspect of electron transport. Here we use Boltzmann transport calculations applied to ab initio band structures to extract a density-of-states effective mass from the Seebeck Coefficient and an inertial mass from the electrical conductivity to characterize the band structure irrespective of the exact scattering mechanism. We identify a Fermi Surface Complexity Factor: Nv*K* from the ratio of these two masses, which in simple cases depends on the number of Fermi surface pockets (Nv* ) and their anisotropy K*, both of which are beneficial to high thermoelectric performance as exemplified by the high values found in PbTe. The Fermi Surface Complexity factor can be used in high-throughput search of promising thermoelectric materials.

Self-consistent self-interaction corrected density functional theory calculations for atoms using Fermi-Löwdin orbitals: Optimized Fermi-orbital descriptors for Li-Kr

NASA Astrophysics Data System (ADS)

Kao, Der-you; Withanage, Kushantha; Hahn, Torsten; Batool, Javaria; Kortus, Jens; Jackson, Koblar

2017-10-01

In the Fermi-Löwdin orbital method for implementing self-interaction corrections (FLO-SIC) in density functional theory (DFT), the local orbitals used to make the corrections are generated in a unitary-invariant scheme via the choice of the Fermi orbital descriptors (FODs). These are M positions in 3-d space (for an M-electron system) that can be loosely thought of as classical electron positions. The orbitals that minimize the DFT energy including the SIC are obtained by finding optimal positions for the FODs. In this paper, we present optimized FODs for the atoms from Li-Kr obtained using an unbiased search method and self-consistent FLO-SIC calculations. The FOD arrangements display a clear shell structure that reflects the principal quantum numbers of the orbitals. We describe trends in the FOD arrangements as a function of atomic number. FLO-SIC total energies for the atoms are presented and are shown to be in close agreement with the results of previous SIC calculations that imposed explicit constraints to determine the optimal local orbitals, suggesting that FLO-SIC yields the same solutions for atoms as these computationally demanding earlier methods, without invoking the constraints.

Relationship between electronic properties and drug activity of seven quinoxaline compounds: A DFT study

NASA Astrophysics Data System (ADS)

Behzadi, Hadi; Roonasi, Payman; Assle taghipour, Khatoon; van der Spoel, David; Manzetti, Sergio

2015-07-01

The quantum chemical calculations at the DFT/B3LYP level of theory were carried out on seven quinoxaline compounds, which have been synthesized as anti-Mycobacterium tuberculosis agents. Three conformers were optimized for each compound and the lowest energy structure was found and used in further calculations. The electronic properties including EHOMO, ELUMO and related parameters as well as electron density around oxygen and nitrogen atoms were calculated for each compound. The relationship between the calculated electronic parameters and biological activity of the studied compounds were investigated. Six similar quinoxaline derivatives with possible more drug activity were suggested based on the calculated electronic descriptors. A mechanism was proposed and discussed based on the calculated electronic parameters and bond dissociation energies.

Supramolecular architecture of 5-bromo-7-methoxy-1-methyl-1H-benzoimidazole.3H2O: Synthesis, spectroscopic investigations, DFT computation, MD simulations and docking studies

NASA Astrophysics Data System (ADS)

Murthy, P. Krishna; Smitha, M.; Sheena Mary, Y.; Armaković, Stevan; Armaković, Sanja J.; Rao, R. Sreenivasa; Suchetan, P. A.; Giri, L.; Pavithran, Rani; Van Alsenoy, C.

2017-12-01

Crystal and molecular structure of newly synthesized compound 5-bromo-7-methoxy-1-methyl-1H-benzoimidazole (BMMBI) has been authenticated by single crystal X-ray diffraction, FT-IR, FT-Raman, 1H NMR, 13C NMR and UV-Visible spectroscopic techniques; compile both experimental and theoretical results which are performed by DFT/B3LYP/6-311++G(d,p) method at ground state in gas phase. Visualize nature and type of intermolecular interactions and crucial role of these interactions in supra-molecular architecture has been investigated by use of a set of graphical tools 3D-Hirshfeld surfaces and 2D-fingerprint plots analysis. The title compound stabilized by strong intermolecular hydrogen bonds N⋯Hsbnd O and O⋯Hsbnd O, which are envisaged by dark red spots on dnorm mapped surfaces and weak Br⋯Br contacts envisaged by red spot on dnorm mapped surface. The detailed fundamental vibrational assignments of wavenumbers were aid by with help of Potential Energy distribution (PED) analysis by using GAR2PED program and shows good agreement with experimental values. Besides frontier orbitals analysis, global reactivity descriptors, natural bond orbitals and Mullikan charges analysis were performed by same basic set at ground state in gas phase. Potential reactive sites of the title compound have been identified by ALIE, Fukui functions and MEP, which are mapped to the electron density surfaces. Stability of BMMBI have been investigated from autoxidation process and pronounced interaction with water (hydrolysis) by using bond dissociation energies (BDE) and radial distribution functions (RDF), respectively after MD simulations. In order to identify molecule's most important reactive spots we have used a combination of DFT calculations and MD simulations. Reactivity study encompassed calculations of a set of quantities such as: HOMO-LUMO gap, MEP and ALIE surfaces, Fukui functions, bond dissociation energies and radial distribution functions. To confirm the potential of title molecule in the area of pharmaceutics, we have also calculated a series of drug likeness parameters. Possibly important biological activity of BMMBI molecule was also confirmed by molecular docking study.

Chaining direct memory access data transfer operations for compute nodes in a parallel computer

DOEpatents

Archer, Charles J.; Blocksome, Michael A.

2010-09-28

Methods, systems, and products are disclosed for chaining DMA data transfer operations for compute nodes in a parallel computer that include: receiving, by an origin DMA engine on an origin node in an origin injection FIFO buffer for the origin DMA engine, a RGET data descriptor specifying a DMA transfer operation data descriptor on the origin node and a second RGET data descriptor on the origin node, the second RGET data descriptor specifying a target RGET data descriptor on the target node, the target RGET data descriptor specifying an additional DMA transfer operation data descriptor on the origin node; creating, by the origin DMA engine, an RGET packet in dependence upon the RGET data descriptor, the RGET packet containing the DMA transfer operation data descriptor and the second RGET data descriptor; and transferring, by the origin DMA engine to a target DMA engine on the target node, the RGET packet.

Replenishing data descriptors in a DMA injection FIFO buffer

DOEpatents

Archer, Charles J [Rochester, MN; Blocksome, Michael A [Rochester, MN; Cernohous, Bob R [Rochester, MN; Heidelberger, Philip [Cortlandt Manor, NY; Kumar, Sameer [White Plains, NY; Parker, Jeffrey J [Rochester, MN

2011-10-11

Methods, apparatus, and products are disclosed for replenishing data descriptors in a Direct Memory Access (`DMA`) injection first-in-first-out (`FIFO`) buffer that include: determining, by a messaging module on an origin compute node, whether a number of data descriptors in a DMA injection FIFO buffer exceeds a predetermined threshold, each data descriptor specifying an application message for transmission to a target compute node; queuing, by the messaging module, a plurality of new data descriptors in a pending descriptor queue if the number of the data descriptors in the DMA injection FIFO buffer exceeds the predetermined threshold; establishing, by the messaging module, interrupt criteria that specify when to replenish the injection FIFO buffer with the plurality of new data descriptors in the pending descriptor queue; and injecting, by the messaging module, the plurality of new data descriptors into the injection FIFO buffer in dependence upon the interrupt criteria.

Empirical temperature-dependent intermolecular potentials determined by data mining from crystal data

NASA Astrophysics Data System (ADS)

Hofmann, D. W. M.; Kuleshova, L. N.

2018-05-01

Modern force fields are accurate enough to describe thermal effects in molecular crystals. Here, we have extended our earlier approach to discrete force fields for various temperatures to a force field with a continuous function. For the parametrisation of the force field, we used data mining on experimental structures with the temperature as an additional descriptor. The obtained force field can be used to minimise energy at a finite temperature and for molecular dynamics with zero-K potentials. The applicability of the method has been demonstrated for the prediction of crystal density, temperature density gradients and transition temperature.

QSAR modeling of cumulative environmental end-points for the prioritization of hazardous chemicals.

PubMed

Gramatica, Paola; Papa, Ester; Sangion, Alessandro

2018-01-24

The hazard of chemicals in the environment is inherently related to the molecular structure and derives simultaneously from various chemical properties/activities/reactivities. Models based on Quantitative Structure Activity Relationships (QSARs) are useful to screen, rank and prioritize chemicals that may have an adverse impact on humans and the environment. This paper reviews a selection of QSAR models (based on theoretical molecular descriptors) developed for cumulative multivariate endpoints, which were derived by mathematical combination of multiple effects and properties. The cumulative end-points provide an integrated holistic point of view to address environmentally relevant properties of chemicals.

Evaluation of the structural, electronic, topological and vibrational properties of N-(3,4-dimethoxybenzyl)-hexadecanamide isolated from Maca (Lepidium meyenii) using different spectroscopic techniques

NASA Astrophysics Data System (ADS)

Chain, Fernando; Iramain, Maximiliano Alberto; Grau, Alfredo; Catalán, César A. N.; Brandán, Silvia Antonia

2017-01-01

N-(3,4-dimethoxybenzyl)-hexadecanamide (DMH) was characterized by using Fourier Transform infrared (FT-IR) and Raman (FT-Raman), Ultraviolet- Visible (UV-Visible) and Hydrogen and Carbon Nuclear Magnetic Resonance (1H and 13C NMR) spectroscopies. The structural, electronic, topological and vibrational properties were evaluated in gas phase and in n-hexane employing ONIOM and self-consistent force field (SCRF) calculations. The atomic charges, molecular electrostatic potentials, stabilization energies and topological properties of DMH were analyzed and compared with those calculated for N-(3,4-dimethoxybenzyl)-acetamide (DMA) in order to evaluate the effect of the side chain on the properties of DMH. The reactivity and behavior of this alkamide were predicted by using the gap energies and some descriptors. Force fields and the corresponding force constants were reported for DMA only in gas phase and n-hexane due to the high number of vibration normal modes showed by DMH, while the complete vibrational assignments are presented for DMA and both forms of DMH. The comparisons between the experimental FTIR, FT-Raman, UV-Visible and 1H and 13C NMR spectra with the corresponding theoretical ones showed a reasonable concordance.

Universal fragment descriptors for predicting properties of inorganic crystals

NASA Astrophysics Data System (ADS)

Isayev, Olexandr; Oses, Corey; Toher, Cormac; Gossett, Eric; Curtarolo, Stefano; Tropsha, Alexander

2017-06-01

Although historically materials discovery has been driven by a laborious trial-and-error process, knowledge-driven materials design can now be enabled by the rational combination of Machine Learning methods and materials databases. Here, data from the AFLOW repository for ab initio calculations is combined with Quantitative Materials Structure-Property Relationship models to predict important properties: metal/insulator classification, band gap energy, bulk/shear moduli, Debye temperature and heat capacities. The prediction's accuracy compares well with the quality of the training data for virtually any stoichiometric inorganic crystalline material, reciprocating the available thermomechanical experimental data. The universality of the approach is attributed to the construction of the descriptors: Property-Labelled Materials Fragments. The representations require only minimal structural input allowing straightforward implementations of simple heuristic design rules.

Universal fragment descriptors for predicting properties of inorganic crystals.

PubMed

Isayev, Olexandr; Oses, Corey; Toher, Cormac; Gossett, Eric; Curtarolo, Stefano; Tropsha, Alexander

2017-06-05

Although historically materials discovery has been driven by a laborious trial-and-error process, knowledge-driven materials design can now be enabled by the rational combination of Machine Learning methods and materials databases. Here, data from the AFLOW repository for ab initio calculations is combined with Quantitative Materials Structure-Property Relationship models to predict important properties: metal/insulator classification, band gap energy, bulk/shear moduli, Debye temperature and heat capacities. The prediction's accuracy compares well with the quality of the training data for virtually any stoichiometric inorganic crystalline material, reciprocating the available thermomechanical experimental data. The universality of the approach is attributed to the construction of the descriptors: Property-Labelled Materials Fragments. The representations require only minimal structural input allowing straightforward implementations of simple heuristic design rules.

Computational prediction of the pKas of small peptides through Conceptual DFT descriptors

NASA Astrophysics Data System (ADS)

Frau, Juan; Hernández-Haro, Noemí; Glossman-Mitnik, Daniel

2017-03-01

The experimental pKa of a group of simple amines have been plotted against several Conceptual DFT descriptors calculated by means of different density functionals, basis sets and solvation schemes. It was found that the best fits are those that relate the pKa of the amines with the global hardness η through the MN12SX density functional in connection with the Def2TZVP basis set and the SMD solvation model, using water as a solvent. The parameterized equation resulting from the linear regression analysis has then been used for the prediction of the pKa of small peptides of interest in the study of diabetes and Alzheimer disease. The accuracy of the results is relatively good, with a MAD of 0.36 units of pKa.

From QSAR to QSIIR: Searching for Enhanced Computational Toxicology Models

PubMed Central

Zhu, Hao

2017-01-01

Quantitative Structure Activity Relationship (QSAR) is the most frequently used modeling approach to explore the dependency of biological, toxicological, or other types of activities/properties of chemicals on their molecular features. In the past two decades, QSAR modeling has been used extensively in drug discovery process. However, the predictive models resulted from QSAR studies have limited use for chemical risk assessment, especially for animal and human toxicity evaluations, due to the low predictivity of new compounds. To develop enhanced toxicity models with independently validated external prediction power, novel modeling protocols were pursued by computational toxicologists based on rapidly increasing toxicity testing data in recent years. This chapter reviews the recent effort in our laboratory to incorporate the biological testing results as descriptors in the toxicity modeling process. This effort extended the concept of QSAR to Quantitative Structure In vitro-In vivo Relationship (QSIIR). The QSIIR study examples provided in this chapter indicate that the QSIIR models that based on the hybrid (biological and chemical) descriptors are indeed superior to the conventional QSAR models that only based on chemical descriptors for several animal toxicity endpoints. We believe that the applications introduced in this review will be of interest and value to researchers working in the field of computational drug discovery and environmental chemical risk assessment. PMID:23086837

A Viral-Human Interactome Based on Structural Motif-Domain Interactions Captures the Human Infectome

PubMed Central

Guo, Xianwu; Rodríguez-Pérez, Mario A.

2013-01-01

Protein interactions between a pathogen and its host are fundamental in the establishment of the pathogen and underline the infection mechanism. In the present work, we developed a single predictive model for building a host-viral interactome based on the identification of structural descriptors from motif-domain interactions of protein complexes deposited in the Protein Data Bank (PDB). The structural descriptors were used for searching, in a database of protein sequences of human and five clinically important viruses; therefore, viral and human proteins sharing a descriptor were predicted as interacting proteins. The analysis of the host-viral interactome allowed to identify a set of new interactions that further explain molecular mechanism associated with viral infections and showed that it was able to capture human proteins already associated to viral infections (human infectome) and non-infectious diseases (human diseasome). The analysis of human proteins targeted by viral proteins in the context of a human interactome showed that their neighbors are enriched in proteins reported with differential expression under infection and disease conditions. It is expected that the findings of this work will contribute to the development of systems biology for infectious diseases, and help guide the rational identification and prioritization of novel drug targets. PMID:23951184

QSAR and 3D-QSAR studies applied to compounds with anticonvulsant activity.

PubMed

Garro Martinez, Juan C; Vega-Hissi, Esteban G; Andrada, Matías F; Estrada, Mario R

2015-01-01

Quantitative structure-activity relationships (QSAR and 3D-QSAR) have been applied in the last decade to obtain a reliable statistical model for the prediction of the anticonvulsant activities of new chemical entities. However, despite the large amount of information on QSAR, no recent review has published and discussed this data in detail. In this review, the authors provide a detailed discussion of QSAR studies that have been applied to compounds with anticonvulsant activity published between the years 2003 and 2013. They also evaluate the mathematical approaches and the main software used to develop the QSAR and 3D-QSAR model. QSAR methodologies continue to attract the attention of researchers and provide valuable information for the development of new potentially active compounds including those with anticonvulsant activity. This has been helped in part by improvements in the size and performance of computers; the development of specific software and the development of novel molecular descriptors, which have given rise to new and more predictive QSAR models. The extensive development of descriptors, and the way by which descriptor values are derived, have allowed the evolution of the QSAR methods. This evolution could strengthen the QSAR methods as an important tool in research and development of new and more potent anticonvulsant agents.

Multiple QSAR models, pharmacophore pattern and molecular docking analysis for anticancer activity of α, β-unsaturated carbonyl-based compounds, oxime and oxime ether analogues

NASA Astrophysics Data System (ADS)

Masand, Vijay H.; El-Sayed, Nahed N. E.; Bambole, Mukesh U.; Quazi, Syed A.

2018-04-01

Multiple discrete quantitative structure-activity relationships (QSARs) models were constructed for the anticancer activity of α, β-unsaturated carbonyl-based compounds, oxime and oxime ether analogues with a variety of substituents like sbnd Br, sbnd OH, -OMe, etc. at different positions. A big pool of descriptors was considered for QSAR model building. Genetic algorithm (GA), available in QSARINS-Chem, was executed to choose optimum number and set of descriptors to create the multi-linear regression equations for a dataset of sixty-nine compounds. The newly developed five parametric models were subjected to exhaustive internal and external validation along with Y-scrambling using QSARINS-Chem, according to the OECD principles for QSAR model validation. The models were built using easily interpretable descriptors and accepted after confirming statistically robustness with high external predictive ability. The five parametric models were found to have R2 = 0.80 to 0.86, R2ex = 0.75 to 0.84, and CCCex = 0.85 to 0.90. The models indicate that frequency of nitrogen and oxygen atoms separated by five bonds from each other and internal electronic environment of the molecule have correlation with the anticancer activity.

High throughput heuristics for prioritizing human exposure to environmental chemicals.

PubMed

Wambaugh, John F; Wang, Anran; Dionisio, Kathie L; Frame, Alicia; Egeghy, Peter; Judson, Richard; Setzer, R Woodrow

2014-11-04

The risk posed to human health by any of the thousands of untested anthropogenic chemicals in our environment is a function of both the hazard presented by the chemical and the extent of exposure. However, many chemicals lack estimates of exposure intake, limiting the understanding of health risks. We aim to develop a rapid heuristic method to determine potential human exposure to chemicals for application to the thousands of chemicals with little or no exposure data. We used Bayesian methodology to infer ranges of exposure consistent with biomarkers identified in urine samples from the U.S. population by the National Health and Nutrition Examination Survey (NHANES). We performed linear regression on inferred exposure for demographic subsets of NHANES demarked by age, gender, and weight using chemical descriptors and use information from multiple databases and structure-based calculators. Five descriptors are capable of explaining roughly 50% of the variability in geometric means across 106 NHANES chemicals for all the demographic groups, including children aged 6-11. We use these descriptors to estimate human exposure to 7968 chemicals, the majority of which have no other quantitative exposure prediction. For thousands of chemicals with no other information, this approach allows forecasting of average exposure intake of environmental chemicals.