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.

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.

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.

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.

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

Bio-activity of aminosulfonyl ureas in the light of nucleic acid bases and DNA base pair interaction.

PubMed

Mondal Roy, Sutapa

2018-08-01

The quantum chemical descriptors based on density functional theory (DFT) are applied to predict the biological activity (log IC 50 ) of one class of acyl-CoA: cholesterol O-acyltransferase (ACAT) inhibitors, viz. aminosulfonyl ureas. ACAT are very effective agents for reduction of triglyceride and cholesterol levels in human body. Successful two parameter quantitative structure-activity relationship (QSAR) models are developed with a combination of relevant global and local DFT based descriptors for prediction of biological activity of aminosulfonyl ureas. The global descriptors, electron affinity of the ACAT inhibitors (EA) and/or charge transfer (ΔN) between inhibitors and model biosystems (NA bases and DNA base pairs) along with the local group atomic charge on sulfonyl moiety (∑Q Sul ) of the inhibitors reveals more than 90% efficacy of the selected descriptors for predicting the experimental log (IC 50 ) values. Copyright © 2018 Elsevier Ltd. All rights reserved.

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.

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.

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.

Quantum chemical characterization of zwitterionic structures: Supramolecular complexes for modifying the wettability of oil-water-limestone system.

PubMed

Lopez-Chavez, Ernesto; Garcia-Quiroz, Alberto; Gonzalez-Garcia, Gerardo; Orozco-Duran, Gabriela E; Zamudio-Rivera, Luis S; Martinez-Magadan, José M; Buenrostro-Gonzalez, Eduardo; Hernandez-Altamirano, Raul

2014-06-01

In this work, we present a quantum chemical study pertaining to some supramolecular complexes acting as wettability modifiers of oil-water-limestone system. The complexes studied are derived from zwitterionic liquids of the types N'-alkyl-bis, N-alquenil, N-cycloalkyl, N-amyl-bis-beta amino acid or salts acting as sparkling agents. We studied two molecules of zwitterionic liquids (ZL10 and ZL13), HOMO and LUMO levels, and the energy gap between them, were calculated, as well as the electron affinity (EA) and ionization potential (IP), chemical potential, chemical hardness, chemical electrophilicity index and selectivity descriptors such Fukui indices. In this work, electrochemical comparison was realized with cocamidopropyl betaine (CPB), which is a structure zwitterionic liquid type, nowadays widely applied in enhanced recovery processes. Copyright © 2014 Elsevier Inc. All rights reserved.

Quantum chemistry in environmental pesticide risk assessment.

PubMed

Villaverde, Juan J; López-Goti, Carmen; Alcamí, Manuel; Lamsabhi, Al Mokhtar; Alonso-Prados, José L; Sandín-España, Pilar

2017-11-01

The scientific community and regulatory bodies worldwide, currently promote the development of non-experimental tests that produce reliable data for pesticide risk assessment. The use of standard quantum chemistry methods could allow the development of tools to perform a first screening of compounds to be considered for the experimental studies, improving the risk assessment. This fact results in a better distribution of resources and in better planning, allowing a more exhaustive study of the pesticides and their metabolic products. The current paper explores the potential of quantum chemistry in modelling toxicity and environmental behaviour of pesticides and their by-products by using electronic descriptors obtained computationally. Quantum chemistry has potential to estimate the physico-chemical properties of pesticides, including certain chemical reaction mechanisms and their degradation pathways, allowing modelling of the environmental behaviour of both pesticides and their by-products. In this sense, theoretical methods can contribute to performing a more focused risk assessment of pesticides used in the market, and may lead to higher quality and safer agricultural products. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

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.

Synthesis and DFT calculations of some 2-aminothiazoles

NASA Astrophysics Data System (ADS)

Rezania, Jafar; Behzadi, Hadi; Shockravi, Abbas; Ehsani, Morteza; Akbarzadeh, Elahe

2018-04-01

A series of 2-aminothiazole derivatives have been synthesized by the reaction of acetyl compounds with thiourea and iodine as catalyst under solvent-free condition, a green chemistry method. The quantum chemical calculations at the DFT/B3LYP level of theory in gas phase were carried out for starting acetyl derivatives. The highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) and related reactivity descriptor of acetyl derivatives, as well as, enthalpy of reactions are calculated in order to investigate the reaction properties of acetyl compounds and yields of the reactions. The calculated reactivity descriptors are well correlated to activity of different acetyl derivatives.

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.

Electron-density descriptors as predictors in quantitative structure--activity/property relationships and drug design.

PubMed

Matta, Chérif F; Arabi, Alya A

2011-06-01

The use of electron density-based molecular descriptors in drug research, particularly in quantitative structure--activity relationships/quantitative structure--property relationships studies, is reviewed. The exposition starts by a discussion of molecular similarity and transferability in terms of the underlying electron density, which leads to a qualitative introduction to the quantum theory of atoms in molecules (QTAIM). The starting point of QTAIM is the topological analysis of the molecular electron-density distributions to extract atomic and bond properties that characterize every atom and bond in the molecule. These atomic and bond properties have considerable potential as bases for the construction of robust quantitative structure--activity/property relationships models as shown by selected examples in this review. QTAIM is applicable to the electron density calculated from quantum-chemical calculations and/or that obtained from ultra-high resolution x-ray diffraction experiments followed by nonspherical refinement. Atomic and bond properties are introduced followed by examples of application of each of these two families of descriptors. The review ends with a study whereby the molecular electrostatic potential, uniquely determined by the density, is used in conjunction with atomic properties to elucidate the reasons for the biological similarity of bioisosteres.

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.

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.

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 quantum chemical study of molecular properties and QSPR modeling of oximes, amidoximes and hydroxamic acids with nucleophilic activity against toxic organophosphorus agents

NASA Astrophysics Data System (ADS)

Alencar Filho, Edilson B.; Santos, Aline A.; Oliveira, Boaz G.

2017-04-01

The proposal of this work includes the use of quantum chemical methods and cheminformatics strategies in order to understand the structural profile and reactivity of α-nucleophiles compounds such as oximes, amidoximes and hydroxamic acids, related to hydrolysis rate of organophosphates. Theoretical conformational study of 41 compounds were carried out through the PM3 semiempirical Hamiltonian, followed by the geometry optimization at the B3LYP/6-31+G(d,p) level of theory, complemented by Polarized Continuum Model (PCM) to simulate the aqueous environment. In line with the experimental hypothesis about hydrolytic power, the strength of the Intramolecular Hydrogen Bonds (IHBs) at light of the Bader's Quantum Theory of Atoms in Molecules (QTAIM) is related to the preferential conformations of α-nucleophiles. A set of E-Dragon descriptors (1,666) were submitted to a variable selection through Ordered Predictor Selection (OPS) algorithm. Five descriptors, including atomic charges obtained from the Natural Bond Orbitals (NBO) protocol jointly with a fragment index associated to the presence/absence of IHBs, provided a Quantitative Structure-Property Relationship (QSPR) model via Multiple Linear Regression (MLR). This model showed good validation parameters (R2 = 0.80, Qloo2 = 0.67 and Qext2 = 0.81) and allowed the identification of significant physicochemical features on the molecular scaffold in order to design compounds potentially more active against organophosphorus poisoning.

Influence of the physicochemical and aromatic properties on the chemical reactivity and its relation with carcinogenic and anticoagulant effect of 17β-aminoestrogens

NASA Astrophysics Data System (ADS)

Soriano-Correa, Catalina; Raya, Angélica; Barrientos-Salcedo, Carolina; Esquivel, Rodolfo O.

2014-06-01

Activity of steroid hormones is dependent upon a number of factors, as solubility, transport and metabolism. The functional differences caused by structural modifications could exert an influence on the chemical reactivity and biological effect. The goal of this work is to study the influence of the physicochemical and aromatic properties on the chemical reactivity and its relation with the carcinogenic risk that can associate with the anticoagulant effect of 17β-aminoestrogens using quantum-chemical descriptors at the DFT-B3LYP, BH&HLYP and M06-2X levels. The relative acidity of (H1) of the hydroxyl group increases with electron-withdrawing groups. Electron-donor groups favor the basicity. The steric hindrance of the substituents decreases the aromatic character and consequently diminution the carcinogenic effect. Density descriptors: hardness, electrophilic index, atomic charges, molecular orbitals, electrostatic potential and their geometric parameters permit analyses of the chemical reactivity and physicochemical features and to identify some reactive sites of 17β-aminoestrogens.

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

Use of statistical and neural net approaches in predicting toxicity of chemicals.

PubMed

Basak, S C; Grunwald, G D; Gute, B D; Balasubramanian, K; Opitz, D

2000-01-01

Hierarchical quantitative structure-activity relationships (H-QSAR) have been developed as a new approach in constructing models for estimating physicochemical, biomedicinal, and toxicological properties of interest. This approach uses increasingly more complex molecular descriptors in a graduated approach to model building. In this study, statistical and neural network methods have been applied to the development of H-QSAR models for estimating the acute aquatic toxicity (LC50) of 69 benzene derivatives to Pimephales promelas (fathead minnow). Topostructural, topochemical, geometrical, and quantum chemical indices were used as the four levels of the hierarchical method. It is clear from both the statistical and neural network models that topostructural indices alone cannot adequately model this set of congeneric chemicals. Not surprisingly, topochemical indices greatly increase the predictive power of both statistical and neural network models. Quantum chemical indices also add significantly to the modeling of this set of acute aquatic toxicity data.

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.

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

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

Singh, Kunwar P., E-mail: kpsingh_52@yahoo.com; Gupta, Shikha

Ensemble learning approach based decision treeboost (DTB) and decision tree forest (DTF) models are introduced in order to establish quantitative structure–toxicity relationship (QSTR) for the prediction of toxicity of 1450 diverse chemicals. Eight non-quantum mechanical molecular descriptors were derived. Structural diversity of the chemicals was evaluated using Tanimoto similarity index. Stochastic gradient boosting and bagging algorithms supplemented DTB and DTF models were constructed for classification and function optimization problems using the toxicity end-point in T. pyriformis. Special attention was drawn to prediction ability and robustness of the models, investigated both in external and 10-fold cross validation processes. In complete data,more » optimal DTB and DTF models rendered accuracies of 98.90%, 98.83% in two-category and 98.14%, 98.14% in four-category toxicity classifications. Both the models further yielded classification accuracies of 100% in external toxicity data of T. pyriformis. The constructed regression models (DTB and DTF) using five descriptors yielded correlation coefficients (R{sup 2}) of 0.945, 0.944 between the measured and predicted toxicities with mean squared errors (MSEs) of 0.059, and 0.064 in complete T. pyriformis data. The T. pyriformis regression models (DTB and DTF) applied to the external toxicity data sets yielded R{sup 2} and MSE values of 0.637, 0.655; 0.534, 0.507 (marine bacteria) and 0.741, 0.691; 0.155, 0.173 (algae). The results suggest for wide applicability of the inter-species models in predicting toxicity of new chemicals for regulatory purposes. These approaches provide useful strategy and robust tools in the screening of ecotoxicological risk or environmental hazard potential of chemicals. - Graphical abstract: Importance of input variables in DTB and DTF classification models for (a) two-category, and (b) four-category toxicity intervals in T. pyriformis data. Generalization and predictive abilities of the constructed (c) DTB and (d) DTF regression models to predict the T. pyriformis toxicity of diverse chemicals. - Highlights: • Ensemble learning (EL) based models constructed for toxicity prediction of chemicals • Predictive models used a few simple non-quantum mechanical molecular descriptors. • EL-based DTB/DTF models successfully discriminated toxic and non-toxic chemicals. • DTB/DTF regression models precisely predicted toxicity of chemicals in multi-species. • Proposed EL based models can be used as tool to predict toxicity of new chemicals.« 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.

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.

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.

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.

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.

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

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.

Computational Studies of Free Radical-Scavenging Properties of Phenolic Compounds

PubMed Central

Alov, Petko; Tsakovska, Ivanka; Pajeva, Ilza

2015-01-01

For more than half a century free radical-induced alterations at cellular and organ levels have been investigated as a probable underlying mechanism of a number of adverse health conditions. Consequently, significant research efforts have been spent for discovering more effective and potent antioxidants / free radical scavengers for treatment of these adverse conditions. Being by far the most used antioxidants among natural and synthetic compounds, mono- and polyphenols have been the focus of both experimental and computational research on mechanisms of free radical scavenging. Quantum chemical studies have provided a significant amount of data on mechanisms of reactions between phenolic compounds and free radicals outlining a number of properties with a key role for the radical scavenging activity and capacity of phenolics. The obtained quantum chemical parameters together with other molecular descriptors have been used in quantitative structure-activity relationship (QSAR) analyses for the design of new more effective phenolic antioxidants and for identification of the most useful natural antioxidant phenolics. This review aims at presenting the state of the art in quantum chemical and QSAR studies of phenolic antioxidants and at analysing the trends observed in the field in the last decade. PMID:25547098

Computational studies of free radical-scavenging properties of phenolic compounds.

PubMed

Alov, Petko; Tsakovska, Ivanka; Pajeva, Ilza

2015-01-01

For more than half a century free radical-induced alterations at cellular and organ levels have been investigated as a probable underlying mechanism of a number of adverse health conditions. Consequently, significant research efforts have been spent for discovering more effective and potent antioxidants / free radical scavengers for treatment of these adverse conditions. Being by far the most used antioxidants among natural and synthetic compounds, mono- and polyphenols have been the focus of both experimental and computational research on mechanisms of free radical scavenging. Quantum chemical studies have provided a significant amount of data on mechanisms of reactions between phenolic compounds and free radicals outlining a number of properties with a key role for the radical scavenging activity and capacity of phenolics. The obtained quantum chemical parameters together with other molecular descriptors have been used in quantitative structure-activity relationship (QSAR) analyses for the design of new more effective phenolic antioxidants and for identification of the most useful natural antioxidant phenolics. This review aims at presenting the state of the art in quantum chemical and QSAR studies of phenolic antioxidants and at analysing the trends observed in the field in the last decade.

A DFT approach to discriminate the antagonist and partial agonist activity of ligands binding to the NMDA receptor

NASA Astrophysics Data System (ADS)

Haslak, Zeynep Pinar; Bozkurt, Esra; Dutagaci, Bercem; De Proft, Frank; Aviyente, Viktorya; De Vleeschouwer, Freija

2018-02-01

The activation of N-methyl-D-aspartate receptors is found to be intimately associated with neurodegenerative diseases which make them promising therapeutic targets. Despite the significantly increasing multidisciplinary interests centred on this ionotropic channel, design of new ligands with intended functional activity remains a great challenge. In this article, a computational study based on density functional theory is presented to understand the structural factors of ligands determining their function as antagonists and partial agonists. With this aim, the GluN1 subunit is chosen as being one of the essential components in the activation mechanism, and quantum chemical calculations are implemented for 30 antagonists and 30 partial agonists known to bind to this subunit with different binding affinities. Several quantum chemical descriptors are investigated which might unlock the difference between antagonists and partial agonists.

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.

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.

Electronic structure descriptor for the discovery of narrow-band red-emitting phosphors

DOE PAGES

Wang, Zhenbin; Chu, Iek -Heng; Zhou, Fei; ...

2016-05-09

Narrow-band red-emitting phosphors are a critical component of phosphor-converted light-emitting diodes for highly efficient illumination-grade lighting. In this work, we report the discovery of a quantitative descriptor for narrow-band Eu 2+-activated emission identified through a comparison of the electronic structures of known narrow-band and broad-band phosphors. We find that a narrow emission bandwidth is characterized by a large splitting of more than 0.1 eV between the two highest Eu 2+ 4 f 7 bands. By incorporating this descriptor in a high-throughput first-principles screening of 2259 nitride compounds, we identify five promising new nitride hosts for Eu 2+-activated red-emitting phosphors thatmore » are predicted to exhibit good chemical stability, thermal quenching resistance, and quantum efficiency, as well as narrow-band emission. Lastly, our findings provide important insights into the emission characteristics of rare-earth activators in phosphor hosts and a general strategy to the discovery of phosphors with a desired emission peak and bandwidth.« less

Electronic structure descriptor for the discovery of narrow-band red-emitting phosphors

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

Wang, Zhenbin; Chu, Iek -Heng; Zhou, Fei

Narrow-band red-emitting phosphors are a critical component of phosphor-converted light-emitting diodes for highly efficient illumination-grade lighting. In this work, we report the discovery of a quantitative descriptor for narrow-band Eu 2+-activated emission identified through a comparison of the electronic structures of known narrow-band and broad-band phosphors. We find that a narrow emission bandwidth is characterized by a large splitting of more than 0.1 eV between the two highest Eu 2+ 4 f 7 bands. By incorporating this descriptor in a high-throughput first-principles screening of 2259 nitride compounds, we identify five promising new nitride hosts for Eu 2+-activated red-emitting phosphors thatmore » are predicted to exhibit good chemical stability, thermal quenching resistance, and quantum efficiency, as well as narrow-band emission. Lastly, our findings provide important insights into the emission characteristics of rare-earth activators in phosphor hosts and a general strategy to the discovery of phosphors with a desired emission peak and bandwidth.« less

[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.

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.

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.

Predicting carcinogenicity of diverse chemicals using probabilistic neural network modeling approaches

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

Singh, Kunwar P., E-mail: kpsingh_52@yahoo.com; Environmental Chemistry Division, CSIR-Indian Institute of Toxicology Research, Post Box 80, Mahatma Gandhi Marg, Lucknow 226 001; Gupta, Shikha

Robust global models capable of discriminating positive and non-positive carcinogens; and predicting carcinogenic potency of chemicals in rodents were developed. The dataset of 834 structurally diverse chemicals extracted from Carcinogenic Potency Database (CPDB) was used which contained 466 positive and 368 non-positive carcinogens. Twelve non-quantum mechanical molecular descriptors were derived. Structural diversity of the chemicals and nonlinearity in the data were evaluated using Tanimoto similarity index and Brock–Dechert–Scheinkman statistics. Probabilistic neural network (PNN) and generalized regression neural network (GRNN) models were constructed for classification and function optimization problems using the carcinogenicity end point in rat. Validation of the models wasmore » performed using the internal and external procedures employing a wide series of statistical checks. PNN constructed using five descriptors rendered classification accuracy of 92.09% in complete rat data. The PNN model rendered classification accuracies of 91.77%, 80.70% and 92.08% in mouse, hamster and pesticide data, respectively. The GRNN constructed with nine descriptors yielded correlation coefficient of 0.896 between the measured and predicted carcinogenic potency with mean squared error (MSE) of 0.44 in complete rat data. The rat carcinogenicity model (GRNN) applied to the mouse and hamster data yielded correlation coefficient and MSE of 0.758, 0.71 and 0.760, 0.46, respectively. The results suggest for wide applicability of the inter-species models in predicting carcinogenic potency of chemicals. Both the PNN and GRNN (inter-species) models constructed here can be useful tools in predicting the carcinogenicity of new chemicals for regulatory purposes. - Graphical abstract: Figure (a) shows classification accuracies (positive and non-positive carcinogens) in rat, mouse, hamster, and pesticide data yielded by optimal PNN model. Figure (b) shows generalization and predictive abilities of the interspecies GRNN model to predict the carcinogenic potency of diverse chemicals. - Highlights: • Global robust models constructed for carcinogenicity prediction of diverse chemicals. • Tanimoto/BDS test revealed structural diversity of chemicals and nonlinearity in data. • PNN/GRNN successfully predicted carcinogenicity/carcinogenic potency of chemicals. • Developed interspecies PNN/GRNN models for carcinogenicity prediction. • Proposed models can be used as tool to predict carcinogenicity of new chemicals.« less

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.

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.

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

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.

Quantum-mechanical parameters for the risk assessment of multi-walled carbon-nanotubes: A study using adsorption of probe compounds and its application to biomolecules.

PubMed

Chayawan; Vikas

2016-11-01

This work forwards new insights into the risk-assessment of multi-walled carbon-nanotubes (MWCNTs) while analysing the role of quantum-mechanical interactions between the electrons in the adsorption of probe compounds and biomolecules by MWCNTs. For this, the quantitative models are developed using quantum-chemical descriptors and their electron-correlation contribution. The major quantum-chemical factors contributing to the adsorption are found to be mean polarizability, electron-correlation energy, and electron-correlation contribution to the absolute electronegativity and LUMO energy. The proposed models, based on only three quantum-chemical factors, are found to be even more robust and predictive than the previously known five or four factors based linear free-energy and solvation-energy relationships. The proposed models are employed to predict the adsorption of biomolecules including steroid hormones and DNA bases. The steroid hormones are predicted to be strongly adsorbed by the MWCNTs, with the order: hydrocortisone > aldosterone > progesterone > ethinyl-oestradiol > testosterone > oestradiol, whereas the DNA bases are found to be relatively less adsorbed but follow the order as: guanine > adenine > thymine > cytosine > uracil. Besides these, the developed electron-correlation based models predict several insecticides, pesticides, herbicides, fungicides, plasticizers and antimicrobial agents in cosmetics, to be strongly adsorbed by the carbon-nanotubes. The present study proposes that the instantaneous inter-electronic interactions may be quite significant in various physico-chemical processes involving MWCNTs, and can be used as a reliable predictor for their risk assessment. Copyright © 2016 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.

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

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.

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.

Finding Chemical Structures Corresponding to a Set of Coordinates in Chemical Descriptor Space.

PubMed

Miyao, Tomoyuki; Funatsu, Kimito

2017-08-01

When chemical structures are searched based on descriptor values, or descriptors are interpreted based on values, it is important that corresponding chemical structures actually exist. In order to consider the existence of chemical structures located in a specific region in the chemical space, we propose to search them inside training data domains (TDDs), which are dense areas of a training dataset in the chemical space. We investigated TDDs' features using diverse and local datasets, assuming that GDB11 is the chemical universe. These two analyses showed that considering TDDs gives higher chance of finding chemical structures than a random search-based method, and that novel chemical structures actually exist inside TDDs. In addition to those findings, we tested the hypothesis that chemical structures were distributed on the limited areas of chemical space. This hypothesis was confirmed by the fact that distances among chemical structures in several descriptor spaces were much shorter than those among randomly generated coordinates in the training data range. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Predicting Drug-induced Hepatotoxicity Using QSAR and Toxicogenomics Approaches

PubMed Central

Low, Yen; Uehara, Takeki; Minowa, Yohsuke; Yamada, Hiroshi; Ohno, Yasuo; Urushidani, Tetsuro; Sedykh, Alexander; Muratov, Eugene; Fourches, Denis; Zhu, Hao; Rusyn, Ivan; Tropsha, Alexander

2014-01-01

Quantitative Structure-Activity Relationship (QSAR) modeling and toxicogenomics are used independently as predictive tools in toxicology. In this study, we evaluated the power of several statistical models for predicting drug hepatotoxicity in rats using different descriptors of drug molecules, namely their chemical descriptors and toxicogenomic profiles. The records were taken from the Toxicogenomics Project rat liver microarray database containing information on 127 drugs (http://toxico.nibio.go.jp/datalist.html). The model endpoint was hepatotoxicity in the rat following 28 days of exposure, established by liver histopathology and serum chemistry. First, we developed multiple conventional QSAR classification models using a comprehensive set of chemical descriptors and several classification methods (k nearest neighbor, support vector machines, random forests, and distance weighted discrimination). With chemical descriptors alone, external predictivity (Correct Classification Rate, CCR) from 5-fold external cross-validation was 61%. Next, the same classification methods were employed to build models using only toxicogenomic data (24h after a single exposure) treated as biological descriptors. The optimized models used only 85 selected toxicogenomic descriptors and had CCR as high as 76%. Finally, hybrid models combining both chemical descriptors and transcripts were developed; their CCRs were between 68 and 77%. Although the accuracy of hybrid models did not exceed that of the models based on toxicogenomic data alone, the use of both chemical and biological descriptors enriched the interpretation of the models. In addition to finding 85 transcripts that were predictive and highly relevant to the mechanisms of drug-induced liver injury, chemical structural alerts for hepatotoxicity were also identified. These results suggest that concurrent exploration of the chemical features and acute treatment-induced changes in transcript levels will both enrich the mechanistic understanding of sub-chronic liver injury and afford models capable of accurate prediction of hepatotoxicity from chemical structure and short-term assay results. PMID:21699217

Development of bovine serum albumin-water partition coefficients predictive models for ionogenic organic chemicals based on chemical form adjusted descriptors.

PubMed

Ding, Feng; Yang, Xianhai; Chen, Guosong; Liu, Jining; Shi, Lili; Chen, Jingwen

2017-10-01

The partition coefficients between bovine serum albumin (BSA) and water (K BSA/w ) for ionogenic organic chemicals (IOCs) were different greatly from those of neutral organic chemicals (NOCs). For NOCs, several excellent models were developed to predict their logK BSA/w . However, it was found that the conventional descriptors are inappropriate for modeling logK BSA/w of IOCs. Thus, alternative approaches are urgently needed to develop predictive models for K BSA/w of IOCs. In this study, molecular descriptors that can be used to characterize the ionization effects (e.g. chemical form adjusted descriptors) were calculated and used to develop predictive models for logK BSA/w of IOCs. The models developed had high goodness-of-fit, robustness, and predictive ability. The predictor variables selected to construct the models included the chemical form adjusted averages of the negative potentials on the molecular surface (V s-adj - ), the chemical form adjusted molecular dipole moment (dipolemoment adj ), the logarithm of the n-octanol/water distribution coefficient (logD). As these molecular descriptors can be calculated from their molecular structures directly, the developed model can be easily used to fill the logK BSA/w data gap for other IOCs within the applicability domain. Furthermore, the chemical form adjusted descriptors calculated in this study also could be used to construct predictive models on other endpoints of IOCs. Copyright © 2017 Elsevier Inc. All rights reserved.

Spectroscopic and chemical reactivity analysis of D-Myo-Inositol using quantum chemical approach and its experimental verification

NASA Astrophysics Data System (ADS)

Mishra, Devendra P.; Srivastava, Anchal; Shukla, R. K.

2017-07-01

This paper describes the spectroscopic (^1H and ^{13}C NMR, FT-IR and UV-Visible), chemical, nonlinear optical and thermodynamic properties of D-Myo-Inositol using quantum chemical technique and its experimental verification. The structural parameters of the compound are determined from the optimized geometry by B3LYP method with 6 {-}311{+}{+}G(d,p) basis set. It was found that the optimized parameters thus obtained are almost in agreement with the experimental ones. A detailed interpretation of the infrared spectra of D-Myo-Inositol is also reported in the present work. After optimization, the proton and carbon NMR chemical shifts of the studied compound are calculated using GIAO and 6 {-}311{+}{+}G(d,p) basis set. The search of organic materials with improved charge transfer properties requires precise quantum chemical calculations of space-charge density distribution, state and transition dipole moments and HOMO-LUMO states. The nature of the transitions in the observed UV-Visible spectrum of the compound has been studied by the time-dependent density functional theory (TD-DFT). The global reactivity descriptors like chemical potential, electronegativity, hardness, softness and electrophilicity index, have been calculated using DFT. The thermodynamic calculation related to the title compound was also performed at B3LYP/ 6 {-}311{+}{+}G(d,p) level of theory. The standard statistical thermodynamic functions like heat capacity at constant pressure, entropy and enthalpy change were obtained from the theoretical harmonic frequencies of the optimized molecule. It is observed that the values of heat capacity, entropy and enthalpy increase with increase in temperature from 100 to 1000 K, which is attributed to the enhancement of molecular vibration with the increase in temperature.

Importance of asparagine on the conformational stability and chemical reactivity of selected anti-inflammatory peptides

NASA Astrophysics Data System (ADS)

Soriano-Correa, Catalina; Barrientos-Salcedo, Carolina; Campos-Fernández, Linda; Alvarado-Salazar, Andres; Esquivel, Rodolfo O.

2015-08-01

Inflammatory response events are initiated by a complex series of molecular reactions that generate chemical intermediaries. The structure and properties of peptides and proteins are determined by the charge distribution of their side chains, which play an essential role in its electronic structure and physicochemical properties, hence on its biological functionality. The aim of this study was to analyze the effect of changing one central amino acid, such as substituting asparagine for aspartic acid, from Cys-Asn-Ser in aqueous solution, by assessing the conformational stability, physicochemical properties, chemical reactivity and their relationship with anti-inflammatory activity; employing quantum-chemical descriptors at the M06-2X/6-311+G(d,p) level. Our results suggest that asparagine plays a more critical role than aspartic acid in the structural stability, physicochemical features, and chemical reactivity of these tripeptides. Substituent groups in the side chain cause significant changes on the conformational stability and chemical reactivity, and consequently on their anti-inflammatory activity.

Use of in Vitro HTS-Derived Concentration–Response Data as Biological Descriptors Improves the Accuracy of QSAR Models of in Vivo Toxicity

PubMed Central

Sedykh, Alexander; Zhu, Hao; Tang, Hao; Zhang, Liying; Richard, Ann; Rusyn, Ivan; Tropsha, Alexander

2011-01-01

Background Quantitative high-throughput screening (qHTS) assays are increasingly being used to inform chemical hazard identification. Hundreds of chemicals have been tested in dozens of cell lines across extensive concentration ranges by the National Toxicology Program in collaboration with the National Institutes of Health Chemical Genomics Center. Objectives Our goal was to test a hypothesis that dose–response data points of the qHTS assays can serve as biological descriptors of assayed chemicals and, when combined with conventional chemical descriptors, improve the accuracy of quantitative structure–activity relationship (QSAR) models applied to prediction of in vivo toxicity end points. Methods We obtained cell viability qHTS concentration–response data for 1,408 substances assayed in 13 cell lines from PubChem; for a subset of these compounds, rodent acute toxicity half-maximal lethal dose (LD50) data were also available. We used the k nearest neighbor classification and random forest QSAR methods to model LD50 data using chemical descriptors either alone (conventional models) or combined with biological descriptors derived from the concentration–response qHTS data (hybrid models). Critical to our approach was the use of a novel noise-filtering algorithm to treat qHTS data. Results Both the external classification accuracy and coverage (i.e., fraction of compounds in the external set that fall within the applicability domain) of the hybrid QSAR models were superior to conventional models. Conclusions Concentration–response qHTS data may serve as informative biological descriptors of molecules that, when combined with conventional chemical descriptors, may considerably improve the accuracy and utility of computational approaches for predicting in vivo animal toxicity end points. PMID:20980217

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.

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.

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.

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.

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.

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.

Theoretical insights on flavanones as antioxidants and UV filters: A TDDFT and NLMO study.

PubMed

Ajmala Shireen, P; Abdul Mujeeb, V M; Muraleedharan, K

2017-05-01

UV radiations can cause several irritations to the skin like sunburn, photo aging and even skin cancer. Sunscreens are widely used to protect the skin against these harmful radiations. One of the ingredients present in these sunscreens are organic molecules capable of absorbing these harmful radiations. Recently, the search is on for antioxidant molecules which can act as UV filters as they can facilitate photo protection. In this study, a computational investigation based on density functional theory (DFT) is attempted on flavanones namely pinocembrin, pinostrobin and alpinetin found in Boesenbergia pandurata. Several quantum chemical descriptors are computed to understand the antioxidant potentiality of these molecules. Quantum chemical descriptors of these flavanone molecules are found to be comparable to that of well-known anti-oxidant quercetin. UV response of these molecules are studied using time dependent density functional theory (TD-DFT) formalism and by means of natural bond orbital (NBO) theory. It could be seen that these molecules exhibit a broad absorption in the UV region 270-390nm. This falls exactly in the region of harmful UVB and UVA radiation. Thus, these molecules have the potential to absorb the harmful UV radiation. From NLMO cluster studies, the orbital contribution to absorption is explained. In flavanones, unlike other classes of flavonoids, there is a discontinuity in the electron conjugation due to the absence of C2C3 double bond. This might be the key structural feature that leads to the absorption of these molecules to be centered around the UV region. These molecules can thus be treated as promising candidates for antioxidant UV filters in sunscreens. Copyright © 2017 Elsevier B.V. All rights reserved.

Structure-activity relations of 2-(methylthio)benzimidazole by FTIR, FT-Raman, NMR, DFT and conceptual DFT methods.

PubMed

Arjunan, V; Raj, Arushma; Ravindran, P; Mohan, S

2014-01-24

The vibrational fundamental modes of 2-(methylthio)benzimidazole (2MTBI) have been analysed by combining FTIR, FT-Raman and quantum chemical calculations. The structural parameters of the compound are determined from the optimised geometry by B3LYP with 6-31G(∗∗), 6-311++G(∗∗) and cc-pVTZ basis sets and giving energies, harmonic vibrational frequencies, depolarisation ratios, IR intensities and Raman activities. (1)H and (13)C NMR spectra have been analysed and (1)H and (13)C nuclear magnetic resonance chemical shifts are calculated using the gauge independent atomic orbital (GIAO) method. The structure-activity relationship of the compound is also investigated by conceptual DFT methods. The chemical reactivity and site selectivity of the molecule has been determined with the help of global and local reactivity descriptors. Copyright © 2013 Elsevier B.V. All rights reserved.

Local chemical potential, local hardness, and dual descriptors in temperature dependent chemical reactivity theory.

PubMed

Franco-Pérez, Marco; Ayers, Paul W; Gázquez, José L; Vela, Alberto

2017-05-31

In this work we establish a new temperature dependent procedure within the grand canonical ensemble, to avoid the Dirac delta function exhibited by some of the second order chemical reactivity descriptors based on density functional theory, at a temperature of 0 K. Through the definition of a local chemical potential designed to integrate to the global temperature dependent electronic chemical potential, the local chemical hardness is expressed in terms of the derivative of this local chemical potential with respect to the average number of electrons. For the three-ground-states ensemble model, this local hardness contains a term that is equal to the one intuitively proposed by Meneses, Tiznado, Contreras and Fuentealba, which integrates to the global hardness given by the difference in the first ionization potential, I, and the electron affinity, A, at any temperature. However, in the present approach one finds an additional temperature-dependent term that introduces changes at the local level and integrates to zero. Additionally, a τ-hard dual descriptor and a τ-soft dual descriptor given in terms of the product of the global hardness and the global softness multiplied by the dual descriptor, respectively, are derived. Since all these reactivity indices are given by expressions composed of terms that correspond to products of the global properties multiplied by the electrophilic or nucleophilic Fukui functions, they may be useful for studying and comparing equivalent sites in different chemical environments.

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.

A novel model to predict gas-phase hydroxyl radical oxidation kinetics of polychlorinated compounds.

PubMed

Luo, Shuang; Wei, Zongsu; Spinney, Richard; Yang, Zhihui; Chai, Liyuan; Xiao, Ruiyang

2017-04-01

In this study, a novel model based on aromatic meta-substituent grouping was presented to predict the second-order rate constants (k) for OH oxidation of PCBs in gas-phase. Since the oxidation kinetics are dependent on the chlorination degree and position, we hypothesized that it may be more accurate for k value prediction if we group PCB congeners based on substitution positions (i.e., ortho (o), meta (m), and para (p)). To test this hypothesis, we examined the correlation of polarizability (α), a quantum chemical based descriptor for k values, with an empirical Hammett constant (σ + ) on each substitution position. Our result shows that α is highly linearly correlated to ∑σ o,m,p + based on aromatic meta-substituents leading to the grouping based predictive model. With the new model, the calculated k values exhibited an excellent agreement with experimental measurements, and greater predictive power than the quantum chemical based quantitative structure activity relationship (QSAR) model. Further, the relationship of α and ∑σ o,m,p + for PCDDs congeners, together with highest occupied molecular orbital (HOMO) distribution, were used to validate the aromatic meta-substituent grouping method. This newly developed model features a combination of good predictability of quantum chemical based QSAR model and simplicity of Hammett relationship, showing a great potential for fast and computational tractable prediction of k values for gas-phase OH oxidation of polychlorinated compounds. Copyright © 2017 Elsevier Ltd. All rights reserved.

Synthesis, vibrational, NMR, quantum chemical and structure-activity relation studies of 2-hydroxy-4-methoxyacetophenone.

PubMed

Arjunan, V; Devi, L; Subbalakshmi, R; Rani, T; Mohan, S

2014-09-15

The stable geometry of 2-hydroxy-4-methoxyacetophenone is optimised by DFT/B3LYP method with 6-311++G(∗∗) and cc-pVTZ basis sets. The structural parameters, thermodynamic properties and vibrational frequencies of the optimised geometry have been determined. The effects of substituents (hydroxyl, methoxy and acetyl groups) on the benzene ring vibrational frequencies are analysed. The vibrational frequencies of the fundamental modes of 2-hydroxy-4-methoxyacetophenone have been precisely assigned and analysed and the theoretical results are compared with the experimental vibrations. 1H and 13C NMR isotropic chemical shifts are calculated and assignments made are compared with the experimental values. The energies of important MO's, the total electron density and electrostatic potential of the compound are determined. Various reactivity and selectivity descriptors such as chemical hardness, chemical potential, softness, electrophilicity, nucleophilicity and the appropriate local quantities are calculated. Copyright © 2014 Elsevier B.V. All rights reserved.

Molecular Descriptors

NASA Astrophysics Data System (ADS)

Consonni, Viviana; Todeschini, Roberto

In the last decades, several scientific researches have been focused on studying how to encompass and convert - by a theoretical pathway - the information encoded in the molecular structure into one or more numbers used to establish quantitative relationships between structures and properties, biological activities, or other experimental properties. Molecular descriptors are formally mathematical representations of a molecule obtained by a well-specified algorithm applied to a defined molecular representation or a well-specified experimental procedure. They play a fundamental role in chemistry, pharmaceutical sciences, environmental protection policy, toxicology, ecotoxicology, health research, and quality control. Evidence of the interest of the scientific community in the molecular descriptors is provided by the huge number of descriptors proposed up today: more than 5000 descriptors derived from different theories and approaches are defined in the literature and most of them can be calculated by means of dedicated software applications. Molecular descriptors are of outstanding importance in the research fields of quantitative structure-activity relationships (QSARs) and quantitative structure-property relationships (QSPRs), where they are the independent chemical information used to predict the properties of interest. Along with the definition of appropriate molecular descriptors, the molecular structure representation and the mathematical tools for deriving and assessing models are other fundamental components of the QSAR/QSPR approach. The remarkable progress during the last few years in chemometrics and chemoinformatics has led to new strategies for finding mathematical meaningful relationships between the molecular structure and biological activities, physico-chemical, toxicological, and environmental properties of chemicals. Different approaches for deriving molecular descriptors here reviewed and some of the most relevant descriptors are presented in detail with numerical examples.

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.

Conformational analysis, spectroscopic, structure-activity relations and quantum chemical simulation studies of 4-(trifluoromethyl)benzylamine

NASA Astrophysics Data System (ADS)

Arjunan, V.; Devi, L.; Mohan, S.

2018-05-01

The FT-IR and FT-Raman spectra of 4-trifluoromethylbenzylamine (TFMBA) have been recorded in the range 4000-450 and 4000-100 cm-1 respectively. The conformational analysis of the compound has been carried out to attain stable geometry of the compound. The complete vibrational assignment and analysis of the fundamental modes of the compound are carried out using the experimental FTIR and FT-Raman data and quantum chemical studies. The experimental vibrational frequencies are compared with the wavenumbers obtained theoretically from the B3LYP gradient calculations employing the standard high level 6-311++G** and cc-pVTZ basis sets for the optimised geometry of the compound. The structural parameters, thermodynamic properties and vibrational frequencies of the normal modes obtained from the B3LYP methods are in good agreement with the experimental data. The 1H (400 MHz; CDCl3) and 13C (100 MHz; CDCl3) nuclear magnetic resonance (NMR) spectra were also recorded. The electronic properties, highest occupied molecular orbital and lowest unoccupied molecular orbital energies are measured by DFT approach. The charges of the atoms by natural bond orbital (NBO) analysis are determined by B3LYP/cc-pVTZ method. The structure-chemical reactivity relations of the compound are determined through chemical potential, global hardness, global softness, electronegativity, electrophilicity and local reactivity descriptors by conceptual DFT methods.

Relations among several nuclear and electronic density functional reactivity indexes

NASA Astrophysics Data System (ADS)

Torrent-Sucarrat, Miquel; Luis, Josep M.; Duran, Miquel; Toro-Labbé, Alejandro; Solà, Miquel

2003-11-01

An expansion of the energy functional in terms of the total number of electrons and the normal coordinates within the canonical ensemble is presented. A comparison of this expansion with the expansion of the energy in terms of the total number of electrons and the external potential leads to new relations among common density functional reactivity descriptors. The formulas obtained provide explicit links between important quantities related to the chemical reactivity of a system. In particular, the relation between the nuclear and the electronic Fukui functions is recovered. The connection between the derivatives of the electronic energy and the nuclear repulsion energy with respect to the external potential offers a proof for the "Quantum Chemical le Chatelier Principle." Finally, the nuclear linear response function is defined and the relation of this function with the electronic linear response function is given.

RS-predictor: a new tool for predicting sites of cytochrome P450-mediated metabolism applied to CYP 3A4.

PubMed

Zaretzki, Jed; Bergeron, Charles; Rydberg, Patrik; Huang, Tao-wei; Bennett, Kristin P; Breneman, Curt M

2011-07-25

This article describes RegioSelectivity-Predictor (RS-Predictor), a new in silico method for generating predictive models of P450-mediated metabolism for drug-like compounds. Within this method, potential sites of metabolism (SOMs) are represented as "metabolophores": A concept that describes the hierarchical combination of topological and quantum chemical descriptors needed to represent the reactivity of potential metabolic reaction sites. RS-Predictor modeling involves the use of metabolophore descriptors together with multiple-instance ranking (MIRank) to generate an optimized descriptor weight vector that encodes regioselectivity trends across all cases in a training set. The resulting pathway-independent (O-dealkylation vs N-oxidation vs Csp(3) hydroxylation, etc.), isozyme-specific regioselectivity model may be used to predict potential metabolic liabilities. In the present work, cross-validated RS-Predictor models were generated for a set of 394 substrates of CYP 3A4 as a proof-of-principle for the method. Rank aggregation was then employed to merge independently generated predictions for each substrate into a single consensus prediction. The resulting consensus RS-Predictor models were shown to reliably identify at least one observed site of metabolism in the top two rank-positions on 78% of the substrates. Comparisons between RS-Predictor and previously described regioselectivity prediction methods reveal new insights into how in silico metabolite prediction methods should be compared.

Predicting the outcomes of organic reactions via machine learning: are current descriptors sufficient?

PubMed

Skoraczyński, G; Dittwald, P; Miasojedow, B; Szymkuć, S; Gajewska, E P; Grzybowski, B A; Gambin, A

2017-06-15

As machine learning/artificial intelligence algorithms are defeating chess masters and, most recently, GO champions, there is interest - and hope - that they will prove equally useful in assisting chemists in predicting outcomes of organic reactions. This paper demonstrates, however, that the applicability of machine learning to the problems of chemical reactivity over diverse types of chemistries remains limited - in particular, with the currently available chemical descriptors, fundamental mathematical theorems impose upper bounds on the accuracy with which raction yields and times can be predicted. Improving the performance of machine-learning methods calls for the development of fundamentally new chemical descriptors.

QSAR models for degradation of organic pollutants in ozonation process under acidic condition.

PubMed

Zhu, Huicen; Guo, Weimin; Shen, Zhemin; Tang, Qingli; Ji, Wenchao; Jia, Lijuan

2015-01-01

Although some researches about the degradation of organic pollutants have been carried out during recent years, reaction rate constants are available only for homologue compounds with similar structures or components. Therefore, it is of great significance to find a universal relationship between reaction rate and certain parameters of several diverse organic pollutants. In this study, removal ratio and kinetics of 33 kinds of organic substances were investigated by ozonation process, including azo dyes, heterocyclic compounds, ionic compounds and so on. Most quantum chemical parameters were conducted by using Gaussian 09 at the DFT B3LYP/6-311G level, including μ, q H(+), q(C)minq(C)max, ELUMO and EHOMO. Other descriptors, bond order (BO) as well as Fukui indices (f(+), f(-) and f(0)), were calculated by Material Studio 6.1 at Dmol(3)/GGA-BLYP/DNP(3.5) basis for each organic compound. The recommended model for predicting rate constants was lnk'=1.978-95.484f(0)x-3.350q(C)min+38.221f(+)x, which had the squared regression coefficient R(2)=0.763 and standard deviation SD=0.716. The results of t test and the Fisher test suggested that the model exhibited optimum stability. Also, the model was validated by internal and external validations. Recommended QSAR model showed that the highest f(0) value of main-chain carbons (f(0)x) is more closely related to lnk' than other quantum descriptors. Copyright © 2014 Elsevier Ltd. All rights reserved.

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.

Theories of quantum dissipation and nonlinear coupling bath descriptors

NASA Astrophysics Data System (ADS)

Xu, Rui-Xue; Liu, Yang; Zhang, Hou-Dao; Yan, YiJing

2018-03-01

The quest of an exact and nonperturbative treatment of quantum dissipation in nonlinear coupling environments remains in general an intractable task. In this work, we address the key issues toward the solutions to the lowest nonlinear environment, a harmonic bath coupled both linearly and quadratically with an arbitrary system. To determine the bath coupling descriptors, we propose a physical mapping scheme, together with the prescription reference invariance requirement. We then adopt a recently developed dissipaton equation of motion theory [R. X. Xu et al., Chin. J. Chem. Phys. 30, 395 (2017)], with the underlying statistical quasi-particle ("dissipaton") algebra being extended to the quadratic bath coupling. We report the numerical results on a two-level system dynamics and absorption and emission line shapes.

SVM prediction of ligand-binding sites in bacterial lipoproteins employing shape and physio-chemical descriptors.

PubMed

Kadam, Kiran; Prabhakar, Prashant; Jayaraman, V K

2012-11-01

Bacterial lipoproteins play critical roles in various physiological processes including the maintenance of pathogenicity and numbers of them are being considered as potential candidates for generating novel vaccines. In this work, we put forth an algorithm to identify and predict ligand-binding sites in bacterial lipoproteins. The method uses three types of pocket descriptors, namely fpocket descriptors, 3D Zernike descriptors and shell descriptors, and combines them with Support Vector Machine (SVM) method for the classification. The three types of descriptors represent shape-based properties of the pocket as well as its local physio-chemical features. All three types of descriptors, along with their hybrid combinations are evaluated with SVM and to improve classification performance, WEKA-InfoGain feature selection is applied. Results obtained in the study show that the classifier successfully differentiates between ligand-binding and non-binding pockets. For the combination of three types of descriptors, 10 fold cross-validation accuracy of 86.83% is obtained for training while the selected model achieved test Matthews Correlation Coefficient (MCC) of 0.534. Individually or in combination with new and existing methods, our model can be a very useful tool for the prediction of potential ligand-binding sites in bacterial lipoproteins.

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.

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.

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

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.

Toxicity prediction of PHDDs and phenols in the light of nucleic acid bases and DNA base pair interaction.

PubMed

Mondal Roy, Sutapa; Roy, Debesh R; Sahoo, Suban K

2015-11-01

The applicability of Density Functional Theory (DFT) based descriptors for the development of quantitative structure-toxicity relationships (QSTR) is assessed for two different series of toxic aromatic compounds, viz., polyhalogenated dibenzo-p-dioxins (PHDDs) and phenols (PHs). A series of 20 compounds each for PHDDs and PHs with their experimental toxicities (IC50 and IGC50) is chosen in the present study to develop DFT based efficient quantum chemical parameters (QCPs) for explaining the toxin potential of the considered compounds. A systematic analysis to find out the electron donation/acceptance nature of these selected compounds with the considered model biosystems, viz., nucleic acid (NA) bases and DNA base pairs, is performed to identify potential QCPs. Accordingly, PHDDs is found to be electron acceptors whereas phenols as donors, during their interaction with biosystems. Two parameter regression model is carried out comprising global charge transfer (ΔN), and local Fukui Function's for nucleophilic attack (fk(+)) for PHDDs and the same for electrophilic attack (fk(-)) in case of PHs. It is heartening to note that our chosen descriptors, viz, charge transfer (ΔN) and Fukui Function (fk(±)) plays a crucial role by explaining more than 90% of the observed toxic behavior (in terms of correlation-coefficient, R) of PHDDs and PHs. The developed QCPs, viz., ΔN and fk(±) can be added as the new descriptors in the QSTR parlance. Copyright © 2015 Elsevier Inc. All rights reserved.

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.

Systematically evaluating read-across prediction and ...

EPA Pesticide Factsheets

Read-across is a popular data gap filling technique within category and analogue approaches for regulatory purposes. Acceptance of read-across remains an ongoing challenge with several efforts underway for identifying and addressing uncertainties. Here we demonstrate an algorithmic, automated approach to evaluate the utility of using in vitro bioactivity data (“bioactivity descriptors”, from EPA’s ToxCast program) in conjunction with chemical descriptor information to derive local validity domains (specific sets of nearest neighbors) to facilitate read-across for a number of in vivo repeated dose toxicity study types. 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. The approach enabled a performance baseline for read-across predictions of specific study outcomes to be established. Bioactivity descriptors were often found to be more predictive of in vivo toxicity outcomes than chemical descriptors or a combination of both. The approach shows promise as part of a screening assessment in the absence of prior knowledge. Future work will investigate to what extent encoding expert knowledge leads to an improvement in read-across prediction. Read-across is a popular data gap filling technique within category and analogue approaches

Use of in Vitro HTS-Derived Concentration-Response Data as ...

EPA Pesticide Factsheets

Background: Quantitative high-throughput screening (qHTS) assays are increasingly being employed to inform chemical hazard identification. Hundreds of chemicals have been tested in dozens of cell lines across extensive concentration ranges by the National Toxicology Program in collaboration with the NIH Chemical Genomics Center. Objectives: To test a hypothesis that dose-response data points of the qHTS assays can serve as biological descriptors of assayed chemicals and, when combined with conventional chemical descriptors, may improve the accuracy of Quantitative Structure-Activity Relationship (QSAR) models applied to prediction of in vivo toxicity endpoints. Methods and Results: The cell viability qHTS concentration-response data for 1,408 substances assayed in 13 cell lines were obtained from PubChem; for a subset of these compounds rodent acute toxicity LD50 data were also available. The classification k Nearest Neighbor and Random Forest QSAR methods were employed for modeling LD50 data using either chemical descriptors alone (conventional models) or in combination with biological descriptors derived from the concentration-response qHTS data (hybrid models). Critical to our approach was the use of a novel noise-filtering algorithm to treat qHTS data. We show that both the external classification accuracy and coverage (i.e., fraction of compounds in the external set that fall within the applicability domain) of the hybrid QSAR models was superior to convent

Recent advances in the in silico modelling of UDP glucuronosyltransferase substrates.

PubMed

Sorich, Michael J; Smith, Paul A; Miners, John O; Mackenzie, Peter I; McKinnon, Ross A

2008-01-01

UDP glucurononosyltransferases (UGT) are a superfamily of enzymes that catalyse the conjugation of a range of structurally diverse drugs, environmental and endogenous chemicals with glucuronic acid. This process plays a significant role in the clearance and detoxification of many chemicals. Over the last decade the regulation and substrate profiles of UGT isoforms have been increasingly characterised. The resulting data has facilitated the prototyping of ligand based in silico models capable of predicting, and gaining insights into, binding affinity and the substrate- and regio- selectivity of glucuronidation by UGT isoforms. Pharmacophore modelling has produced particularly insightful models and quantitative structure-activity relationships based on machine learning algorithms result in accurate predictions. Simple structural chemical descriptors were found to capture much of the chemical information relevant to UGT metabolism. However, quantum chemical properties of molecules and the nucleophilic atoms in the molecule can enhance both the predictivity and chemical intuitiveness of structure-activity models. Chemical diversity analysis of known substrates has shown some bias towards chemicals with aromatic and aliphatic hydroxyl groups. Future progress in in silico development will depend on larger and more diverse high quality metabolic datasets. Furthermore, improved protein structure data on UGTs will enable the application of structural modelling techniques likely leading to greater insight into the binding and reactive processes of UGT catalysed glucuronidation.

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.

Synthesis and antibacterial activity of sulfonamides. SAR and DFT studies

NASA Astrophysics Data System (ADS)

Boufas, Wahida; Dupont, Nathalie; Berredjem, Malika; Berrezag, Kamel; Becheker, Imène; Berredjem, Hajira; Aouf, Nour-Eddine

2014-09-01

A series of substituted sulfonamide derivatives were synthesized from chlorosulfonyl isocyanate (CSI) in tree steps (carbamoylation, sulfamoylation and deprotection). Antibacterial activity in vitro of some newly formed compounds investigated against clinical strains Gram-positive and Gram-negative: Escherichia coli and Staphylococcus aureus applying the method of dilution and minimal inhibition concentration (MIC) methods. These compounds have significant bacteriostatic activity with totalities of bacterial strains used. DFT calculations with B3LYP/6-31G(d) level have been used to analyze the electronic and geometric characteristics deduced for the stable structure of three compounds presenting conjugation between a nitrogen atom N through its lone pair and an aromatic ring next to it. The principal quantum chemical descriptors have been correlated with the antibacterial activity.

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.

Some new reaction pathways for the formation of cytosine in interstellar space - A quantum chemical study

NASA Astrophysics Data System (ADS)

Gupta, V. P.; Tandon, Poonam; Mishra, Priti

2013-03-01

The detection of nucleic acid bases in carbonaceous meteorites suggests that their formation and survival is possible outside of the Earth. Small N-heterocycles, including pyrimidine, purines and nucleobases, have been extensively sought in the interstellar medium. It has been suggested theoretically that reactions between some interstellar molecules may lead to the formation of cytosine, uracil and thymine though these processes involve significantly high potential barriers. We attempted therefore to use quantum chemical techniques to explore if cytosine can possibly form in the interstellar space by radical-radical and radical-molecule interaction schemes, both in the gas phase and in the grains, through barrier-less or low barrier pathways. Results of DFT calculations for the formation of cytosine starting from some of the simple molecules and radicals detected in the interstellar space are being reported. Global and local descriptors such as molecular hardness, softness and electrophilicity, and condensed Fukui functions and local philicity indices were used to understand the mechanistic aspects of chemical reaction. The presence and nature of weak bonds in the molecules and transition states formed during the reaction process have been ascertained using Bader's quantum theory of atoms in molecules (QTAIMs). Two exothermic reaction pathways starting from propynylidyne (CCCH) and cyanoacetylene (HCCCN), respectively, have been identified. While the first reaction path is found to be totally exothermic, it involves a barrier of 12.5 kcal/mol in the gas phase against the lowest value of about 32 kcal/mol reported in the literature. The second path is both exothermic and barrier-less. The later has, therefore, a greater probability of occurrence in the cold interstellar clouds (10-50 K).

Exploring simple, transparent, interpretable and predictive QSAR models for classification and quantitative prediction of rat toxicity of ionic liquids using OECD recommended guidelines.

PubMed

Das, Rudra Narayan; Roy, Kunal; Popelier, Paul L A

2015-11-01

The present study explores the chemical attributes of diverse ionic liquids responsible for their cytotoxicity in a rat leukemia cell line (IPC-81) by developing predictive classification as well as regression-based mathematical models. Simple and interpretable descriptors derived from a two-dimensional representation of the chemical structures along with quantum topological molecular similarity indices have been used for model development, employing unambiguous modeling strategies that strictly obey the guidelines of the Organization for Economic Co-operation and Development (OECD) for quantitative structure-activity relationship (QSAR) analysis. The structure-toxicity relationships that emerged from both classification and regression-based models were in accordance with the findings of some previous studies. The models suggested that the cytotoxicity of ionic liquids is dependent on the cationic surfactant action, long alkyl side chains, cationic lipophilicity as well as aromaticity, the presence of a dialkylamino substituent at the 4-position of the pyridinium nucleus and a bulky anionic moiety. The models have been transparently presented in the form of equations, thus allowing their easy transferability in accordance with the OECD guidelines. The models have also been subjected to rigorous validation tests proving their predictive potential and can hence be used for designing novel and "greener" ionic liquids. The major strength of the present study lies in the use of a diverse and large dataset, use of simple reproducible descriptors and compliance with the OECD norms. Copyright © 2015 Elsevier Ltd. All rights reserved.

On the Development and Use of Large Chemical Similarity Networks, Informatics Best Practices and Novel Chemical Descriptors towards Materials Quantitative Structure Property Relationships

ERIC Educational Resources Information Center

Krein, Michael

2011-01-01

After decades of development and use in a variety of application areas, Quantitative Structure Property Relationships (QSPRs) and related descriptor-based statistical learning methods have achieved a level of infamy due to their misuse. The field is rife with past examples of overtrained models, overoptimistic performance assessment, and outright…

PyBioMed: a python library for various molecular representations of chemicals, proteins and DNAs and their interactions.

PubMed

Dong, Jie; Yao, Zhi-Jiang; Zhang, Lin; Luo, Feijun; Lin, Qinlu; Lu, Ai-Ping; Chen, Alex F; Cao, Dong-Sheng

2018-03-20

With the increasing development of biotechnology and informatics technology, publicly available data in chemistry and biology are undergoing explosive growth. Such wealthy information in these data needs to be extracted and transformed to useful knowledge by various data mining methods. Considering the amazing rate at which data are accumulated in chemistry and biology fields, new tools that process and interpret large and complex interaction data are increasingly important. So far, there are no suitable toolkits that can effectively link the chemical and biological space in view of molecular representation. To further explore these complex data, an integrated toolkit for various molecular representation is urgently needed which could be easily integrated with data mining algorithms to start a full data analysis pipeline. Herein, the python library PyBioMed is presented, which comprises functionalities for online download for various molecular objects by providing different IDs, the pretreatment of molecular structures, the computation of various molecular descriptors for chemicals, proteins, DNAs and their interactions. PyBioMed is a feature-rich and highly customized python library used for the characterization of various complex chemical and biological molecules and interaction samples. The current version of PyBioMed could calculate 775 chemical descriptors and 19 kinds of chemical fingerprints, 9920 protein descriptors based on protein sequences, more than 6000 DNA descriptors from nucleotide sequences, and interaction descriptors from pairwise samples using three different combining strategies. Several examples and five real-life applications were provided to clearly guide the users how to use PyBioMed as an integral part of data analysis projects. By using PyBioMed, users are able to start a full pipelining from getting molecular data, pretreating molecules, molecular representation to constructing machine learning models conveniently. PyBioMed provides various user-friendly and highly customized APIs to calculate various features of biological molecules and complex interaction samples conveniently, which aims at building integrated analysis pipelines from data acquisition, data checking, and descriptor calculation to modeling. PyBioMed is freely available at http://projects.scbdd.com/pybiomed.html .

A conceptual DFT study of the molecular properties of glycating carbonyl compounds.

PubMed

Frau, Juan; Glossman-Mitnik, Daniel

2017-01-01

Several glycating carbonyl compounds have been studied by resorting to the latest Minnesota family of density functional with the objective of determinating their molecular properties. In particular, the chemical reactivity descriptors that arise from conceptual density functional theory and chemical reactivity theory have been calculated through a [Formula: see text]SCF protocol. The validity of the KID (Koopmans' in DFT) procedure has been checked by comparing the reactivity descriptors obtained from the values of the HOMO and LUMO with those calculated through vertical energy values. The reactivity sites have been determined by means of the calculation of the Fukui function indices, the condensed dual descriptor [Formula: see text] and the electrophilic and nucleophilic Parr functions. The glycating power of the studied compounds have been compared with the same property for simple carbohydrates.Graphical abstractSeveral glycating carbonyl compounds have been studied by resorting to the latest Minnesota family of density functional with the objective of determinating their molecular properties, the chemical reactivity descriptors and the validity of the KID (Koopmans' in DFT) procedure.

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.

A Molecular Electron Density Theory Study of the Chemical Reactivity of Cis- and Trans-Resveratrol.

PubMed

Frau, Juan; Muñoz, Francisco; Glossman-Mitnik, Daniel

2016-12-01

The chemical reactivity of resveratrol isomers with the potential to play a role as inhibitors of the nonenzymatic glycation of amino acids and proteins, both acting as antioxidants and as chelating agents for metallic ions such as Cu, Al and Fe, have been studied by resorting to the latest family of Minnesota density functionals. The chemical reactivity descriptors have been calculated through Molecular Electron Density Theory encompassing Conceptual DFT. The active sites for nucleophilic and electrophilic attacks have been chosen by relating them to the Fukui function indices, the dual descriptor f ( 2 ) ( r ) and the electrophilic and nucleophilic Parr functions. The validity of "Koopmans' theorem in DFT" has been assessed by means of a comparison between the descriptors calculated through vertical energy values and those arising from the HOMO and LUMO values.

DFT simulation, quantum chemical electronic structure, spectroscopic and structure-activity investigations of 2-benzothiazole acetonitrile.

PubMed

Arjunan, V; Thillai Govindaraja, S; Jose, Sujin P; Mohan, S

2014-07-15

The Fourier transform infrared and FT-Raman spectra of 2-benzothiazole acetonitrile (BTAN) have been recorded in the range 4000-450 and 4000-100 cm(-1) respectively. The conformational analysis of the compound has been carried out to obtain the stable geometry of the compound. The complete vibrational assignment and analysis of the fundamental modes of the compound are carried out using the experimental FTIR and FT-Raman data and quantum chemical studies. The experimental vibrational frequencies are compared with the wavenumbers derived theoretically by B3LYP gradient calculations employing the standard 6-31G(**), high level 6-311++G(**) and cc-pVTZ basis sets. The structural parameters, thermodynamic properties and vibrational frequencies of the normal modes obtained from the B3LYP methods are in good agreement with the experimental data. The (1)H (400 MHz; CDCl3) and (13)C (100 MHz;CDCl3) nuclear magnetic resonance (NMR) spectra are also recorded. The electronic properties, the energies of the highest occupied and lowest unoccupied molecular orbitals are measured by DFT approach. The kinetic stability of the molecule has been determined from the frontier molecular orbital energy gap. The charges of the atoms and the structure-chemical reactivity relations of the compound are determined by its chemical potential, global hardness, global softness, electronegativity, electrophilicity and local reactivity descriptors by conceptual DFT methods. The non-linear optical properties of the compound have been discussed by measuring the polarisability and hyperpolarisability tensors. Copyright © 2014 Elsevier B.V. All rights reserved.

Conformational, structural, vibrational, electronic and quantum chemical investigations of cis-2-methoxycinnamic acid

NASA Astrophysics Data System (ADS)

Arjunan, V.; Anitha, R.; Marchewka, M. K.; Mohan, S.; Yang, Haifeng

2015-01-01

The Fourier transform infrared (FTIR) and FT-Raman spectra of cis-2-methoxycinnamic acid have been measured in the range 4000-400 and 4000-100 cm-1, respectively. Complete vibrational assignment and analysis of the fundamental modes of the compound were carried out using the observed FTIR and FT-Raman data. The geometry was optimised without any symmetry constrains using the DFT/B3LYP method utilising 6-311++G∗∗ and cc-pVTZ basis sets. The thermodynamic stability and chemical reactivity descriptors of the molecule have been determined. The exact environment of C and H of the molecule has been analysed by NMR spectroscopies through 1H and 13C NMR chemical shifts of the molecule. The energies of the frontier molecular orbitals have also been determined. Complete NBO analysis was also carried out to find out the intramolecular electronic interactions and their stabilisation energy. The vibrational frequencies which were determined experimentally are compared with those obtained theoretically from density functional theory (DFT) gradient calculations employing the B3LYP/6-311++G∗∗ and cc-pVTZ methods.

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.

Integration of QSAR and SAR methods for the mechanistic interpretation of predictive models for carcinogenicity

PubMed Central

Fjodorova, Natalja; Novič, Marjana

2012-01-01

The knowledge-based Toxtree expert system (SAR approach) was integrated with the statistically based counter propagation artificial neural network (CP ANN) model (QSAR approach) to contribute to a better mechanistic understanding of a carcinogenicity model for non-congeneric chemicals using Dragon descriptors and carcinogenic potency for rats as a response. The transparency of the CP ANN algorithm was demonstrated using intrinsic mapping technique specifically Kohonen maps. Chemical structures were represented by Dragon descriptors that express the structural and electronic features of molecules such as their shape and electronic surrounding related to reactivity of molecules. It was illustrated how the descriptors are correlated with particular structural alerts (SAs) for carcinogenicity with recognized mechanistic link to carcinogenic activity. Moreover, the Kohonen mapping technique enables one to examine the separation of carcinogens and non-carcinogens (for rats) within a family of chemicals with a particular SA for carcinogenicity. The mechanistic interpretation of models is important for the evaluation of safety of chemicals. PMID:24688639

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.

Multiple feature fusion via covariance matrix for visual tracking

NASA Astrophysics Data System (ADS)

Jin, Zefenfen; Hou, Zhiqiang; Yu, Wangsheng; Wang, Xin; Sun, Hui

2018-04-01

Aiming at the problem of complicated dynamic scenes in visual target tracking, a multi-feature fusion tracking algorithm based on covariance matrix is proposed to improve the robustness of the tracking algorithm. In the frame-work of quantum genetic algorithm, this paper uses the region covariance descriptor to fuse the color, edge and texture features. It also uses a fast covariance intersection algorithm to update the model. The low dimension of region covariance descriptor, the fast convergence speed and strong global optimization ability of quantum genetic algorithm, and the fast computation of fast covariance intersection algorithm are used to improve the computational efficiency of fusion, matching, and updating process, so that the algorithm achieves a fast and effective multi-feature fusion tracking. The experiments prove that the proposed algorithm can not only achieve fast and robust tracking but also effectively handle interference of occlusion, rotation, deformation, motion blur and so on.

A QSPR study on the solvent-induced frequency shifts of acetone and dimethyl sulfoxide in organic solvents.

PubMed

Ou, Yu Heng; Chang, Chia Ming; Chen, Ying Shao

2016-06-05

In this study, solvent-induced frequency shifts (SIFS) in the infrared spectrum of acetone and dimethyl sulfoxide in organic solvents were investigated by using four types of quantum-chemical reactivity descriptors. The results showed that the SIFS of acetone is mainly affected by the electron-acceptance chemical potential and the maximum nucleophilic condensed local softness of organic solvents, which represent the electron flow and the polarization between acetone and solvent molecules. On the other hand, the SIFS of dimethyl sulfoxide changes with the maximum positive charge of hydrogen atom and the inverse of apolar surface area of solvent molecules, showing that the electrostatic and hydrophilic interactions are main mechanisms between dimethyl sulfoxide and solvent molecules. The introduction of the four-element theory model-based quantitative structure-property relationship approach improved the assessing quality and provided a basis for interpreting the solute-solvent interactions. Copyright © 2016 Elsevier B.V. All rights reserved.

Predicting hepatotoxicity using ToxCast in vitro bioactivity and ...

EPA Pesticide Factsheets

Background: The U.S. EPA ToxCastTM program is screening thousands of environmental chemicals for bioactivity using hundreds of high-throughput in vitro assays to build predictive models of toxicity. We represented chemicals based on bioactivity and chemical structure descriptors then used supervised machine learning to predict their hepatotoxic effects.Results: A set of 677 chemicals were represented by 711 in vitro bioactivity descriptors (from ToxCast assays), 4,376 chemical structure descriptors (from QikProp, OpenBabel, PADEL, and PubChem), and three hepatotoxicity categories (from animal studies). Hepatotoxicants were defined by rat liver histopathology observed after chronic chemical testing and grouped into hypertrophy (161), injury (101) and proliferative lesions (99). Classifiers were built using six machine learning algorithms: linear discriminant analysis (LDA), Naïve Bayes (NB), support vector classification (SVM), classification and regression trees (CART), k-nearest neighbors (KNN) and an ensemble of classifiers (ENSMB). Classifiers of hepatotoxicity were built using chemical structure, ToxCast bioactivity, and a hybrid representation. Predictive performance was evaluated using 10-fold cross-validation testing and in-loop, filter-based, feature subset selection. Hybrid classifiers had the best balanced accuracy for predicting hypertrophy (0.78±0.08), injury (0.73±0.10) and proliferative lesions (0.72±0.09). Though chemical and bioactivity class

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.

Using novel descriptor accounting for ligand-receptor interactions to define and visually explore biologically relevant chemical space.

PubMed

Rabal, Obdulia; Oyarzabal, Julen

2012-05-25

The definition and pragmatic implementation of biologically relevant chemical space is critical in addressing navigation strategies in the overlapping regions where chemistry and therapeutically relevant targets reside and, therefore, also key to performing an efficient drug discovery project. Here, we describe the development and implementation of a simple and robust method for representing biologically relevant chemical space as a general reference according to current knowledge, independently of any reference space, and analyzing chemical structures accordingly. Underlying our method is the generation of a novel descriptor (LiRIf) that converts structural information into a one-dimensional string accounting for the plausible ligand-receptor interactions as well as for topological information. Capitalizing on ligand-receptor interactions as a descriptor enables the clustering, profiling, and comparison of libraries of compounds from a chemical biology and medicinal chemistry perspective. In addition, as a case study, R-groups analysis is performed to identify the most populated ligand-receptor interactions according to different target families (GPCR, kinases, etc.), as well as to evaluate the coverage of biologically relevant chemical space by structures annotated in different databases (ChEMBL, Glida, etc.).

Predicting in vivo effect levels for repeat-dose systemic toxicity using chemical, biological, kinetic and study covariates.

PubMed

Truong, Lisa; Ouedraogo, Gladys; Pham, LyLy; Clouzeau, Jacques; Loisel-Joubert, Sophie; Blanchet, Delphine; Noçairi, Hicham; Setzer, Woodrow; Judson, Richard; Grulke, Chris; Mansouri, Kamel; Martin, Matthew

2018-02-01

In an effort to address a major challenge in chemical safety assessment, alternative approaches for characterizing systemic effect levels, a predictive model was developed. Systemic effect levels were curated from ToxRefDB, HESS-DB and COSMOS-DB from numerous study types totaling 4379 in vivo studies for 1247 chemicals. Observed systemic effects in mammalian models are a complex function of chemical dynamics, kinetics, and inter- and intra-individual variability. To address this complex problem, systemic effect levels were modeled at the study-level by leveraging study covariates (e.g., study type, strain, administration route) in addition to multiple descriptor sets, including chemical (ToxPrint, PaDEL, and Physchem), biological (ToxCast), and kinetic descriptors. Using random forest modeling with cross-validation and external validation procedures, study-level covariates alone accounted for approximately 15% of the variance reducing the root mean squared error (RMSE) from 0.96 log 10 to 0.85 log 10  mg/kg/day, providing a baseline performance metric (lower expectation of model performance). A consensus model developed using a combination of study-level covariates, chemical, biological, and kinetic descriptors explained a total of 43% of the variance with an RMSE of 0.69 log 10  mg/kg/day. A benchmark model (upper expectation of model performance) was also developed with an RMSE of 0.5 log 10  mg/kg/day by incorporating study-level covariates and the mean effect level per chemical. To achieve a representative chemical-level prediction, the minimum study-level predicted and observed effect level per chemical were compared reducing the RMSE from 1.0 to 0.73 log 10  mg/kg/day, equivalent to 87% of predictions falling within an order-of-magnitude of the observed value. Although biological descriptors did not improve model performance, the final model was enriched for biological descriptors that indicated xenobiotic metabolism gene expression, oxidative stress, and cytotoxicity, demonstrating the importance of accounting for kinetics and non-specific bioactivity in predicting systemic effect levels. Herein, we generated an externally predictive model of systemic effect levels for use as a safety assessment tool and have generated forward predictions for over 30,000 chemicals.

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.

Comparison of the applicability domain of a quantitative structure-activity relationship for estrogenicity with a large chemical inventory.

PubMed

Netzeva, Tatiana I; Gallegos Saliner, Ana; Worth, Andrew P

2006-05-01

The aim of the present study was to illustrate that it is possible and relatively straightforward to compare the domain of applicability of a quantitative structure-activity relationship (QSAR) model in terms of its physicochemical descriptors with a large inventory of chemicals. A training set of 105 chemicals with data for relative estrogenic gene activation, obtained in a recombinant yeast assay, was used to develop the QSAR. A binary classification model for predicting active versus inactive chemicals was developed using classification tree analysis and two descriptors with a clear physicochemical meaning (octanol-water partition coefficient, or log Kow, and the number of hydrogen bond donors, or n(Hdon)). The model demonstrated a high overall accuracy (90.5%), with a sensitivity of 95.9% and a specificity of 78.1%. The robustness of the model was evaluated using the leave-many-out cross-validation technique, whereas the predictivity was assessed using an artificial external test set composed of 12 compounds. The domain of the QSAR training set was compared with the chemical space covered by the European Inventory of Existing Commercial Chemical Substances (EINECS), as incorporated in the CDB-EC software, in the log Kow / n(Hdon) plane. The results showed that the training set and, therefore, the applicability domain of the QSAR model covers a small part of the physicochemical domain of the inventory, even though a simple method for defining the applicability domain (ranges in the descriptor space) was used. However, a large number of compounds are located within the narrow descriptor window.

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.

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.

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.

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.

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

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.

Synthesis, spectral and chemical reactivity analysis of 2,4-dinitrophenyl hydrazone having pyrrole moiety

NASA Astrophysics Data System (ADS)

Rawat, Poonam; Singh, R. N.

2015-10-01

In this paper we present combined experimental and theoretical study on a newly synthesized ethyl 2-cyano-3-[5-(2,4-dinitrophenyl)-hydrazonomethyl)-1H-pyrrol-2-yl]-acrylate (ECDHPA). Quantum chemical calculations have been performed using HF/6-31G(d,p), B3LYP/6-31G(d,p) and B3LYP/6-31++G(d,p) levels. The results obtained from quantum chemical calculations matches well with the experimental finding. Molecular electrostatic potential (MEP) surface of N17sbnd H39⋯O42dbnd N37 zone show green color having moderate electrostatic potential indicating hydrogen bonding. For the interactions N17sbnd H34⋯O42 electron density and its Laplacian (∇2ρBCP) are in the range 0.051-0.119 a.u., indicating interaction follows the Koch and Popelier criteria. The observed Nsbnd H (νN17sbnd H34) stretch of sbnd CHdbnd Nsbnd NH sbnd part of molecule at 3262 cm-1 indicate the red shift and the involvement in hydrogen bonding. Natural bond orbital (NBO) investigation shows various intramolecular interactions within molecule. Electrophilic charge transfer (ECT) has been calculated to investigate the relative electrophilic or nucleophilic behavior of reactant molecules involved in chemical reaction. The first hyperpolarizability (β0) value of ECDHPA is calculated as 22.42 × 10-30 esu. The solvent-induced effects on the non-linear optical properties (NLO) were studied using self-consistent reaction field (SCRF) method and observed that the β0 value increases as solvent polarity increases. DFT based electronic descriptors analysis reveals that studied molecule is a strong electrophile and it would undergo to form various heterocyclic compounds.

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.

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.

Novel benzanthrone probes for membrane and protein studies

NASA Astrophysics Data System (ADS)

Ryzhova, Olga; Vus, Kateryna; Trusova, Valeriya; Kirilova, Elena; Kirilov, Georgiy; Gorbenko, Galyna; Kinnunen, Paavo

2016-09-01

The applicability of a series of novel benzanthrone dyes to monitoring the changes in physicochemical properties of lipid bilayer and to differentiating between the native and aggregated protein states has been evaluated. Based on the quantitative parameters of the dye-membrane and dye-protein binding derived from the fluorimetric titration data, the most prospective membrane probes and amyloid tracers have been selected from the group of examined compounds. Analysis of the red edge excitation shifts of the membrane- and amyloid-bound dyes provided information on the properties of benzanthrone binding sites within the lipid and protein matrixes. To understand how amyloid specificity of benzanthrones correlates with their structure, quantitative structure activity relationship (QSAR) analysis was performed involving a range of quantum chemical molecular descriptors. A statistically significant model was obtained for predicting the sensitivity of novel benzanthrone dyes to amyloid fibrils.

Spectral analysis and quantum chemical studies of chair and twist-boat conformers of cycloheximide in gas and solution phases

NASA Astrophysics Data System (ADS)

Tokatli, A.; Ucun, F.; Sütçü, K.; Osmanoğlu, Y. E.; Osmanoğlu, Ş.

2018-02-01

In this study the conformational behavior of cycloheximide in the gas and solution (CHCl3) phases has theoretically been investigated by spectroscopic and quantum chemical properties using density functional theory (wB97X-D) method with 6-31++G(d,p) basis set, for the first time. The calculated IR results reveal that in the ground state the molecule exits as a mixture of the chair and twist-boat conformers in the gas phase, while the calculated NMR results reveal that it only exits as the chair conformer in the solution phase. In order to obtain the contributions coming from intramolecular interactions to the stability of the conformers in the gas and solution phases, the quantum theory of atoms in molecules (QTAIM), noncovalent interactions (NCI) method, and natural bond orbital analysis (NBO) have been employed. The QTAIM and NCI methods indicated that by intramolecular interactions with bond critical point (BCP) the twist-boat conformer is more stabilized than the chair conformer, while by steric interactions it is more destabilized. Considering that these interactions balance each other, the stabilities of the conformers are understood to be dictated by the van der Waals interactions. The NBO analyses show that the hyperconjugative and steric effects play an important role in the stabilization in the gas and solution phases. Furthermore, to get a better understanding of the chemical behavior of this important antibiotic drug we have evaluated and, commented the global and local reactivity descriptors of the both conformers. Finally, the EPR analysis of γ-irradiated cycloheximide has been done. The comparison of the experimental and calculated data have showed the inducement of a radical structure of (CH2)2ĊCH2 in the molecule. The experimental EPR spectrum has also confirmed that the molecule simultaneously exists in the chair and twist-boat conformers in the solid phase.

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.

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.

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.

Exploring the structure-activity relations of N-carbethoxyphthalimide by combining FTIR, FT-Raman and NMR spectroscopy with DFT electronic structure method.

PubMed

Arjunan, V; Govindaraja, S Thillai; Ravindran, P; Mohan, S

2014-01-01

The complete vibrational assignment and analysis of N-carbethoxyphthalimide were carried out using the experimental FTIR and FT-Raman data in the range 4000-450 and 4000-100 cm(-1), respectively along with quantum chemical studies of the compound using DFT-B3LYP gradient calculations employing the 6-31G**, 6-311++G** and cc-pVDZ basis sets. The 1H (400 MHz; CDCl3) and 13C (100 MHz;CDCl3) nuclear magnetic resonance (NMR) spectra were also recorded. Due to the partial ionic nature of the carbonyl group, the carbon atoms C1 and C3 in NCEP show downfield effect and the corresponding observed chemical shift of both are observed at 163.76 ppm and the carbon atom C16 in the carbethoxy group also give signal in the downfield at 148.45 ppm. The active sites are determined by molecular electrostatic potential. The possible electronic transitions are determined by HOMO and LUMO orbital shapes and their energies. The structure-chemical reactivity relations of the compound were determined through chemical potential, global hardness, global softness, electronegativity, electrophilicity and local reactivity descriptors by conceptual DFT methods. Copyright © 2013 Elsevier B.V. All rights reserved.

High-Throughput Design of Two-Dimensional Electron Gas Systems Based on Polar/Nonpolar Perovskite Oxide Heterostructures

PubMed Central

Yang, Kesong; Nazir, Safdar; Behtash, Maziar; Cheng, Jianli

2016-01-01

The two-dimensional electron gas (2DEG) formed at the interface between two insulating oxides such as LaAlO3 and SrTiO3 (STO) is of fundamental and practical interest because of its novel interfacial conductivity and its promising applications in next-generation nanoelectronic devices. Here we show that a group of combinatorial descriptors that characterize the polar character, lattice mismatch, band gap, and the band alignment between the perovskite-oxide-based band insulators and the STO substrate, can be introduced to realize a high-throughput (HT) design of SrTiO3-based 2DEG systems from perovskite oxide quantum database. Equipped with these combinatorial descriptors, we have carried out a HT screening of all the polar perovskite compounds, uncovering 42 compounds of potential interests. Of these, Al-, Ga-, Sc-, and Ta-based compounds can form a 2DEG with STO, while In-based compounds exhibit a strain-induced strong polarization when deposited on STO substrate. In particular, the Ta-based compounds can form 2DEG with potentially high electron mobility at (TaO2)+/(SrO)0 interface. Our approach, by defining materials descriptors solely based on the bulk materials properties, and by relying on the perovskite-oriented quantum materials repository, opens new avenues for the discovery of perovskite-oxide-based functional interface materials in a HT fashion. PMID:27708415

Exploration of the Medicinal Peptide Space.

PubMed

Gevaert, Bert; Stalmans, Sofie; Wynendaele, Evelien; Taevernier, Lien; Bracke, Nathalie; D'Hondt, Matthias; De Spiegeleer, Bart

2016-01-01

The chemical properties of peptide medicines, known as the 'medicinal peptide space' is considered a multi-dimensional subset of the global peptide space, where each dimension represents a chemical descriptor. These descriptors can be linked to biofunctional, medicinal properties to varying degrees. Knowledge of this space can increase the efficiency of the peptide-drug discovery and development process, as well as advance our understanding and classification of peptide medicines. For 245 peptide drugs, already available on the market or in clinical development, multivariate dataexploration was performed using peptide relevant physicochemical descriptors, their specific peptidedrug target and their clinical use. Our retrospective analysis indicates that clusters in the medicinal peptide space are located in a relatively narrow range of the physicochemical space: dense and empty regions were found, which can be explored for the discovery of novel peptide drugs.

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

SNAP: Automated Generation of High-Accuracy Interatomic Potentials using Quantum Data

NASA Astrophysics Data System (ADS)

Thompson, Aidan; Wood, Mitchell; Phillpot, Simon

Molecular dynamics simulation is a powerful computational method for bridging between macroscopic continuum models and quantum models treating a few hundred atoms, but it is limited by the accuracy of the interatomic potential. Sound physical and chemical understanding have led to good potentials for certain systems, but it is difficult to extend them to new materials and properties. The solution is obvious but challenging: develop more complex potentials that reproduce large quantum datasets. The growing availability of large data sets has made it possible to use automated machine-learning approaches for interatomic potential development. In the SNAP approach, the interatomic potential depends on a very general set of atomic neighborhood descriptors, based on the bispectrum components of the density projected onto the surface of the unit 3-sphere. Previously, this approach was demonstrated for tantalum, reproducing the screw dislocation Peierls barrier. In this talk, it will be shown that the SNAP method is capable of reproducing a wide range of energy landscapes relevant to diverse material science applications: i) point defects in indium phosphide, ii) stability of tungsten surfaces at high temperatures, and iii) formation of intrinsic defects in uranium. Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corp., for the U.S. Dept. of Energys National Nuclear Security Admin. under contract DE-AC04-94AL85000.

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

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.

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.

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.

Recognition of anions using urea and thiourea substituted calixarenes: A density functional theory study of non-covalent interactions

NASA Astrophysics Data System (ADS)

Athar, Mohd; Lone, Mohsin Y.; Jha, Prakash C.

2018-02-01

Designing of new calixarene receptors for the selective binding of anions is an age-old concept; even though expected outcomes from this field are at premature stage. Herein, we have performed quantum chemical calculations to provide structural basis of anion binding with urea and thiourea substituted calixarenes (1, 2, and 3). In particular, spherical halides (F-, Cl-, Br-) and linear anions (CN-, N3-, SCN-) were modelled for calculating binding energies with receptor 1, 2 and 3 followed by their marked IR vibrations; taking the available experimental information into account. We found that the thiourea substitutions have better capability to stabilize the anions. Results have suggested that the structural behaviour of macrocyclic motifs were responsible for displaying the anion binding potentials. Moreover, second order "charge transfer" interactions of n-σ∗NH and n-σ∗OH type along the H-bond axis played critical role in developing hydrogen bonds. The present work also examines the role of non-covalent interactions (NCI) and their effects on thermodynamic and chemical-reactivity descriptors.

Insights into the regioselectivity and RNA-binding affinity of HIV-1 nucleocapsid protein from linear-scaling quantum methods.

PubMed

Khandogin, Jana; Musier-Forsyth, Karin; York, Darrin M

2003-07-25

Human immunodeficiency virus type 1 (HIV-1) nucleocapsid protein (NC) plays several important roles in the viral life-cycle and presents an attractive target for rational drug design. Here, the macromolecular reactivity of NC and its binding to RNA is characterized through determination of electrostatic and chemical descriptors derived from linear-scaling quantum calculations in solution. The computational results offer a rationale for the experimentally observed susceptibility of the Cys49 thiolate toward small-molecule electrophilic agents, and support the recently proposed stepwise protonation mechanism of the C-terminal Zn-coordination complex. The distinctive binding mode of NC to SL2 and SL3 stem-loops of the HIV-1 genomic RNA packaging signal is studied on the basis of protein side-chain contributions to the electrostatic binding energies. These results indicate the importance of several basic residues in the 3(10) helical region and the N-terminal zinc finger, and rationalize the presence of several evolutionarily conserved residues in NC. The combined reactivity and RNA-binding study provides new insights that may contribute toward the structure-based design of anti-HIV therapies.

A Novel Two-Step Hierarchical Quantitative Structure–Activity Relationship Modeling Work Flow for Predicting Acute Toxicity of Chemicals in Rodents

PubMed Central

Zhu, Hao; Ye, Lin; Richard, Ann; Golbraikh, Alexander; Wright, Fred A.; Rusyn, Ivan; Tropsha, Alexander

2009-01-01

Background Accurate prediction of in vivo toxicity from in vitro testing is a challenging problem. Large public–private consortia have been formed with the goal of improving chemical safety assessment by the means of high-throughput screening. Objective A wealth of available biological data requires new computational approaches to link chemical structure, in vitro data, and potential adverse health effects. Methods and results A database containing experimental cytotoxicity values for in vitro half-maximal inhibitory concentration (IC50) and in vivo rodent median lethal dose (LD50) for more than 300 chemicals was compiled by Zentralstelle zur Erfassung und Bewertung von Ersatz- und Ergaenzungsmethoden zum Tierversuch (ZEBET; National Center for Documentation and Evaluation of Alternative Methods to Animal Experiments). The application of conventional quantitative structure–activity relationship (QSAR) modeling approaches to predict mouse or rat acute LD50 values from chemical descriptors of ZEBET compounds yielded no statistically significant models. The analysis of these data showed no significant correlation between IC50 and LD50. However, a linear IC50 versus LD50 correlation could be established for a fraction of compounds. To capitalize on this observation, we developed a novel two-step modeling approach as follows. First, all chemicals are partitioned into two groups based on the relationship between IC50 and LD50 values: One group comprises compounds with linear IC50 versus LD50 relationships, and another group comprises the remaining compounds. Second, we built conventional binary classification QSAR models to predict the group affiliation based on chemical descriptors only. Third, we developed k-nearest neighbor continuous QSAR models for each subclass to predict LD50 values from chemical descriptors. All models were extensively validated using special protocols. Conclusions The novelty of this modeling approach is that it uses the relationships between in vivo and in vitro data only to inform the initial construction of the hierarchical two-step QSAR models. Models resulting from this approach employ chemical descriptors only for external prediction of acute rodent toxicity. PMID:19672406

A novel two-step hierarchical quantitative structure-activity relationship modeling work flow for predicting acute toxicity of chemicals in rodents.

PubMed

Zhu, Hao; Ye, Lin; Richard, Ann; Golbraikh, Alexander; Wright, Fred A; Rusyn, Ivan; Tropsha, Alexander

2009-08-01

Accurate prediction of in vivo toxicity from in vitro testing is a challenging problem. Large public-private consortia have been formed with the goal of improving chemical safety assessment by the means of high-throughput screening. A wealth of available biological data requires new computational approaches to link chemical structure, in vitro data, and potential adverse health effects. A database containing experimental cytotoxicity values for in vitro half-maximal inhibitory concentration (IC(50)) and in vivo rodent median lethal dose (LD(50)) for more than 300 chemicals was compiled by Zentralstelle zur Erfassung und Bewertung von Ersatz- und Ergaenzungsmethoden zum Tierversuch (ZEBET; National Center for Documentation and Evaluation of Alternative Methods to Animal Experiments). The application of conventional quantitative structure-activity relationship (QSAR) modeling approaches to predict mouse or rat acute LD(50) values from chemical descriptors of ZEBET compounds yielded no statistically significant models. The analysis of these data showed no significant correlation between IC(50) and LD(50). However, a linear IC(50) versus LD(50) correlation could be established for a fraction of compounds. To capitalize on this observation, we developed a novel two-step modeling approach as follows. First, all chemicals are partitioned into two groups based on the relationship between IC(50) and LD(50) values: One group comprises compounds with linear IC(50) versus LD(50) relationships, and another group comprises the remaining compounds. Second, we built conventional binary classification QSAR models to predict the group affiliation based on chemical descriptors only. Third, we developed k-nearest neighbor continuous QSAR models for each subclass to predict LD(50) values from chemical descriptors. All models were extensively validated using special protocols. The novelty of this modeling approach is that it uses the relationships between in vivo and in vitro data only to inform the initial construction of the hierarchical two-step QSAR models. Models resulting from this approach employ chemical descriptors only for external prediction of acute rodent toxicity.

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.

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

Quantitative Structure--Activity Relationship (QSAR) for the Oxidation of Trace Organic Contaminants by Sulfate Radical.

PubMed

Xiao, Ruiyang; Ye, Tiantian; Wei, Zongsu; Luo, Shuang; Yang, Zhihui; Spinney, Richard

2015-11-17

The sulfate radical anion (SO4•–) based oxidation of trace organic contaminants (TrOCs) has recently received great attention due to its high reactivity and low selectivity. In this study, a meta-analysis was conducted to better understand the role of functional groups on the reactivity between SO4•– and TrOCs. The results indicate that compounds in which electron transfer and addition channels dominate tend to exhibit a faster second-order rate constants (kSO4•–) than that of H–atom abstraction, corroborating the SO4•– reactivity and mechanisms observed in the individual studies. Then, a quantitative structure activity relationship (QSAR) model was developed using a sequential approach with constitutional, geometrical, electrostatic, and quantum chemical descriptors. Two descriptors, ELUMO and EHOMO energy gap (ELUMO–EHOMO) and the ratio of oxygen atoms to carbon atoms (#O:C), were found to mechanistically and statistically affect kSO4•– to a great extent with the standardized QSAR model: ln kSO4•– = 26.8–3.97 × #O:C – 0.746 × (ELUMO–EHOMO). In addition, the correlation analysis indicates that there is no dominant reaction channel for SO4•– reactions with various structurally diverse compounds. Our QSAR model provides a robust predictive tool for estimating emerging micropollutants removal using SO4•– during wastewater treatment processes.

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.

Entropy Is Simple, Qualitatively.

ERIC Educational Resources Information Center

Lambert, Frank L.

2002-01-01

Suggests that qualitatively, entropy is simple. Entropy increase from a macro viewpoint is a measure of the dispersal of energy from localized to spread out at a temperature T. Fundamentally based on statistical and quantum mechanics, this approach is superior to the non-fundamental "disorder" as a descriptor of entropy change. (MM)

DFT and TD-DFT study of the adsorption and detection of sulfur mustard chemical warfare agent by the C24, C12Si12, Al12N12, Al12P12, Be12O12, B12N12 and Mg12O12 nanocages

NASA Astrophysics Data System (ADS)

Jouypazadeh, Hamidreza; Farrokhpour, Hossein

2018-07-01

In the present research, the interaction of sulfur mustard, a chemical warfare agent, with the surface of C24, C12Si12, Al12N12, Al12P12, Be12O12, B12N12 and Mg12O12 nanocages was studied using the dispersion corrected density function theory (DFT-D3) method. The calculated adsorption energies of sulfur mustard on the surface of the nanocages showed that the Al12N12, C12Si12 and Mg12O12 are useful for the adsorption of the sulfur mustard. The quantum theory atom in molecule (QTAIM) analysis was used to study the nature of interactions of sulfur mustard with the surface of the selected nanocages. Based on QTAIM analysis, the majority of interactions of sulfur and chlorine atoms of sulfur mustard with the surface of the considered nanocages are covalent and quasi covalent whereas the interactions of hydrogen atoms of sulfur mustard with the surface of the nanocages are generally non-covalent. The charge transfer between sulfur mustard and the nanocages as well as chemical quantum descriptors of complexes were calculated using natural bond orbital (NBO) method. The most electron charge transfers from the sulfur mustard to B12N12 nanocage where the S atom of sulfur mustard donor a chemical bond to B atom of the nanocage. The ability of the considered nanocages for detecting sulfur mustard was studied using time-dependent density function theory (TD-DFT) and density of state (DOS) diagram. It is found that the C24, Al12P12, Be12O12 and B12N12 nanocages are useful sensors for this chemical agent.

Predicting hepatotoxicity using ToxCast in vitro bioactivity and chemical structure

EPA Science Inventory

Background: The U.S. EPA ToxCastTM program is screening thousands of environmental chemicals for bioactivity using hundreds of high-throughput in vitro assays to build predictive models of toxicity. We represented chemicals based on bioactivity and chemical structure descriptors ...

Determination of solute descriptors by chromatographic methods.

PubMed

Poole, Colin F; Atapattu, Sanka N; Poole, Salwa K; Bell, Andrea K

2009-10-12

The solvation parameter model is now well established as a useful tool for obtaining quantitative structure-property relationships for chemical, biomedical and environmental processes. The model correlates a free-energy related property of a system to six free-energy derived descriptors describing molecular properties. These molecular descriptors are defined as L (gas-liquid partition coefficient on hexadecane at 298K), V (McGowan's characteristic volume), E (excess molar refraction), S (dipolarity/polarizability), A (hydrogen-bond acidity), and B (hydrogen-bond basicity). McGowan's characteristic volume is trivially calculated from structure and the excess molar refraction can be calculated for liquids from their refractive index and easily estimated for solids. The remaining four descriptors are derived by experiment using (largely) two-phase partitioning, chromatography, and solubility measurements. In this article, the use of gas chromatography, reversed-phase liquid chromatography, micellar electrokinetic chromatography, and two-phase partitioning for determining solute descriptors is described. A large database of experimental retention factors and partition coefficients is constructed after first applying selection tools to remove unreliable experimental values and an optimized collection of varied compounds with descriptor values suitable for calibrating chromatographic systems is presented. These optimized descriptors are demonstrated to be robust and more suitable than other groups of descriptors characterizing the separation properties of chromatographic systems.

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

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.

Lagrangian descriptors of driven chemical reaction manifolds.

PubMed

Craven, Galen T; Junginger, Andrej; Hernandez, Rigoberto

2017-08-01

The persistence of a transition state structure in systems driven by time-dependent environments allows the application of modern reaction rate theories to solution-phase and nonequilibrium chemical reactions. However, identifying this structure is problematic in driven systems and has been limited by theories built on series expansion about a saddle point. Recently, it has been shown that to obtain formally exact rates for reactions in thermal environments, a transition state trajectory must be constructed. Here, using optimized Lagrangian descriptors [G. T. Craven and R. Hernandez, Phys. Rev. Lett. 115, 148301 (2015)PRLTAO0031-900710.1103/PhysRevLett.115.148301], we obtain this so-called distinguished trajectory and the associated moving reaction manifolds on model energy surfaces subject to various driving and dissipative conditions. In particular, we demonstrate that this is exact for harmonic barriers in one dimension and this verification gives impetus to the application of Lagrangian descriptor-based methods in diverse classes of chemical reactions. The development of these objects is paramount in the theory of reaction dynamics as the transition state structure and its underlying network of manifolds directly dictate reactivity and selectivity.

Quantitative structure activity relationships from optimised ab initio bond lengths: steroid binding affinity and antibacterial activity of nitrofuran derivatives

NASA Astrophysics Data System (ADS)

Smith, P. J.; Popelier, P. L. A.

2004-02-01

The present day abundance of cheap computing power enables the use of quantum chemical ab initio data in Quantitative Structure-Activity Relationships (QSARs). Optimised bond lengths are a new such class of descriptors, which we have successfully used previously in representing electronic effects in medicinal and ecological QSARs (enzyme inhibitory activity, hydrolysis rate constants and pKas). Here we use AM1 and HF/3-21G* bond lengths in conjunction with Partial Least Squares (PLS) and a Genetic Algorithm (GA) to predict the Corticosteroid-Binding Globulin (CBG) binding activity of the classic steroid data set, and the antibacterial activity of nitrofuran derivatives. The current procedure, which does not require molecular alignment, produces good r2 and q2 values. Moreover, it highlights regions in the common steroid skeleton deemed relevant to the active regions of the steroids and nitrofuran derivatives.

Use of Cell Viability Assay Data Improves the Prediction Accuracy of Conventional Quantitative Structure–Activity Relationship Models of Animal Carcinogenicity

PubMed Central

Zhu, Hao; Rusyn, Ivan; Richard, Ann; Tropsha, Alexander

2008-01-01

Background To develop efficient approaches for rapid evaluation of chemical toxicity and human health risk of environmental compounds, the National Toxicology Program (NTP) in collaboration with the National Center for Chemical Genomics has initiated a project on high-throughput screening (HTS) of environmental chemicals. The first HTS results for a set of 1,408 compounds tested for their effects on cell viability in six different cell lines have recently become available via PubChem. Objectives We have explored these data in terms of their utility for predicting adverse health effects of the environmental agents. Methods and results Initially, the classification k nearest neighbor (kNN) quantitative structure–activity relationship (QSAR) modeling method was applied to the HTS data only, for a curated data set of 384 compounds. The resulting models had prediction accuracies for training, test (containing 275 compounds together), and external validation (109 compounds) sets as high as 89%, 71%, and 74%, respectively. We then asked if HTS results could be of value in predicting rodent carcinogenicity. We identified 383 compounds for which data were available from both the Berkeley Carcinogenic Potency Database and NTP–HTS studies. We found that compounds classified by HTS as “actives” in at least one cell line were likely to be rodent carcinogens (sensitivity 77%); however, HTS “inactives” were far less informative (specificity 46%). Using chemical descriptors only, kNN QSAR modeling resulted in 62.3% prediction accuracy for rodent carcinogenicity applied to this data set. Importantly, the prediction accuracy of the model was significantly improved (72.7%) when chemical descriptors were augmented by HTS data, which were regarded as biological descriptors. Conclusions Our studies suggest that combining NTP–HTS profiles with conventional chemical descriptors could considerably improve the predictive power of computational approaches in toxicology. PMID:18414635

PREDICTING TOXICOLOGICAL ENDPOINTS OF CHEMICALS USING QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIPS (QSARS)

EPA Science Inventory

Quantitative structure-activity relationships (QSARs) are being developed to predict the toxicological endpoints for untested chemicals similar in structure to chemicals that have known experimental toxicological data. Based on a very large number of predetermined descriptors, a...

Cheminformatic Analysis of the US EPA ToxCast Chemical Library

EPA Science Inventory

The ToxCast project is employing high throughput screening (HTS) technologies, along with chemical descriptors and computational models, to develop approaches for screening and prioritizing environmental chemicals for further toxicity testing. ToxCast Phase I generated HTS data f...

Bayesian screening for active compounds in high-dimensional chemical spaces combining property descriptors and molecular fingerprints.

PubMed

Vogt, Martin; Bajorath, Jürgen

2008-01-01

Bayesian classifiers are increasingly being used to distinguish active from inactive compounds and search large databases for novel active molecules. We introduce an approach to directly combine the contributions of property descriptors and molecular fingerprints in the search for active compounds that is based on a Bayesian framework. Conventionally, property descriptors and fingerprints are used as alternative features for virtual screening methods. Following the approach introduced here, probability distributions of descriptor values and fingerprint bit settings are calculated for active and database molecules and the divergence between the resulting combined distributions is determined as a measure of biological activity. In test calculations on a large number of compound activity classes, this methodology was found to consistently perform better than similarity searching using fingerprints and multiple reference compounds or Bayesian screening calculations using probability distributions calculated only from property descriptors. These findings demonstrate that there is considerable synergy between different types of property descriptors and fingerprints in recognizing diverse structure-activity relationships, at least in the context of Bayesian modeling.

Monitoring the sensory quality of canned white asparagus through cluster analysis.

PubMed

Arana, Inés; Ibañez, Francisco C; Torre, Paloma

2016-05-01

White asparagus is one of the 30 vegetables most consumed in the world. This paper unifies the stages of their sensory quality control. The aims of this work were to describe the sensory properties of canned white asparagus and their quality control and to evaluate the applicability of agglomerative hierarchical clustering (AHC) for classifying and monitoring the sensory quality of manufacturers. Sixteen sensory descriptors and their evaluation technique were defined. The sensory profile of canned white asparagus was high flavor characteristic, little acidity and bitterness, medium firmness and very light fibrosity, among other characteristics. The dendrogram established groups of manufacturers that had similar scores in the same set of descriptors, and each cluster grouped the manufacturers that had a similar quality profile. The sensory profile of canned white asparagus was clearly defined through the intensity evaluation of 16 descriptors, and the sensory quality report provided to the manufacturers is in detail and of easy interpretation. AHC grouped the manufacturers according to the highest quality scores in certain descriptors and is a useful tool because it is very visual. © 2015 Society of Chemical Industry. © 2015 Society of Chemical Industry.

Investigations on the charge transfer mechanism at donor/acceptor interfaces in the quest for descriptors of organic solar cell performance.

PubMed

Muraoka, Azusa; Fujii, Mikiya; Mishima, Kenji; Matsunaga, Hiroki; Benten, Hiroaki; Ohkita, Hideo; Ito, Shinzaburo; Yamashita, Koichi

2018-05-07

Herein, we theoretically and experimentally investigated the mechanisms of charge separation processes of organic thin-film solar cells. PTB7, PTB1, and PTBF2 have been chosen as donors and PC 71 BM has been chosen as an acceptor considering that effective charge generation depends on the difference between the material combinations. Experimental results of transient absorption spectroscopy show that the hot process is a key step for determining external quantum efficiency (EQE) in these systems. From the quantum chemistry calculations, it has been found that EQE tends to increase as the transferred charge, charge transfer distance, and variation of dipole moments between the ground and excited states of the donor/acceptor complexes increase; this indicates that these physical quantities are a good descriptor to assess the donor-acceptor charge transfer quality contributing to the solar cell performance. We propose that designing donor/acceptor interfaces with large values of charge transfer distance and variation of dipole moments of the donor/acceptor complexes is a prerequisite for developing high-efficiency polymer/PCBM solar cells.

Using machine learning and quantum chemistry descriptors to predict the toxicity of ionic liquids.

PubMed

Cao, Lingdi; Zhu, Peng; Zhao, Yongsheng; Zhao, Jihong

2018-06-15

Large-scale application of ionic liquids (ILs) hinges on the advancement of designable and eco-friendly nature. Research of the potential toxicity of ILs towards different organisms and trophic levels is insufficient. Quantitative structure-activity relationships (QSAR) model is applied to evaluate the toxicity of ILs towards the leukemia rat cell line (ICP-81). The structures of 57 cations and 21 anions were optimized by quantum chemistry. The electrostatic potential surface area (S EP ) and charge distribution area (S σ-profile ) descriptors are calculated and used to predict the toxicity of ILs. The performance and predictive aptitude of extreme learning machine (ELM) model are analyzed and compared with those of multiple linear regression (MLR) and support vector machine (SVM) models. The highest R 2 and the lowest AARD% and RMSE of the training set, test set and total set for the ELM are observed, which validates the superior performance of the ELM than that of obtained by the MLR and SVM. The applicability domain of the model is assessed by the Williams plot. Copyright © 2018 Elsevier B.V. All rights reserved.

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.

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.

Visual analytics in cheminformatics: user-supervised descriptor selection for QSAR methods.

PubMed

Martínez, María Jimena; Ponzoni, Ignacio; Díaz, Mónica F; Vazquez, Gustavo E; Soto, Axel J

2015-01-01

The design of QSAR/QSPR models is a challenging problem, where the selection of the most relevant descriptors constitutes a key step of the process. Several feature selection methods that address this step are concentrated on statistical associations among descriptors and target properties, whereas the chemical knowledge is left out of the analysis. For this reason, the interpretability and generality of the QSAR/QSPR models obtained by these feature selection methods are drastically affected. Therefore, an approach for integrating domain expert's knowledge in the selection process is needed for increase the confidence in the final set of descriptors. In this paper a software tool, which we named Visual and Interactive DEscriptor ANalysis (VIDEAN), that combines statistical methods with interactive visualizations for choosing a set of descriptors for predicting a target property is proposed. Domain expertise can be added to the feature selection process by means of an interactive visual exploration of data, and aided by statistical tools and metrics based on information theory. Coordinated visual representations are presented for capturing different relationships and interactions among descriptors, target properties and candidate subsets of descriptors. The competencies of the proposed software were assessed through different scenarios. These scenarios reveal how an expert can use this tool to choose one subset of descriptors from a group of candidate subsets or how to modify existing descriptor subsets and even incorporate new descriptors according to his or her own knowledge of the target property. The reported experiences showed the suitability of our software for selecting sets of descriptors with low cardinality, high interpretability, low redundancy and high statistical performance in a visual exploratory way. Therefore, it is possible to conclude that the resulting tool allows the integration of a chemist's expertise in the descriptor selection process with a low cognitive effort in contrast with the alternative of using an ad-hoc manual analysis of the selected descriptors. Graphical abstractVIDEAN allows the visual analysis of candidate subsets of descriptors for QSAR/QSPR. In the two panels on the top, users can interactively explore numerical correlations as well as co-occurrences in the candidate subsets through two interactive graphs.

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.

Reliable but Timesaving: In Search of an Efficient Quantum-chemical Method for the Description of Functional Fullerenes.

PubMed

Reis, H; Rasulev, B; Papadopoulos, M G; Leszczynski, J

2015-01-01

Fullerene and its derivatives are currently one of the most intensively investigated species in the area of nanomedicine and nanochemistry. Various unique properties of fullerenes are responsible for their wide range applications in industry, biology and medicine. A large pool of functionalized C60 and C70 fullerenes is investigated theoretically at different levels of quantum-mechanical theory. The semiempirial PM6 method, density functional theory with the B3LYP functional, and correlated ab initio MP2 method are employed to compute the optimized structures, and an array of properties for the considered species. In addition to the calculations for isolated molecules, the results of solution calculations are also reported at the DFT level, using the polarizable continuum model (PCM). Ionization potentials (IPs) and electron affinities (EAs) are computed by means of Koopmans' theorem as well as with the more accurate but computationally expensive ΔSCF method. Both procedures yield comparable values, while comparison of IPs and EAs computed with different quantum-mechanical methods shows surprisingly large differences. Harmonic vibrational frequencies are computed at the PM6 and B3LYP levels of theory and compared with each other. A possible application of the frequencies as 3D descriptors in the EVA (EigenVAlues) method is shown. All the computed data are made available, and may be used to replace experimental data in routine applications where large amounts of data are required, e.g. in structure-activity relationship studies of the toxicity of fullerene derivatives.

Development of TLSER model and QSAR model for predicting partition coefficients of hydrophobic organic chemicals between low density polyethylene film and water.

PubMed

Liu, Huihui; Wei, Mengbi; Yang, Xianhai; Yin, Cen; He, Xiao

2017-01-01

Partition coefficients are vital parameters for measuring accurately the chemicals concentrations by passive sampling devices. Given the wide use of low density polyethylene (LDPE) film in passive sampling, we developed a theoretical linear solvation energy relationship (TLSER) model and a quantitative structure-activity relationship (QSAR) model for the prediction of the partition coefficient of chemicals between LDPE and water (K pew ). For chemicals with the octanol-water partition coefficient (log K ow ) <8, a TLSER model with V x (McGowan volume) and qA - (the most negative charge on O, N, S, X atoms) as descriptors was developed, but the model had relatively low determination coefficient (R 2 ) and cross-validated coefficient (Q 2 ). In order to further explore the theoretical mechanisms involved in the partition process, a QSAR model with four descriptors (MLOGP (Moriguchi octanol-water partition coeff.), P_VSA_s_3 (P_VSA-like on I-state, bin 3), Hy (hydrophilic factor) and NssO (number of atoms of type ssO)) was established, and statistical analysis indicated that the model had satisfactory goodness-of-fit, robustness and predictive ability. For chemicals with log K OW >8, a TLSER model with V x and a QSAR model with MLOGP as descriptor were developed. This is the first paper to explore the models for highly hydrophobic chemicals. The applicability domain of the models, characterized by the Euclidean distance-based method and Williams plot, covered a large number of structurally diverse chemicals, which included nearly all the common hydrophobic organic compounds. Additionally, through mechanism interpretation, we explored the structural features those governing the partition behavior of chemicals between LDPE and water. Copyright © 2016 Elsevier B.V. All rights reserved.

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.

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.

Chemical reactivity indices for the complete series of chlorinated benzenes: solvent effect.

PubMed

Padmanabhan, J; Parthasarathi, R; Subramanian, V; Chattaraj, P K

2006-03-02

We present a comprehensive analysis to probe the effect of solvation on the reactivity of the complete series of chlorobenzenes through the conceptual density functional theory (DFT)-based global and local descriptors. We propose a multiphilic descriptor in this study to explore the nature of attack at a particular site in a molecule. It is defined as the difference between nucleophilic and electrophilic condensed philicity functions. This descriptor is capable of explaining both the nucleophilicity and electrophilicity of the given atomic sites in the molecule simultaneously. The predictive ability of this descriptor is tested on the complete series of chlorobenzenes in gas and solvent media. A structure-toxicity analysis of these entire sets of chlorobenzenes toward aquatic organisms demonstrates the importance of the electrophilicity index in the prediction of the reactivity/toxicity.

Integrating Biological and Chemical Data for Hepatotoxicity Prediction (SOT)

EPA Science Inventory

The U.S. EPA ToxCastTM program is screening thousands of environmental chemicals for bioactivity using hundreds of high-throughput in vitro assays to build predictive models of toxicity. A set of 677 chemicals were represented by 711 bioactivity descriptors (from ToxCast assays),...

Toxmatch-a new software tool to aid in the development and evaluation of chemically similar groups.

PubMed

Patlewicz, G; Jeliazkova, N; Gallegos Saliner, A; Worth, A P

2008-01-01

Chemical similarity is a widely used concept in toxicology, and is based on the hypothesis that similar compounds should have similar biological activities. This forms the underlying basis for performing read-across, forming chemical groups and developing (Quantitative) Structure-Activity Relationships ((Q)SARs). Chemical similarity is often perceived as structural similarity but in fact there are a number of other approaches that can be used to assess similarity. A systematic similarity analysis usually comprises two main steps. Firstly the chemical structures to be compared need to be characterised in terms of relevant descriptors which encode their physicochemical, topological, geometrical and/or surface properties. A second step involves a quantitative comparison of those descriptors using similarity (or dissimilarity) indices. This work outlines the use of chemical similarity principles in the formation of endpoint specific chemical groupings. Examples are provided to illustrate the development and evaluation of chemical groupings using a new software application called Toxmatch that was recently commissioned by the European Chemicals Bureau (ECB), of the European Commission's Joint Research Centre. Insights from using this software are highlighted with specific focus on the prospective application of chemical groupings under the new chemicals legislation, REACH.

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.

Aconitum and Delphinium sp. Alkaloids as Antagonist Modulators of Voltage-Gated Na+ Channels. AM1/DFT Electronic Structure Investigations and QSAR Studies

PubMed Central

Turabekova, Malakhat A.; Rasulev, Bakhtiyor F.; Levkovich, Mikhail G.; Abdullaev, Nasrulla D.; Leszczynski, Jerzy

2015-01-01

Early pharmacological studies of Aconitum and Delphinium sp. alkaloids suggested that these neurotoxins act at site 2 of voltage-gated Na+ channel and allosterically modulate its function. Understanding structural requirements for these compounds to exhibit binding activity at voltage-gated Na+ channel has been important in various fields. This paper reports quantum-chemical studies and quantitative structure-activity relationships (QSARs) based on a total of 65 natural alkaloids from two plant species, which includes both blockers and openers of sodium ion channel. A series of 18 antagonist alkaloids (9 blockers and 9 openers) have been studied using AM1 and DFT computational methods in order to reveal their structure-activity (structure-toxicity) relationship at electronic level. An examination of frontier orbitals obtained for ground and protonated forms of the compounds revealed that HOMOs and LUMOs were mainly represented by nitrogen atom and benzyl/benzoylester orbitals with –OH and –OCOCH3 contributions. The results obtained from this research have confirmed the experimental findings suggesting that neurotoxins acting at type 2 receptor site of voltage-dependent sodium channel are activators and blockers with common structural features and differ only in efficacy. The energetic tendency of HOMO-LUMO energy gap can probably distinguish activators and blockers that have been observed. Genetic Algorithm with Multiple Linear Regression Analysis (GA-MLRA) technique was also applied for the generation of two-descriptor QSAR models for the set of 65 blockers. Additionally to the computational studies, the HOMO-LUMO gap descriptor in each obtained QSAR model has confirmed the crucial role of charge transfer in receptor-ligand interactions. A number of other descriptors such as logP, IBEG, nNH2, nHDon, nCO have been selected as complementary ones to LUMO and their role in activity alteration has also been discussed. PMID:18201930

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.

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.

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.

New Fukui, dual and hyper-dual kernels as bond reactivity descriptors.

PubMed

Franco-Pérez, Marco; Polanco-Ramírez, Carlos-A; Ayers, Paul W; Gázquez, José L; Vela, Alberto

2017-06-21

We define three new linear response indices with promising applications for bond reactivity using the mathematical framework of τ-CRT (finite temperature chemical reactivity theory). The τ-Fukui kernel is defined as the ratio between the fluctuations of the average electron density at two different points in the space and the fluctuations in the average electron number and is designed to integrate to the finite-temperature definition of the electronic Fukui function. When this kernel is condensed, it can be interpreted as a site-reactivity descriptor of the boundary region between two atoms. The τ-dual kernel corresponds to the first order response of the Fukui kernel and is designed to integrate to the finite temperature definition of the dual descriptor; it indicates the ambiphilic reactivity of a specific bond and enriches the traditional dual descriptor by allowing one to distinguish between the electron-accepting and electron-donating processes. Finally, the τ-hyper dual kernel is defined as the second-order derivative of the Fukui kernel and is proposed as a measure of the strength of ambiphilic bonding interactions. Although these quantities have never been proposed, our results for the τ-Fukui kernel and for τ-dual kernel can be derived in zero-temperature formulation of the chemical reactivity theory with, among other things, the widely-used parabolic interpolation model.

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).

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.

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

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

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.

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 ...

Application of counterpropagation artificial neural network for modelling properties of fish antibiotics.

PubMed

Maran, E; Novic, M; Barbieri, P; Zupan, J

2004-01-01

The present study focuses on fish antibiotics which are an important group of pharmaceuticals used in fish farming to treat infections and, until recently, most of them have been exposed to the environment with very little attention. Information about the environmental behaviour and the description of the environmental fate of medical substances are difficult or expensive to obtain. The experimental information in terms of properties is reported when available, in other cases, it is estimated by standard tools as those provided by the United States Environmental Protection Agency EPISuite software and by custom quantitative structure-activity relationship (QSAR) applications. In this study, a QSAR screening of 15 fish antibiotics and 132 xenobiotic molecules was performed with two aims: (i) to develop a model for the estimation of octanol--water partition coefficient (logP) and (ii) to estimate the relative binding affinity to oestrogen receptor (log RBA) using a model constructed on the activities of 132 xenobiotic compounds. The custom models are based on constitutional, topological, electrostatic and quantum chemical descriptors computed by the CODESSA software. Kohonen neural networks (self organising maps) were used to study similarity between the considered chemicals while counter-propagation artificial neural networks were used to estimate the properties.

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.

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

Public Databases Supporting Computational Toxicology

EPA Science Inventory

A major goal of the emerging field of computational toxicology is the development of screening-level models that predict potential toxicity of chemicals from a combination of mechanistic in vitro assay data and chemical structure descriptors. In order to build these models, resea...

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.

High Throughput Heuristics for Prioritizing Human Exposure to ...

EPA Pesticide Factsheets

The risk posed to human health by any of the thousands of untested anthropogenic chemicals in our environment is a function of both the potential hazard presented by the chemical, and the possibility of being exposed. Without the capacity to make quantitative, albeit uncertain, forecasts of exposure, the putative risk of adverse health effect from a chemical cannot be evaluated. We used Bayesian methodology to infer ranges of exposure intakes that are consistent with biomarkers of chemical exposures identified in urine samples from the U.S. population by the National Health and Nutrition Examination Survey (NHANES). We perform linear regression on inferred exposure for demographic subsets of NHANES demarked by age, gender, and weight using high throughput chemical descriptors gleaned from databases and chemical structure-based calculators. We find that five of these descriptors are capable of explaining roughly 50% of the variability across chemicals for all the demographic groups examined, including children aged 6-11. For the thousands of chemicals with no other source of information, this approach allows rapid and efficient prediction of average exposure intake of environmental chemicals. The methods described by this manuscript provide a highly improved methodology for HTS of human exposure to environmental chemicals. The manuscript includes a ranking of 7785 environmental chemicals with respect to potential human exposure, including most of the Tox21 in vit

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.

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

Selection of quantum chemical descriptors by chemometric methods in the study of antioxidant activity of flavonoid compounds

NASA Astrophysics Data System (ADS)

Weber, K. C.; Honório, K. M.; da Silva, S. L.; Mercadante, R.; da Silva, A. B. F.

In the present study, the aim was to select electronic properties responsible for free radical scavenging ability of a set of 25 flavonoid compounds employing chemometric methods. Electronic parameters were calculated using the AM1 semiempirical method, and chemometric methods (principal component analysis, hierarchical cluster analysis, and k-nearest neighbor) were used with the aim to build models able to find relationships between electronic features and the antioxidant activity presented by the compounds studied. According to these models, four electronic variables can be considered important to discriminate more and less antioxidant flavonoid compounds: polarizability (α), charge at carbon 3 (QC3), total charge at substituent 5 (QS5), and total charge at substituent 3' (QS3'). The features found as being responsible for the antioxidant activity of the flavonoid compounds studied are consistent with previous results found in the literature. The results obtained can also bring improvements in the search for better antioxidant flavonoid compounds.

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.

Oral LD50 toxicity modeling and prediction of per- and polyfluorinated chemicals on rat and mouse.

PubMed

Bhhatarai, Barun; Gramatica, Paola

2011-05-01

Quantitative structure-activity relationship (QSAR) analyses were performed using the LD(50) oral toxicity data of per- and polyfluorinated chemicals (PFCs) on rodents: rat and mouse. PFCs are studied under the EU project CADASTER which uses the available experimental data for prediction and prioritization of toxic chemicals for risk assessment by using the in silico tools. The methodology presented here applies chemometrical analysis on the existing experimental data and predicts the toxicity of new compounds. QSAR analyses were performed on the available 58 mouse and 50 rat LD(50) oral data using multiple linear regression (MLR) based on theoretical molecular descriptors selected by genetic algorithm (GA). Training and prediction sets were prepared a priori from available experimental datasets in terms of structure and response. These sets were used to derive statistically robust and predictive (both internally and externally) models. The structural applicability domain (AD) of the models were verified on 376 per- and polyfluorinated chemicals including those in REACH preregistration list. The rat and mouse endpoints were predicted by each model for the studied compounds, and finally 30 compounds, all perfluorinated, were prioritized as most important for experimental toxicity analysis under the project. In addition, cumulative study on compounds within the AD of all four models, including two earlier published models on LC(50) rodent analysis was studied and the cumulative toxicity trend was observed using principal component analysis (PCA). The similarities and the differences observed in terms of descriptors and chemical/mechanistic meaning encoded by descriptors to prioritize the most toxic compounds are highlighted.

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.

Investigating Pharmacological Similarity by Charting Chemical Space.

PubMed

Buonfiglio, Rosa; Engkvist, Ola; Várkonyi, Péter; Henz, Astrid; Vikeved, Elisabet; Backlund, Anders; Kogej, Thierry

2015-11-23

In this study, biologically relevant areas of the chemical space were analyzed using ChemGPS-NP. This application enables comparing groups of ligands within a multidimensional space based on principle components derived from physicochemical descriptors. Also, 3D visualization of the ChemGPS-NP global map can be used to conveniently evaluate bioactive compound similarity and visually distinguish between different types or groups of compounds. To further establish ChemGPS-NP as a method to accurately represent the chemical space, a comparison with structure-based fingerprint has been performed. Interesting complementarities between the two descriptions of molecules were observed. It has been shown that the accuracy of describing molecules with physicochemical descriptors like in ChemGPS-NP is similar to the accuracy of structural fingerprints in retrieving bioactive molecules. Lastly, pharmacological similarity of structurally diverse compounds has been investigated in ChemGPS-NP space. These results further strengthen the case of using ChemGPS-NP as a tool to explore and visualize chemical space.

CADASTER QSPR Models for Predictions of Melting and Boiling Points of Perfluorinated Chemicals.

PubMed

Bhhatarai, Barun; Teetz, Wolfram; Liu, Tao; Öberg, Tomas; Jeliazkova, Nina; Kochev, Nikolay; Pukalov, Ognyan; Tetko, Igor V; Kovarich, Simona; Papa, Ester; Gramatica, Paola

2011-03-14

Quantitative structure property relationship (QSPR) studies on per- and polyfluorinated chemicals (PFCs) on melting point (MP) and boiling point (BP) are presented. The training and prediction chemicals used for developing and validating the models were selected from Syracuse PhysProp database and literatures. The available experimental data sets were split in two different ways: a) random selection on response value, and b) structural similarity verified by self-organizing-map (SOM), in order to propose reliable predictive models, developed only on the training sets and externally verified on the prediction sets. Individual linear and non-linear approaches based models developed by different CADASTER partners on 0D-2D Dragon descriptors, E-state descriptors and fragment based descriptors as well as consensus model and their predictions are presented. In addition, the predictive performance of the developed models was verified on a blind external validation set (EV-set) prepared using PERFORCE database on 15 MP and 25 BP data respectively. This database contains only long chain perfluoro-alkylated chemicals, particularly monitored by regulatory agencies like US-EPA and EU-REACH. QSPR models with internal and external validation on two different external prediction/validation sets and study of applicability-domain highlighting the robustness and high accuracy of the models are discussed. Finally, MPs for additional 303 PFCs and BPs for 271 PFCs were predicted for which experimental measurements are unknown. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Antimalarial activity of synthetic 1,2,4-trioxanes and cyclic peroxy ketals, a quantum similarity study

NASA Astrophysics Data System (ADS)

Gironés, X.; Gallegos, A.; Carbó-Dorca, R.

2001-12-01

In this work, the antimalarial activity of two series of 20 and 7 synthetic 1,2,4-trioxanes and a set of 20 cyclic peroxy ketals are tested for correlation search by means of Molecular Quantum Similarity Measures (MQSM). QSAR models, dealing with different biological responses (IC90, IC50 and ED90) of the parasite Plasmodium Falciparum, are constructed using MQSM as molecular descriptors and are satisfactorily correlated. The statistical results of the 20 1,2,4-trioxanes are deeply analyzed to elucidate the relevant structural features in the biological activity, revealing the importance of phenyl substitutions.

Designing a Quantitative Structure-Activity Relationship for the ...

EPA Pesticide Factsheets

Toxicokinetic models serve a vital role in risk assessment by bridging the gap between chemical exposure and potentially toxic endpoints. While intrinsic metabolic clearance rates have a strong impact on toxicokinetics, limited data is available for environmentally relevant chemicals including nearly 8000 chemicals tested for in vitro bioactivity in the Tox21 program. To address this gap, a quantitative structure-activity relationship (QSAR) for intrinsic metabolic clearance rate was developed to offer reliable in silico predictions for a diverse array of chemicals. Models were constructed with curated in vitro assay data for both pharmaceutical-like chemicals (ChEMBL database) and environmentally relevant chemicals (ToxCast screening) from human liver microsomes (2176 from ChEMBL) and human hepatocytes (757 from ChEMBL and 332 from ToxCast). Due to variability in the experimental data, a binned approach was utilized to classify metabolic rates. Machine learning algorithms, such as random forest and k-nearest neighbor, were coupled with open source molecular descriptors and fingerprints to provide reasonable estimates of intrinsic metabolic clearance rates. Applicability domains defined the optimal chemical space for predictions, which covered environmental chemicals well. A reduced set of informative descriptors (including relative charge and lipophilicity) and a mixed training set of pharmaceuticals and environmentally relevant chemicals provided the best intr

High-throughput screening of chemicals as functional ...

EPA Pesticide Factsheets

Identifying chemicals that provide a specific function within a product, yet have minimal impact on the human body or environment, is the goal of most formulation chemists and engineers practicing green chemistry. We present a methodology to identify potential chemical functional substitutes from large libraries of chemicals using machine learning based models. We collect and analyze publicly available information on the function of chemicals in consumer products or industrial processes to identify a suite of harmonized function categories suitable for modeling. We use structural and physicochemical descriptors for these chemicals to build 41 quantitative structure–use relationship (QSUR) models for harmonized function categories using random forest classification. We apply these models to screen a library of nearly 6400 chemicals with available structure information for potential functional substitutes. Using our Functional Use database (FUse), we could identify uses for 3121 chemicals; 4412 predicted functional uses had a probability of 80% or greater. We demonstrate the potential application of the models to high-throughput (HT) screening for “candidate alternatives” by merging the valid functional substitute classifications with hazard metrics developed from HT screening assays for bioactivity. A descriptor set could be obtained for 6356 Tox21 chemicals that have undergone a battery of HT in vitro bioactivity screening assays. By applying QSURs, we wer

Prediction of Chemical Function: Model Development and ...

EPA Pesticide Factsheets

The United States Environmental Protection Agency’s Exposure Forecaster (ExpoCast) project is developing both statistical and mechanism-based computational models for predicting exposures to thousands of chemicals, including those in consumer products. The high-throughput (HT) screening-level exposures developed under ExpoCast can be combined with HT screening (HTS) bioactivity data for the risk-based prioritization of chemicals for further evaluation. The functional role (e.g. solvent, plasticizer, fragrance) that a chemical performs can drive both the types of products in which it is found and the concentration in which it is present and therefore impacting exposure potential. However, critical chemical use information (including functional role) is lacking for the majority of commercial chemicals for which exposure estimates are needed. A suite of machine-learning based models for classifying chemicals in terms of their likely functional roles in products based on structure were developed. This effort required collection, curation, and harmonization of publically-available data sources of chemical functional use information from government and industry bodies. Physicochemical and structure descriptor data were generated for chemicals with function data. Machine-learning classifier models for function were then built in a cross-validated manner from the descriptor/function data using the method of random forests. The models were applied to: 1) predict chemi

Support vector machine regression (LS-SVM)--an alternative to artificial neural networks (ANNs) for the analysis of quantum chemistry data?

PubMed

Balabin, Roman M; Lomakina, Ekaterina I

2011-06-28

A multilayer feed-forward artificial neural network (MLP-ANN) with a single, hidden layer that contains a finite number of neurons can be regarded as a universal non-linear approximator. Today, the ANN method and linear regression (MLR) model are widely used for quantum chemistry (QC) data analysis (e.g., thermochemistry) to improve their accuracy (e.g., Gaussian G2-G4, B3LYP/B3-LYP, X1, or W1 theoretical methods). In this study, an alternative approach based on support vector machines (SVMs) is used, the least squares support vector machine (LS-SVM) regression. It has been applied to ab initio (first principle) and density functional theory (DFT) quantum chemistry data. So, QC + SVM methodology is an alternative to QC + ANN one. The task of the study was to estimate the Møller-Plesset (MPn) or DFT (B3LYP, BLYP, BMK) energies calculated with large basis sets (e.g., 6-311G(3df,3pd)) using smaller ones (6-311G, 6-311G*, 6-311G**) plus molecular descriptors. A molecular set (BRM-208) containing a total of 208 organic molecules was constructed and used for the LS-SVM training, cross-validation, and testing. MP2, MP3, MP4(DQ), MP4(SDQ), and MP4/MP4(SDTQ) ab initio methods were tested. Hartree-Fock (HF/SCF) results were also reported for comparison. Furthermore, constitutional (CD: total number of atoms and mole fractions of different atoms) and quantum-chemical (QD: HOMO-LUMO gap, dipole moment, average polarizability, and quadrupole moment) molecular descriptors were used for the building of the LS-SVM calibration model. Prediction accuracies (MADs) of 1.62 ± 0.51 and 0.85 ± 0.24 kcal mol(-1) (1 kcal mol(-1) = 4.184 kJ mol(-1)) were reached for SVM-based approximations of ab initio and DFT energies, respectively. The LS-SVM model was more accurate than the MLR model. A comparison with the artificial neural network approach shows that the accuracy of the LS-SVM method is similar to the accuracy of ANN. The extrapolation and interpolation results show that LS-SVM is superior by almost an order of magnitude over the ANN method in terms of the stability, generality, and robustness of the final model. The LS-SVM model needs a much smaller numbers of samples (a much smaller sample set) to make accurate prediction results. Potential energy surface (PES) approximations for molecular dynamics (MD) studies are discussed as a promising application for the LS-SVM calibration approach. This journal is © the Owner Societies 2011

Genotoxicity induced by metal oxide nanoparticles: a weight of evidence study and effect of particle surface and electronic properties.

PubMed

Golbamaki, Azadi; Golbamaki, Nazanin; Sizochenko, Natalia; Rasulev, Bakhtiyor; Leszczynski, Jerzy; Benfenati, Emilio

2018-06-09

The genetic toxicology of nanomaterials is a crucial toxicology issue and one of the least investigated topics. Substantially, the genotoxicity of metal oxide nanomaterials' data is resulting from in vitro comet assay. Current contributions to the genotoxicity data assessed by the comet assay provide a case-by-case evaluation of different types of metal oxides. The existing inconsistency in the literature regarding the genotoxicity testing data requires intelligent assessment strategies, such as weight of evidence evaluation. Two main tasks were performed in the present study. First, the genotoxicity data from comet assay for 16 noncoated metal oxide nanomaterials with different core composition were collected. An evaluation criterion was applied to establish which of these individual lines of evidence were of sufficient quality and what weight could have been given to them in inferring genotoxic results. The collected data were surveyed on (1) minimum necessary characterization points for nanomaterials and (2) principals of correct comet assay testing for nanomaterials. Second, in this study the genotoxicity effect of metal oxide nanomaterials was investigated by quantitative nanostructure-activity relationship approach. A set of quantum-chemical descriptors was developed for all investigated metal oxide nanomaterials. A classification model based on decision tree was developed for the investigated dataset. Thus, three descriptors were identified as the most responsible factors for genotoxicity effect: heat of formation, molecular weight, and surface area of the oxide cluster based on the conductor-like screening model. Conclusively, the proposed genotoxicity assessment strategy is useful to prioritize the study of the nanomaterials for further risk assessment evaluations.

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.

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.

Chlorination pattern effect on thermodynamic parameters and environmental degradability for C₁₀-SCCPs: Quantum chemical calculation based on virtual combinational library.

PubMed

Sun, Yuzhen; Pan, Wenxiao; Lin, Yuan; Fu, Jianjie; Zhang, Aiqian

2016-01-01

Short-chain chlorinated paraffins (SCCPs) are still controversial candidates for inclusion in the Stockholm Convention. The inherent mixture nature of SCCPs makes it rather difficult to explore their environmental behaviors. A virtual molecule library of 42,720 C10-SCCP congeners covering the full structure spectrum was constructed. We explored the structural effects on the thermodynamic parameters and environmental degradability of C10-SCCPs through semi-empirical quantum chemical calculations. The thermodynamic properties were acquired using the AM1 method, and frontier molecular orbital analysis was carried out to obtain the E(HOMO), E(LUMO) and E(LUMO)-E(HOMO) for degradability exploration at the same level. The influence of the chlorination degree (N(Cl)) on the relative stability and environmental degradation was elucidated. A novel structural descriptor, μ, was proposed to measure the dispersion of the chlorine atoms within a molecule. There were significant correlations between thermodynamic values and N(Cl), while the reported N(Cl)-dependent pollution profile of C10-SCCPs in environmental samples was basically consistent with the predicted order of formation stability of C10-SCCP congeners. In addition, isomers with large μ showed higher relative stability than those with small μ. This could be further verified by the relationship between μ and the reactivity of nucleophilic substitution and OH attack respectively. The C10-SCCP congeners with less Cl substitution and lower dispersion degree are susceptible to environmental degradation via nucleophilic substitution and hydroxyl radical attack, while direct photolysis of C10-SCCP congeners cannot readily occur due to the large E(LUMO)-E(HOMO) values. The chlorination effect and the conclusions were further checked with appropriate density functional theory (DFT) calculations. Copyright © 2015. Published by Elsevier B.V.

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.

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...

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.

Adsorption and possible dissociation of glucose by the [BN fullerene-B6]- magnetic nanocomposite. In silico studies

NASA Astrophysics Data System (ADS)

Anota, E. Chigo; Villanueva, M. Salazar; Shakerzadeh, E.; Castro, M.

2018-02-01

The adsorption, activation and possible dissociation of the glucose molecule on the magnetic [BN fullerene-B6]- system is performed by means of density functional theory calculations. Three models of magnetic nanocomposites were inspected: i) pristine BN fullerene, BN fullerene functionalized with a magnetic B6 cluster which generates two structures: ii) pyramidal (P) and iii) triangular (T). Chemical interactions of glucose appear for all these cases; however, for the BNF:B6(T)—glucose system, the interaction generates an effect of dissociation on glucose, due to the magnetic effects, since it has high spin multiplicity. The latter nanocomposite shows electronic behavior like-conductor and like-semi-conductor for the P and T geometries, respectively. Intrinsic magnetism associated to values of 1.0 magneton bohr (µB) for the pyramidal and 5.0 µB for the triangular structure, high polarity, and low-chemical reactivity are found for these systems. These interesting properties make these functionalized fullerenes a good option for being used as nano-vehicles for drug delivery. These quantum descriptors remain invariant when the [BN]-fullerene and [BNF:B6 (P) or (T)]- nanocomposites are interacting with the glucose molecule. According to the determined adsorption energy, chemisorption regimes occur in both the phases: gas and aqueous medium.

Quantitative structure--property relationships for enhancing predictions of synthetic organic chemical removal from drinking water by granular activated carbon.

PubMed

Magnuson, Matthew L; Speth, Thomas F

2005-10-01

Granular activated carbon is a frequently explored technology for removing synthetic organic contaminants from drinking water sources. The success of this technology relies on a number of factors based not only on the adsorptive properties of the contaminant but also on properties of the water itself, notably the presence of substances in the water which compete for adsorption sites. Because it is impractical to perform field-scale evaluations for all possible contaminants, the pore surface diffusion model (PSDM) has been developed and used to predict activated carbon column performance using single-solute isotherm data as inputs. Many assumptions are built into this model to account for kinetics of adsorption and competition for adsorption sites. This work further evaluates and expands this model, through the use of quantitative structure-property relationships (QSPRs) to predict the effect of natural organic matter fouling on activated carbon adsorption of specific contaminants. The QSPRs developed are based on a combination of calculated topographical indices and quantum chemical parameters. The QSPRs were evaluated in terms of their statistical predictive ability,the physical significance of the descriptors, and by comparison with field data. The QSPR-enhanced PSDM was judged to give results better than what could previously be obtained.

Computational study of AuSi{sub n} (n=1-9) nanoalloy clusters invoking DFT based descriptors

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

Ranjan, Prabhat; Kumar, Ajay; Chakraborty, Tanmoy, E-mail: tanmoy.chakraborty@jaipur.manipal.edu, E-mail: tanmoychem@gmail.com

2016-04-13

Nanoalloy clusters formed between Au and Si are topics of great interest today from both scientific and technological point of view. Due to its remarkable catalytic, electronic, mechanical and magnetic properties Au-Si nanoalloy clusters have extensive applications in the field of microelectronics, catalysis, biomedicine, and jewelry industry. Density Functional Theory (DFT) is a new paradigm of quantum mechanics, which is very much popular to study the electronic properties of materials. Conceptual DFT based descriptors have been invoked to correlate the experimental properties of nanoalloy clusters. In this venture, we have systematically investigated AuSi{sub n} (n=1-9) nanoalloy clusters in the theoreticalmore » frame of the B3LYP exchange correlation. The experimental properties of AuSi{sub n} (n=1-9) nanoalloy clusters are correlated in terms of DFT based descriptors viz. HOMO-LUMO gap, Electronegativity (χ), Global Hardness (η), Global Softness (S) and Electrophilicity Index (ω). The calculated HOMO-LUMO gap exhibits interesting odd-even alteration behaviour, indicating that even numbered clusters possess higher stability as compare to their neighbour odd numbered clusters. This study also reflects a very well agreement between experimental bond length and computed data.« less

Informing the Human Plasma Protein Binding of ...

EPA Pesticide Factsheets

The free fraction of a xenobiotic in plasma (Fub) is an important determinant of chemical adsorption, distribution, metabolism, elimination, and toxicity, yet experimental plasma protein binding data is scarce for environmentally relevant chemicals. The presented work explores the merit of utilizing available pharmaceutical data to predict Fub for environmentally relevant chemicals via machine learning techniques. Quantitative structure-activity relationship (QSAR) models were constructed with k nearest neighbors (kNN), support vector machines (SVM), and random forest (RF) machine learning algorithms from a training set of 1045 pharmaceuticals. The models were then evaluated with independent test sets of pharmaceuticals (200 compounds) and environmentally relevant ToxCast chemicals (406 total, in two groups of 238 and 168 compounds). The selection of a minimal feature set of 10-15 2D molecular descriptors allowed for both informative feature interpretation and practical applicability domain assessment via a bounded box of descriptor ranges and principal component analysis. The diverse pharmaceutical and environmental chemical sets exhibit similarities in terms of chemical space (99-82% overlap), as well as comparable bias and variance in constructed learning curves. All the models exhibit significant predictability with mean absolute errors (MAE) in the range of 0.10-0.18 Fub. The models performed best for highly bound chemicals (MAE 0.07-0.12), neutrals (MAE 0

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.

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.

A general representation scheme for crystalline solids based on Voronoi-tessellation real feature values and atomic property data

PubMed Central

Jalem, Randy; Nakayama, Masanobu; Noda, Yusuke; Le, Tam; Takeuchi, Ichiro; Tateyama, Yoshitaka; Yamazaki, Hisatsugu

2018-01-01

Abstract Increasing attention has been paid to materials informatics approaches that promise efficient and fast discovery and optimization of functional inorganic materials. Technical breakthrough is urgently requested to advance this field and efforts have been made in the development of materials descriptors to encode or represent characteristics of crystalline solids, such as chemical composition, crystal structure, electronic structure, etc. We propose a general representation scheme for crystalline solids that lifts restrictions on atom ordering, cell periodicity, and system cell size based on structural descriptors of directly binned Voronoi-tessellation real feature values and atomic/chemical descriptors based on the electronegativity of elements in the crystal. Comparison was made vs. radial distribution function (RDF) feature vector, in terms of predictive accuracy on density functional theory (DFT) material properties: cohesive energy (CE), density (d), electronic band gap (BG), and decomposition energy (Ed). It was confirmed that the proposed feature vector from Voronoi real value binning generally outperforms the RDF-based one for the prediction of aforementioned properties. Together with electronegativity-based features, Voronoi-tessellation features from a given crystal structure that are derived from second-nearest neighbor information contribute significantly towards prediction. PMID:29707064

A general representation scheme for crystalline solids based on Voronoi-tessellation real feature values and atomic property data.

PubMed

Jalem, Randy; Nakayama, Masanobu; Noda, Yusuke; Le, Tam; Takeuchi, Ichiro; Tateyama, Yoshitaka; Yamazaki, Hisatsugu

2018-01-01

Increasing attention has been paid to materials informatics approaches that promise efficient and fast discovery and optimization of functional inorganic materials. Technical breakthrough is urgently requested to advance this field and efforts have been made in the development of materials descriptors to encode or represent characteristics of crystalline solids, such as chemical composition, crystal structure, electronic structure, etc. We propose a general representation scheme for crystalline solids that lifts restrictions on atom ordering, cell periodicity, and system cell size based on structural descriptors of directly binned Voronoi-tessellation real feature values and atomic/chemical descriptors based on the electronegativity of elements in the crystal. Comparison was made vs. radial distribution function (RDF) feature vector, in terms of predictive accuracy on density functional theory (DFT) material properties: cohesive energy (CE), density ( d ), electronic band gap (BG), and decomposition energy (Ed). It was confirmed that the proposed feature vector from Voronoi real value binning generally outperforms the RDF-based one for the prediction of aforementioned properties. Together with electronegativity-based features, Voronoi-tessellation features from a given crystal structure that are derived from second-nearest neighbor information contribute significantly towards prediction.

Use of in Vitro HTS-Derived Concentration-Response Data as Biological Descriptors Improves the Accuracy of QSAR Models of in Vivo Toxicity

EPA Science Inventory

Background: Quantitative high-throughput screening (qHTS) assays are increasingly being employed to inform chemical hazard identification. Hundreds of chemicals have been tested in dozens of cell lines across extensive concentration ranges by the National Toxicology Program in co...

Incorporating Biological, Chemical and Toxicological Knowledge into Predictive Models of Toxicity: Letter to the Editor

EPA Science Inventory

Thomas et al. (2012) recently published an evaluation of statistical models for classifying in vivo toxicity endpoints from ToxRefDB (Knudsen et al. 2009; Martin et al. 2009a and 2009b) using ToxCast in vitro bioactivity data (Judson et al. 2010) and chemical structure descriptor...

Modeling the Biodegradability of Chemical Compounds Using the Online CHEmical Modeling Environment (OCHEM)

PubMed Central

Vorberg, Susann

2013-01-01

Abstract Biodegradability describes the capacity of substances to be mineralized by free‐living bacteria. It is a crucial property in estimating a compound’s long‐term impact on the environment. The ability to reliably predict biodegradability would reduce the need for laborious experimental testing. However, this endpoint is difficult to model due to unavailability or inconsistency of experimental data. Our approach makes use of the Online Chemical Modeling Environment (OCHEM) and its rich supply of machine learning methods and descriptor sets to build classification models for ready biodegradability. These models were analyzed to determine the relationship between characteristic structural properties and biodegradation activity. The distinguishing feature of the developed models is their ability to estimate the accuracy of prediction for each individual compound. The models developed using seven individual descriptor sets were combined in a consensus model, which provided the highest accuracy. The identified overrepresented structural fragments can be used by chemists to improve the biodegradability of new chemical compounds. The consensus model, the datasets used, and the calculated structural fragments are publicly available at http://ochem.eu/article/31660. PMID:27485201

Alkylation of enolates: An electrophilicity perspective

NASA Astrophysics Data System (ADS)

Elango, M.; Parthasarathi, R.; Subramanian, V.; Chattaraj, P. K.

Enolates are ambient nucleophiles, and alkylation can occur either at a carbon or at an oxygen site. It is known that the ratio of C/O alkylation depends significantly on various factors, including the type of enolate, alkylating agent, site of alkylation, and solvent environment. Analysis of regioselectivity and solvent effects on alkylation of lithium enolates is investigated using various reactivity descriptors, including generalized philicity. These results point out the reliability of both global and local reactivity descriptors in providing significant information about site selectivity and chemical reactivity of lithium enolates.

Interrelationships among carcinogenicity, mutagenicity, acute toxicity, and chemical structure in a genotoxicity data base

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

Benigni, R.; Andreoli, C.; Giuliani, A.

1989-01-01

The interrelationships among carcinogenicity, mutagenicity, acute toxicity (LD50), and a number of molecular descriptors were studied by computerized data analysis methods on the data base generated by the International Program for the Evaluation of Short-Term Test for Carcinogens (IPESTTC). With the use of statistical regression methods, three main associations were evidenced: (1) the well-known correlation between carcinogenicity and mutagenicity; (2) a correlation between mutagenicity and toxicity (LD50 ip in mice); and (3) a correlation between toxicity and a recently introduced estimator of the free energy of binding of the molecules to biological receptors. As expected on the basis of themore » large variety of chemical classes represented in the IPESTTC data base, no simple relationship between mutagenicity or carcinogenicity and chemical descriptors was found. To overcome this problem, a new pattern recognition method (REPAD), developed by us for structure-activity studies of noncongeneric chemicals, has been used. This allowed us to highlight a significant difference between the whole patterns of relationships among chemicophysical variables in the two groups to active (mutagenicity and/or carcinogenic) and inactive chemicals. This approach generated a classification rule able to correctly assign about 80% of carcinogens or mutagens.« less

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

Complex Chemical Reaction Networks from Heuristics-Aided Quantum Chemistry.

PubMed

Rappoport, Dmitrij; Galvin, Cooper J; Zubarev, Dmitry Yu; Aspuru-Guzik, Alán

2014-03-11

While structures and reactivities of many small molecules can be computed efficiently and accurately using quantum chemical methods, heuristic approaches remain essential for modeling complex structures and large-scale chemical systems. Here, we present a heuristics-aided quantum chemical methodology applicable to complex chemical reaction networks such as those arising in cell metabolism and prebiotic chemistry. Chemical heuristics offer an expedient way of traversing high-dimensional reactive potential energy surfaces and are combined here with quantum chemical structure optimizations, which yield the structures and energies of the reaction intermediates and products. Application of heuristics-aided quantum chemical methodology to the formose reaction reproduces the experimentally observed reaction products, major reaction pathways, and autocatalytic cycles.

Descriptors of Oxygen-Evolution Activity for Oxides: A Statistical Evaluation

DOE PAGES

Hong, Wesley T.; Welsch, Roy E.; Shao-Horn, Yang

2015-12-16

Catalysts for oxygen electrochemical processes are critical for the commercial viability of renewable energy storage and conversion devices such as fuel cells, artificial photosynthesis, and metal-air batteries. Transition metal oxides are an excellent system for developing scalable, non-noble-metal-based catalysts, especially for the oxygen evolution reaction (OER). Central to the rational design of novel catalysts is the development of quantitative structure-activity relation-ships, which correlate the desired catalytic behavior to structural and/or elemental descriptors of materials. The ultimate goal is to use these relationships to guide materials design. In this study, 101 intrinsic OER activities of 51 perovskites were compiled from fivemore » studies in literature and additional measurements made for this work. We explored the behavior and performance of 14 descriptors of the metal-oxygen bond strength using a number of statistical approaches, including factor analysis and linear regression models. We found that these descriptors can be classified into five descriptor families and identify electron occupancy and metal-oxygen covalency as the dominant influences on the OER activity. However, multiple descriptors still need to be considered in order to develop strong predictive relationships, largely outperforming the use of only one or two descriptors (as conventionally done in the field). Here, we confirmed that the number of d electrons, charge-transfer energy (covalency), and optimality of eg occupancy play the important roles, but found that structural factors such as M-O-M bond angle and tolerance factor are relevant as well. With these tools, we demonstrate how statistical learning can be used to draw novel physical insights and combined with data mining to rapidly screen OER electrocatalysts across a wide chemical space.« less

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

Cuevas, F.A.; Curilef, S., E-mail: scurilef@ucn.cl; Plastino, A.R., E-mail: arplastino@ugr.es

The spread of a wave-packet (or its deformation) is a very important topic in quantum mechanics. Understanding this phenomenon is relevant in connection with the study of diverse physical systems. In this paper we apply various 'spreading measures' to characterize the evolution of an initially localized wave-packet in a tight-binding lattice, with special emphasis on information-theoretical measures. We investigate the behavior of both the probability distribution associated with the wave packet and the concomitant probability current. Complexity measures based upon Renyi entropies appear to be particularly good descriptors of the details of the delocalization process. - Highlights: > Spread ofmore » highly localized wave-packet in the tight-binding lattice. > Entropic and information-theoretical characterization is used to understand the delocalization. > The behavior of both the probability distribution and the concomitant probability current is investigated. > Renyi entropies appear to be good descriptors of the details of the delocalization process.« less

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.

Considering ionic state in modeling sorption of pharmaceuticals to sewage sludge.

PubMed

Rybacka, Aleksandra; Andersson, Patrik L

2016-12-01

Information on the partitioning of chemicals between particulate matter and water in sewage treatment plants (STPs) can be used to predict their subsequent environmental fate. However, this information can be challenging to acquire, especially for pharmaceuticals that are frequently present in ionized forms. This study investigated the relationship between the ionization state of active pharmaceutical ingredients (APIs) and their partitioning between water and sludge in STPs. We also investigated the underlying mechanisms of sludge sorption by using chemical descriptors based on ionized structures, and evaluated the usefulness of these descriptors in quantitative structure-property relationship (QSPR) modeling. K D values were collected for 110 APIs, which were classified as neutral, positive, or negative at pH 7. The models with the highest performance had the R 2 Y and Q 2 values of above 0.75 and 0.65, respectively. We found that the dominant intermolecular forces governing the interactions of neutral and positively charged APIs with sludge are hydrophobic, pi-pi, and dipole-dipole interactions, whereas the interactions of negatively charged APIs with sludge were mainly governed by covalent bonding as well as ion-ion, ion-dipole, and dipole-dipole interactions; hydrophobicity-driven interactions were rather unimportant. Including charge-related descriptors improved the models' performance by 5-10%, underlining the importance of electrostatic interactions. The use of descriptors calculated for ionized structures did not improve the model statistics for positive and negative APIs, but slightly increased model performance for neutral APIs. We attribute this to a better description of neutral zwitterions. Copyright © 2016 Elsevier Ltd. All rights reserved.

Determination and prediction of octanol-air partition coefficients of hydroxylated and methoxylated polybrominated diphenyl ethers.

PubMed

Zhao, Hongxia; Xie, Qing; Tan, Feng; Chen, Jingwen; Quan, Xie; Qu, Baocheng; Zhang, Xin; Li, Xiaona

2010-07-01

The octanol-air partition coefficient (K(OA)) of 19 hydroxylated polybrominated diphenyl ethers (OH-PBDEs) and 10 methoxylated polybrominated diphenyl ethers (MeO-PBDEs) were measured as a function of temperature using a gas chromatographic retention time technique. At room temperature (298.15K), log K(OA) ranged from 8.30 for monobrominated OH/MeO-PBDEs to 13.29 for hexabrominated OH/MeO-PBDEs. The internal energies of phase change from octanol to air (Delta(OA)U) for 29 OH/MeO-PBDE congeners ranged from 72 to 126 kJ mol(-1). Using partial least-squares (PLS) analysis, a statistically quantitative structure-property relationship (QSPR) model for logK(OA) of OH/MeO-PBDE congeners was developed based on the 16 fundamental quantum chemical descriptors computed by PM3 Hamiltonian, for which the Q(cum)(2) was about 0.937. The molecular weight (Mw) and energy of the lowest unoccupied molecular orbital (E(LUMO)) were found to be main factors governing the log K(OA). 2010 Elsevier Ltd. All rights reserved.

Using biological effects tools to define Good Environmental Status under the European Union Marine Strategy Framework Directive.

PubMed

Lyons, B P; Thain, J E; Stentiford, G D; Hylland, K; Davies, I M; Vethaak, A D

2010-10-01

The use of biological effects tools offer enormous potential to meet the challenges outlined by the European Union Marine Strategy Framework Directive (MSFD) whereby Member States are required to develop a robust set of tools for defining 11 qualitative descriptors of Good Environmental Status (GES), such as demonstrating that "Concentrations of contaminants are at levels not giving rise to pollution effects" (GES Descriptor 8). This paper discusses the combined approach of monitoring chemical contaminant levels, along side biological effect measurements relating to the effect of pollutants, for undertaking assessments of GES across European marine regions. We outline the minimum standards that biological effects tools should meet if they are to be used for defining GES in relation to Descriptor 8 and describe the current international initiatives underway to develop assessment criteria for these biological effects techniques. Crown Copyright © 2010. Published by Elsevier Ltd. All rights reserved.

Uncovering the Geometry of Barrierless Reactions Using Lagrangian Descriptors.

PubMed

Junginger, Andrej; Hernandez, Rigoberto

2016-03-03

Transition-state theories describing barrierless chemical reactions, or more general activated problems, are often hampered by the lack of a saddle around which the dividing surface can be constructed. For example, the time-dependent transition-state trajectory uncovering the nonrecrossing dividing surface in thermal reactions in the framework of the Langevin equation has relied on perturbative approaches in the vicinity of the saddle. We recently obtained an alternative approach using Lagrangian descriptors to construct time-dependent and recrossing-free dividing surfaces. This is a nonperturbative approach making no reference to a putative saddle. Here we show how the Lagrangian descriptor can be used to obtain the transition-state geometry of a dissipated and thermalized reaction across barrierless potentials. We illustrate the method in the case of a 1D Brownian motion for both barrierless and step potentials; however, the method is not restricted and can be directly applied to different kinds of potentials and higher dimensional systems.

Using DFT methodology for more reliable predictive models: Design of inhibitors of Golgi α-Mannosidase II.

PubMed

Bobovská, Adela; Tvaroška, Igor; Kóňa, Juraj

2016-05-01

Human Golgi α-mannosidase II (GMII), a zinc ion co-factor dependent glycoside hydrolase (E.C.3.2.1.114), is a pharmaceutical target for the design of inhibitors with anti-cancer activity. The discovery of an effective inhibitor is complicated by the fact that all known potent inhibitors of GMII are involved in unwanted co-inhibition with lysosomal α-mannosidase (LMan, E.C.3.2.1.24), a relative to GMII. Routine empirical QSAR models for both GMII and LMan did not work with a required accuracy. Therefore, we have developed a fast computational protocol to build predictive models combining interaction energy descriptors from an empirical docking scoring function (Glide-Schrödinger), Linear Interaction Energy (LIE) method, and quantum mechanical density functional theory (QM-DFT) calculations. The QSAR models were built and validated with a library of structurally diverse GMII and LMan inhibitors and non-active compounds. A critical role of QM-DFT descriptors for the more accurate prediction abilities of the models is demonstrated. The predictive ability of the models was significantly improved when going from the empirical docking scoring function to mixed empirical-QM-DFT QSAR models (Q(2)=0.78-0.86 when cross-validation procedures were carried out; and R(2)=0.81-0.83 for a testing set). The average error for the predicted ΔGbind decreased to 0.8-1.1kcalmol(-1). Also, 76-80% of non-active compounds were successfully filtered out from GMII and LMan inhibitors. The QSAR models with the fragmented QM-DFT descriptors may find a useful application in structure-based drug design where pure empirical and force field methods reached their limits and where quantum mechanics effects are critical for ligand-receptor interactions. The optimized models will apply in lead optimization processes for GMII drug developments. Copyright © 2016 Elsevier Inc. All rights reserved.

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

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.

Predictive Modeling of Chemical Hazard by Integrating Numerical Descriptors of Chemical Structures and Short-term Toxicity Assay Data

PubMed Central

Rusyn, Ivan; Sedykh, Alexander; Guyton, Kathryn Z.; Tropsha, Alexander

2012-01-01

Quantitative structure-activity relationship (QSAR) models are widely used for in silico prediction of in vivo toxicity of drug candidates or environmental chemicals, adding value to candidate selection in drug development or in a search for less hazardous and more sustainable alternatives for chemicals in commerce. The development of traditional QSAR models is enabled by numerical descriptors representing the inherent chemical properties that can be easily defined for any number of molecules; however, traditional QSAR models often have limited predictive power due to the lack of data and complexity of in vivo endpoints. Although it has been indeed difficult to obtain experimentally derived toxicity data on a large number of chemicals in the past, the results of quantitative in vitro screening of thousands of environmental chemicals in hundreds of experimental systems are now available and continue to accumulate. In addition, publicly accessible toxicogenomics data collected on hundreds of chemicals provide another dimension of molecular information that is potentially useful for predictive toxicity modeling. These new characteristics of molecular bioactivity arising from short-term biological assays, i.e., in vitro screening and/or in vivo toxicogenomics data can now be exploited in combination with chemical structural information to generate hybrid QSAR–like quantitative models to predict human toxicity and carcinogenicity. Using several case studies, we illustrate the benefits of a hybrid modeling approach, namely improvements in the accuracy of models, enhanced interpretation of the most predictive features, and expanded applicability domain for wider chemical space coverage. PMID:22387746

Impact of volatile composition on the sensorial attributes of dried paprikas.

PubMed

Martín, Alberto; Hernández, Alejandro; Aranda, Emilio; Casquete, Rocio; Velázquez, Rocio; Bartolomé, Teresa; Córdoba, María G

2017-10-01

Here we characterised the aroma of smoked, oven-dried, and sun-dried paprikas by sensorial evaluation and analysis of their volatile profiles. The sensorial panel defined smoked paprikas as having an intense, persistent, smoked odour and flavour and the highest acceptability. The oven-dried paprikas had a fruity odour and flavour related with aroma notes to fresh peppers. The sun-dried paprikas were associated with straw aromas and the worse valued. The chemical classes of volatile compounds also defined the paprika types. The smoked paprikas were richer in alcohols, phenols, pyrroles, and pyranones, whereas the oven-dried samples were characterised by their aldehydes and terpenes. The sun-dried paprikas had significantly lower amounts of odorant substances than the smoked and oven-dried paprikas. The intensity, persistence and smokiness descriptors (associated with smoked paprika) were positively associated with phenols and alcohols. Aldehydes were positively correlated with a fruity descriptor, which defined oven-dried paprikas, and negatively correlated with intensity, persistence, smokiness, toasted, and dried pepper descriptors. The descriptor straw, which defined sun-dried paprikas, was negatively correlated with alcohols, phenols, furans, and pyrroles. Copyright © 2017 Elsevier Ltd. All rights reserved.

New antitrichomonal drug-like chemicals selected by bond (edge)-based TOMOCOMD-CARDD descriptors.

PubMed

Meneses-Marcel, Alfredo; Rivera-Borroto, Oscar M; Marrero-Ponce, Yovani; Montero, Alina; Machado Tugores, Yanetsy; Escario, José Antonio; Gómez Barrio, Alicia; Montero Pereira, David; Nogal, Juan José; Kouznetsov, Vladimir V; Ochoa Puentes, Cristian; Bohórquez, Arnold R; Grau, Ricardo; Torrens, Francisco; Ibarra-Velarde, Froylán; Arán, Vicente J

2008-09-01

Bond-based quadratic indices, new TOMOCOMD-CARDD molecular descriptors, and linear discriminant analysis (LDA) were used to discover novel lead trichomonacidals. The obtained LDA-based quantitative structure-activity relationships (QSAR) models, using nonstochastic and stochastic indices, were able to classify correctly 87.91% (87.50%) and 89.01% (84.38%) of the chemicals in training (test) sets, respectively. They showed large Matthews correlation coefficients of 0.75 (0.71) and 0.78 (0.65) for the training (test) sets, correspondingly. Later, both models were applied to the virtual screening of 21 chemicals to find new lead antitrichomonal agents. Predictions agreed with experimental results to a great extent because a correct classification for both models of 95.24% (20 of 21) of the chemicals was obtained. Of the 21 compounds that were screened and synthesized, 2 molecules (chemicals G-1, UC-245) showed high to moderate cytocidal activity at the concentration of 10 microg/ml, another 2 compounds (G-0 and CRIS-148) showed high cytocidal activity only at the concentration of 100 microg/ml, and the remaining chemicals (from CRIS-105 to CRIS-153, except CRIS-148) were inactive at these assayed concentrations. Finally, the best candidate, G-1 (cytocidal activity of 100% at 10 microg/ml) was in vivo assayed in ovariectomized Wistar rats achieving promising results as a trichomonacidal drug-like compound.

Development of estrogen receptor beta binding prediction model using large sets of chemicals.

PubMed

Sakkiah, Sugunadevi; Selvaraj, Chandrabose; Gong, Ping; Zhang, Chaoyang; Tong, Weida; Hong, Huixiao

2017-11-03

We developed an ER β binding prediction model to facilitate identification of chemicals specifically bind ER β or ER α together with our previously developed ER α binding model. Decision Forest was used to train ER β binding prediction model based on a large set of compounds obtained from EADB. Model performance was estimated through 1000 iterations of 5-fold cross validations. Prediction confidence was analyzed using predictions from the cross validations. Informative chemical features for ER β binding were identified through analysis of the frequency data of chemical descriptors used in the models in the 5-fold cross validations. 1000 permutations were conducted to assess the chance correlation. The average accuracy of 5-fold cross validations was 93.14% with a standard deviation of 0.64%. Prediction confidence analysis indicated that the higher the prediction confidence the more accurate the predictions. Permutation testing results revealed that the prediction model is unlikely generated by chance. Eighteen informative descriptors were identified to be important to ER β binding prediction. Application of the prediction model to the data from ToxCast project yielded very high sensitivity of 90-92%. Our results demonstrated ER β binding of chemicals could be accurately predicted using the developed model. Coupling with our previously developed ER α prediction model, this model could be expected to facilitate drug development through identification of chemicals that specifically bind ER β or ER α .

Quantitative structure-activity relationship for the partition coefficient of hydrophobic compounds between silicone oil and air.

PubMed

Qu, Yanfei; Ma, Yongwen; Wan, Jinquan; Wang, Yan

2018-06-01

The silicon oil-air partition coefficients (K SiO/A ) of hydrophobic compounds are vital parameters for applying silicone oil as non-aqueous-phase liquid in partitioning bioreactors. Due to the limited number of K SiO/A values determined by experiment for hydrophobic compounds, there is an urgent need to model the K SiO/A values for unknown chemicals. In the present study, we developed a universal quantitative structure-activity relationship (QSAR) model using a sequential approach with macro-constitutional and micromolecular descriptors for silicone oil-air partition coefficients (K SiO/A ) of hydrophobic compounds with large structural variance. The geometry optimization and vibrational frequencies of each chemical were calculated using the hybrid density functional theory at the B3LYP/6-311G** level. Several quantum chemical parameters that reflect various intermolecular interactions as well as hydrophobicity were selected to develop QSAR model. The result indicates that a regression model derived from logK SiO/A , the number of non-hydrogen atoms (#nonHatoms) and energy gap of E LUMO and E HOMO (E LUMO -E HOMO ) could explain the partitioning mechanism of hydrophobic compounds between silicone oil and air. The correlation coefficient R 2 of the model is 0.922, and the internal and external validation coefficient, Q 2 LOO and Q 2 ext , are 0.91 and 0.89 respectively, implying that the model has satisfactory goodness-of-fit, robustness, and predictive ability and thus provides a robust predictive tool to estimate the logK SiO/A values for chemicals in application domain. The applicability domain of the model was visualized by the Williams plot.

Ab initio Quantum Chemical and Experimental Reaction Kinetics Studies in the Combustion of Bipropellants

DTIC Science & Technology

2017-03-24

NUMBER (Include area code) 24 March 2017 Briefing Charts 01 March 2017 - 31 March 2017 Ab initio Quantum Chemical and Experimental Reaction Kinetics...Laboratory AFRL/RQRS 1 Ara Road Edwards AFB, CA 93524 *Email: ghanshyam.vaghjiani@us.af.mil Ab initio Quantum Chemical and Experimental Reaction ...Clearance 17161 Zador et al., Prog. Energ. Combust. Sci., 37 371 (2011) Why Quantum Chemical Reaction Kinetics Studies? DISTRIBUTION A: Approved for

Quantum indistinguishability in chemical reactions.

PubMed

Fisher, Matthew P A; Radzihovsky, Leo

2018-05-15

Quantum indistinguishability plays a crucial role in many low-energy physical phenomena, from quantum fluids to molecular spectroscopy. It is, however, typically ignored in most high-temperature processes, particularly for ionic coordinates, implicitly assumed to be distinguishable, incoherent, and thus well approximated classically. We explore enzymatic chemical reactions involving small symmetric molecules and argue that in many situations a full quantum treatment of collective nuclear degrees of freedom is essential. Supported by several physical arguments, we conjecture a "quantum dynamical selection" (QDS) rule for small symmetric molecules that precludes chemical processes that involve direct transitions from orbitally nonsymmetric molecular states. As we propose and discuss, the implications of the QDS rule include ( i ) a differential chemical reactivity of para- and orthohydrogen, ( ii ) a mechanism for inducing intermolecular quantum entanglement of nuclear spins, ( iii ) a mass-independent isotope fractionation mechanism, ( iv ) an explanation of the enhanced chemical activity of "reactive oxygen species", ( v ) illuminating the importance of ortho-water molecules in modulating the quantum dynamics of liquid water, and ( vi ) providing the critical quantum-to-biochemical linkage in the nuclear spin model of the (putative) quantum brain, among others.

The underlying toxicological mechanism of chemical mixtures: A case study on mixture toxicity of cyanogenic toxicants and aldehydes to Photobacterium phosphoreum

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

Tian, Dayong; Department of Chemical and Environmental Engineering, Anyang Institute of Technology, Anyang 455000; Lin, Zhifen, E-mail: lzhifen@tongji.edu.cn

Intracellular chemical reaction of chemical mixtures is one of the main reasons that cause synergistic or antagonistic effects. However, it still remains unclear what the influencing factors on the intracellular chemical reaction are, and how they influence on the toxicological mechanism of chemical mixtures. To reveal this underlying toxicological mechanism of chemical mixtures, a case study on mixture toxicity of cyanogenic toxicants and aldehydes to Photobacterium phosphoreum was employed, and both their joint effects and mixture toxicity were observed. Then series of two-step linear regressions were performed to describe the relationships between joint effects, the expected additive toxicities and descriptorsmore » of individual chemicals (including concentrations, binding affinity to receptors, octanol/water partition coefficients). Based on the quantitative relationships, the underlying joint toxicological mechanisms were revealed. The result shows that, for mixtures with their joint effects resulting from intracellular chemical reaction, their underlying toxicological mechanism depends on not only their interaction with target proteins, but also their transmembrane actions and their concentrations. In addition, two generic points of toxicological mechanism were proposed including the influencing factors on intracellular chemical reaction and the difference of the toxicological mechanism between single reactive chemicals and their mixtures. This study provided an insight into the understanding of the underlying toxicological mechanism for chemical mixtures with intracellular chemical reaction. - Highlights: • Joint effects of nitriles and aldehydes at non-equitoxic ratios were determined. • A novel descriptor, ligand–receptor interaction energy (E{sub binding}), was employed. • Quantitative relationships for mixtures were developed based on a novel descriptor. • The underlying toxic mechanism was revealed based on quantitative relationships. • Two generic points of toxicological mechanism were elucidated.« less

A Bayesian network model for predicting aquatic toxicity mode ...

EPA Pesticide Factsheets

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 published dataset containing over one thousand chemicals with MoA assignments for aquatic animal toxicity. Two dimensional theoretical chemical descriptors were generated for each chemical using the Toxicity Estimation Software Tool. The model was developed through augmented Markov blanket discovery from the dataset of 1098 chemicals with the MoA broad classifications as a target node. From cross validation, the overall precision for the model was 80.2%. The best precision was for the AChEI MoA (93.5%) where 257 chemicals out of 275 were correctly classified. Model precision was poorest for the reactivity MoA (48.5%) where 48 out of 99 reactive chemicals were correctly classified. Narcosis represented the largest class within the MoA dataset and had a precision and reliability of 80.0%, reflecting the global precision across all of the MoAs. False negatives for narcosis most often fell into electron transport inhibition, neurotoxicity or reactivity MoAs. False negatives for all other MoAs were most often narcosis. A probabilistic sensitivity analysis was undertaken for each MoA to examine the sensitivity to individual and multiple descriptor findings. The results show that the Markov blank

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.

Diabatic models with transferrable parameters for generalized chemical reactions

NASA Astrophysics Data System (ADS)

Reimers, Jeffrey R.; McKemmish, Laura K.; McKenzie, Ross H.; Hush, Noel S.

2017-05-01

Diabatic models applied to adiabatic electron-transfer theory yield many equations involving just a few parameters that connect ground-state geometries and vibration frequencies to excited-state transition energies and vibration frequencies to the rate constants for electron-transfer reactions, utilizing properties of the conical-intersection seam linking the ground and excited states through the Pseudo Jahn-Teller effect. We review how such simplicity in basic understanding can also be obtained for general chemical reactions. The key feature that must be recognized is that electron-transfer (or hole transfer) processes typically involve one electron (hole) moving between two orbitals, whereas general reactions typically involve two electrons or even four electrons for processes in aromatic molecules. Each additional moving electron leads to new high-energy but interrelated conical-intersection seams that distort the shape of the critical lowest-energy seam. Recognizing this feature shows how conical-intersection descriptors can be transferred between systems, and how general chemical reactions can be compared using the same set of simple parameters. Mathematical relationships are presented depicting how different conical-intersection seams relate to each other, showing that complex problems can be reduced into an effective interaction between the ground-state and a critical excited state to provide the first semi-quantitative implementation of Shaik’s “twin state” concept. Applications are made (i) demonstrating why the chemistry of the first-row elements is qualitatively so different to that of the second and later rows, (ii) deducing the bond-length alternation in hypothetical cyclohexatriene from the observed UV spectroscopy of benzene, (iii) demonstrating that commonly used procedures for modelling surface hopping based on inclusion of only the first-derivative correction to the Born-Oppenheimer approximation are valid in no region of the chemical parameter space, and (iv), demonstrating the types of chemical reactions that may be suitable for exploitation as a chemical qubit in some quantum information processor.

Simulating chemistry using quantum computers.

PubMed

Kassal, Ivan; Whitfield, James D; Perdomo-Ortiz, Alejandro; Yung, Man-Hong; Aspuru-Guzik, Alán

2011-01-01

The difficulty of simulating quantum systems, well known to quantum chemists, prompted the idea of quantum computation. One can avoid the steep scaling associated with the exact simulation of increasingly large quantum systems on conventional computers, by mapping the quantum system to another, more controllable one. In this review, we discuss to what extent the ideas in quantum computation, now a well-established field, have been applied to chemical problems. We describe algorithms that achieve significant advantages for the electronic-structure problem, the simulation of chemical dynamics, protein folding, and other tasks. Although theory is still ahead of experiment, we outline recent advances that have led to the first chemical calculations on small quantum information processors.

Are the Chemical Structures in your QSAR Correct?

EPA Science Inventory

Quantitative structure-activity relationships (QSARs) are used to predict many different endpoints, utilize hundreds and even thousands of different parameters (or descriptors), and are created using a variety of approaches. The one thing they all have in common is the assumptio...

Novel Uses of In Vitro Data to Develop Quantitative Biological Activity Relationship Models for in Vivo Carcinogenicity Prediction.

PubMed

Pradeep, Prachi; Povinelli, Richard J; Merrill, Stephen J; Bozdag, Serdar; Sem, Daniel S

2015-04-01

The availability of large in vitro datasets enables better insight into the mode of action of chemicals and better identification of potential mechanism(s) of toxicity. Several studies have shown that not all in vitro assays can contribute as equal predictors of in vivo carcinogenicity for development of hybrid Quantitative Structure Activity Relationship (QSAR) models. We propose two novel approaches for the use of mechanistically relevant in vitro assay data in the identification of relevant biological descriptors and development of Quantitative Biological Activity Relationship (QBAR) models for carcinogenicity prediction. We demonstrate that in vitro assay data can be used to develop QBAR models for in vivo carcinogenicity prediction via two case studies corroborated with firm scientific rationale. The case studies demonstrate the similarities between QBAR and QSAR modeling in: (i) the selection of relevant descriptors to be used in the machine learning algorithm, and (ii) the development of a computational model that maps chemical or biological descriptors to a toxic endpoint. The results of both the case studies show: (i) improved accuracy and sensitivity which is especially desirable under regulatory requirements, and (ii) overall adherence with the OECD/REACH guidelines. Such mechanism based models can be used along with QSAR models for prediction of mechanistically complex toxic endpoints. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Where the bugs are: analyzing distributions of bacterial phyla by descriptor keyword search in the nucleotide database.

PubMed

Squartini, Andrea

2011-07-26

The associations between bacteria and environment underlie their preferential interactions with given physical or chemical conditions. Microbial ecology aims at extracting conserved patterns of occurrence of bacterial taxa in relation to defined habitats and contexts. In the present report the NCBI nucleotide sequence database is used as dataset to extract information relative to the distribution of each of the 24 phyla of the bacteria superkingdom and of the Archaea. Over two and a half million records are filtered in their cross-association with each of 48 sets of keywords, defined to cover natural or artificial habitats, interactions with plant, animal or human hosts, and physical-chemical conditions. The results are processed showing: (a) how the different descriptors enrich or deplete the proportions at which the phyla occur in the total database; (b) in which order of abundance do the different keywords score for each phylum (preferred habitats or conditions), and to which extent are phyla clustered to few descriptors (specific) or spread across many (cosmopolitan); (c) which keywords individuate the communities ranking highest for diversity and evenness. A number of cues emerge from the results, contributing to sharpen the picture on the functional systematic diversity of prokaryotes. Suggestions are given for a future automated service dedicated to refining and updating such kind of analyses via public bioinformatic engines.

Sensory description of marine oils through development of a sensory wheel and vocabulary.

PubMed

Larssen, W E; Monteleone, E; Hersleth, M

2018-04-01

The Omega-3 industry lacks a defined methodology and a vocabulary for evaluating the sensory quality of marine oils. This study was conducted to identify the sensory descriptors of marine oils and organize them in a sensory wheel for use as a tool in quality assessment. Samples of marine oils were collected from six of the largest producers of omega-3 products in Norway. The oils were selected to cover as much variation in sensory characteristics as possible, i.e. oils with different fatty acid content originating from different species. Oils were evaluated by six industry expert panels and one trained sensory panel to build up a vocabulary through a series of language sessions. A total of 184 aroma (odor by nose), flavor, taste and mouthfeel descriptors were generated. A sensory wheel based on 60 selected descriptors grouped together in 21 defined categories was created to form a graphical presentation of the sensory vocabulary. A selection of the oil samples was also evaluated by a trained sensory panel using descriptive analysis. Chemical analysis showed a positive correlation between primary and secondary oxidation products and sensory properties such as rancidity, chemical flavor and process flavor and a negative correlation between primary oxidation products and acidic. This research is a first step towards the broader objective of standardizing the sensory terminology related to marine oils. Copyright © 2017 Elsevier Ltd. All rights reserved.

Endocrine Profiling and Prioritization of Environmental Chemicals Using ToxCast Data

PubMed Central

Reif, David M.; Martin, Matthew T.; Tan, Shirlee W.; Houck, Keith A.; Judson, Richard S.; Richard, Ann M.; Knudsen, Thomas B.; Dix, David J.; Kavlock, Robert J.

2010-01-01

Background The prioritization of chemicals for toxicity testing is a primary goal of the U.S. Environmental Protection Agency (EPA) ToxCast™ program. Phase I of ToxCast used a battery of 467 in vitro, high-throughput screening assays to assess 309 environmental chemicals. One important mode of action leading to toxicity is endocrine disruption, and the U.S. EPA’s Endocrine Disruptor Screening Program (EDSP) has been charged with screening pesticide chemicals and environmental contaminants for their potential to affect the endocrine systems of humans and wildlife. Objective The goal of this study was to develop a flexible method to facilitate the rational prioritization of chemicals for further evaluation and demonstrate its application as a candidate decision-support tool for EDSP. Methods Focusing on estrogen, androgen, and thyroid pathways, we defined putative endocrine profiles and derived a relative rank or score for the entire ToxCast library of 309 unique chemicals. Effects on other nuclear receptors and xenobiotic metabolizing enzymes were also considered, as were pertinent chemical descriptors and pathways relevant to endocrine-mediated signaling. Results Combining multiple data sources into an overall, weight-of-evidence Toxicological Priority Index (ToxPi) score for prioritizing further chemical testing resulted in more robust conclusions than any single data source taken alone. Conclusions Incorporating data from in vitro assays, chemical descriptors, and biological pathways in this prioritization schema provided a flexible, comprehensive visualization and ranking of each chemical’s potential endocrine activity. Importantly, ToxPi profiles provide a transparent visualization of the relative contribution of all information sources to an overall priority ranking. The method developed here is readily adaptable to diverse chemical prioritization tasks. PMID:20826373

Chemometric Methods and Theoretical Molecular Descriptors in Predictive QSAR Modeling of the Environmental Behavior of Organic Pollutants

NASA Astrophysics Data System (ADS)

Gramatica, Paola

This chapter surveys the QSAR modeling approaches (developed by the author's research group) for the validated prediction of environmental properties of organic pollutants. Various chemometric methods, based on different theoretical molecular descriptors, have been applied: explorative techniques (such as PCA for ranking, SOM for similarity analysis), modeling approaches by multiple-linear regression (MLR, in particular OLS), and classification methods (mainly k-NN, CART, CP-ANN). The focus of this review is on the main topics of environmental chemistry and ecotoxicology, related to the physico-chemical properties, the reactivity, and biological activity of chemicals of high environmental concern. Thus, the review deals with atmospheric degradation reactions of VOCs by tropospheric oxidants, persistence and long-range transport of POPs, sorption behavior of pesticides (Koc and leaching), bioconcentration, toxicity (acute aquatic toxicity, mutagenicity of PAHs, estrogen binding activity for endocrine disruptors compounds (EDCs)), and finally persistent bioaccumulative and toxic (PBT) behavior for the screening and prioritization of organic pollutants. Common to all the proposed models is the attention paid to model validation for predictive ability (not only internal, but also external for chemicals not participating in the model development) and checking of the chemical domain of applicability. Adherence to such a policy, requested also by the OECD principles, ensures the production of reliable predicted data, useful also in the new European regulation of chemicals, REACH.

A Systematic Evaluation of Analogs for the Read-across ...

EPA Pesticide Factsheets

Read-across is a data gap filling technique widely used within category and analog approaches to predict a biological property for a target data-poor chemical using known information from similar (source analog) chemical(s). Potential source analogs are typically identified based on structural similarity. Although much guidance has been published for read-across, practical guiding principles for the identification and evaluation of the scientific validity of source analogs, which is a critical step in deriving a robust read-across prediction, remains largely lacking.This case study explores the extent to which 3 structure descriptor sets (Pubchem, Chemotyper and MoSS) and their combinations are able to identify valid analogs for reading across Estrogen Receptor (ER) activity for a specific class of chemicals: hindered phenols. For each target chemical, two sets of analogs (hindered and non-hindered phenols) were selected using each descriptor set with two cut-offs: (1). Minimum Tanimoto similarity (range 0.1 - 0.9), and (2). Closest N analogs (range 1 - 10). Each target-analog pair was then evaluated for its agreement with measured ER binding and agonism. Subsequently, the analogs were filtered using physchem properties (LogKow & Molecular Volume) and the resultant agreement between each target-analog pair was evaluated. The data set comprised 462 hindered phenols and 296 non-hindered phenols. The results demonstrate that: (1). The concordance in ER activity r

A Systematic Evaluation of Analogs and Automated Read ...

EPA Pesticide Factsheets

Read-across is a data gap filling technique widely used within category and analog approaches to predict a biological property for a data-poor (target) chemical using known information from similar (source analog) chemical(s). Potential source analogs are typically identified based on structural similarity. Although much guidance has been published for read-across, practical principles for the identification and evaluation of the scientific validity of source analogs remains lacking. This case study explores how well 3 structure descriptor sets (Pubchem, Chemotyper and MoSS) are able to identify analogs for read-across and predict Estrogen Receptor (ER) binding activity for a specific class of chemicals: hindered phenols. For each target chemical, analogs were selected using each descriptor set with two cut-offs: (1) Minimum Tanimoto similarity (range 0.1 - 0.9), and (2) Closest N analogs (range 1 - 10). Each target-analog pair was then evaluated for its agreement with measured ER binding and agonism. The analogs were subsequently filtered using: (1) physchem properties (LogKow & Molecular Volume), and (2) number of literature sources as a marker for the quality of the experimental data. A majority vote prediction was made for each target phenol by reading-across from the closest N analogs. The data set comprised 462 hindered phenols and 257 non-hindered phenols. The results demonstrate that: (1) The concordance in ER activity rises with increasing similarity,

The Nature of the Chemical Bond--1990.

ERIC Educational Resources Information Center

Ogilvie, J. F.

1990-01-01

Three aspects of quantum mechanics in modern chemistry are stressed: the fundamental structure of quantum mechanics as a basis of chemical applications, the relationship of quantum mechanics to atomic and molecular structure, and the consequent implications for chemical education. A list of 64 references is included. (CW)

Improving quantitative structure-activity relationship models using Artificial Neural Networks trained with dropout.

PubMed

Mendenhall, Jeffrey; Meiler, Jens

2016-02-01

Dropout is an Artificial Neural Network (ANN) training technique that has been shown to improve ANN performance across canonical machine learning (ML) datasets. Quantitative Structure Activity Relationship (QSAR) datasets used to relate chemical structure to biological activity in Ligand-Based Computer-Aided Drug Discovery pose unique challenges for ML techniques, such as heavily biased dataset composition, and relatively large number of descriptors relative to the number of actives. To test the hypothesis that dropout also improves QSAR ANNs, we conduct a benchmark on nine large QSAR datasets. Use of dropout improved both enrichment false positive rate and log-scaled area under the receiver-operating characteristic curve (logAUC) by 22-46 % over conventional ANN implementations. Optimal dropout rates are found to be a function of the signal-to-noise ratio of the descriptor set, and relatively independent of the dataset. Dropout ANNs with 2D and 3D autocorrelation descriptors outperform conventional ANNs as well as optimized fingerprint similarity search methods.

Improving Quantitative Structure-Activity Relationship Models using Artificial Neural Networks Trained with Dropout

PubMed Central

Mendenhall, Jeffrey; Meiler, Jens

2016-01-01

Dropout is an Artificial Neural Network (ANN) training technique that has been shown to improve ANN performance across canonical machine learning (ML) datasets. Quantitative Structure Activity Relationship (QSAR) datasets used to relate chemical structure to biological activity in Ligand-Based Computer-Aided Drug Discovery (LB-CADD) pose unique challenges for ML techniques, such as heavily biased dataset composition, and relatively large number of descriptors relative to the number of actives. To test the hypothesis that dropout also improves QSAR ANNs, we conduct a benchmark on nine large QSAR datasets. Use of dropout improved both Enrichment false positive rate (FPR) and log-scaled area under the receiver-operating characteristic curve (logAUC) by 22–46% over conventional ANN implementations. Optimal dropout rates are found to be a function of the signal-to-noise ratio of the descriptor set, and relatively independent of the dataset. Dropout ANNs with 2D and 3D autocorrelation descriptors outperform conventional ANNs as well as optimized fingerprint similarity search methods. PMID:26830599

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.

Toxicity evaluation and prediction of toxic chemicals on activated sludge system.

PubMed

Cai, Bijing; Xie, Li; Yang, Dianhai; Arcangeli, Jean-Pierre

2010-05-15

The gaps of data for evaluating toxicity of new or overloaded organic chemicals on activated sludge system resulted in the requirements for methodology of toxicity estimation. In this study, 24 aromatic chemicals typically existed in the industrial wastewater were selected and classified into three groups of benzenes, phenols and anilines. Their toxicity on activated sludge was then investigated. Two indexes of IC(50-M) and IC(50-S) were determined respectively from the respiration rates of activated sludge with different toxicant concentration at mid-term (24h) and short-term (30min) time intervals. Experimental results showed that the group of benzenes was the most toxic, followed by the groups of phenols and anilines. The values of IC(50-M) of the tested chemicals were higher than those of IC(50-S). In addition, quantitative structure-activity relationships (QSARs) models developed from IC(50-M) were more stable and accurate than those of IC(50-S). The multiple linear models based on molecular descriptors and K(ow) presented better reliability than single linear models based on K(ow). Among these molecular descriptors, E(lumo) was the most important impact factor for evaluation of mid-term toxicity. Copyright (c) 2009 Elsevier B.V. All rights reserved.

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.

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

Yang, Haw; Hsia, Chih-Hao

Novel Mn.sup.2+-doped quantum dots are provided. These Mn.sup.2+-doped quantum dots exhibit excellent temperature sensitivity in both organic solvents and water-based solutions. Methods of preparing the Mn.sup.2+-doped quantum dots are provided. The Mn.sup.2+-doped quantum dots may be prepared via a stepwise procedure using air-stable and inexpensive chemicals. The use of air-stable chemicals can significantly reduce the cost of synthesis, chemical storage, and the risk associated with handling flammable chemicals. Methods of temperature sensing using Mn.sup.2+-doped quantum dots are provided. The stepwise procedure provides the ability to tune the temperature-sensing properties to satisfy specific needs for temperature sensing applications. Water solubility maymore » be achieved by passivating the Mn.sup.2+-doped quantum dots, allowing the Mn.sup.2+-doped quantum dots to probe the fluctuations of local temperature in biological environments.« less

A Bayesian network model for predicting aquatic toxicity mode ...

EPA Pesticide Factsheets

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 containing over one thousand chemicals with MoA assignments for aquatic animal toxicity. Two dimensional theoretical chemical descriptors were generated for each chemical using the Toxicity Estimation Software Tool. The model was developed through augmented Markov blanket discovery from the data set with the MoA broad classifications as a target node. From cross validation, the overall precision for the model was 80.2% with a R2 of 0.959. The best precision was for the AChEI MoA (93.5%) where 257 chemicals out of 275 were correctly classified. Model precision was poorest for the reactivity MoA (48.5%) where 48 out of 99 reactive chemicals were correctly classified. Narcosis represented the largest class within the MoA dataset and had a precision and reliability of 80.0%, reflecting the global precision across all of the MoAs. False negatives for narcosis most often fell into electron transport inhibition, neurotoxicity or reactivity MoAs. False negatives for all other MoAs were most often narcosis. A probabilistic sensitivity analysis was undertaken for each MoA to examine the sensitivity to individual and multiple descriptor findings. The results show that the Markov blanket of a structurally

Chemical Entity Semantic Specification: Knowledge representation for efficient semantic cheminformatics and facile data integration

PubMed Central

2011-01-01

Background Over the past several centuries, chemistry has permeated virtually every facet of human lifestyle, enriching fields as diverse as medicine, agriculture, manufacturing, warfare, and electronics, among numerous others. Unfortunately, application-specific, incompatible chemical information formats and representation strategies have emerged as a result of such diverse adoption of chemistry. Although a number of efforts have been dedicated to unifying the computational representation of chemical information, disparities between the various chemical databases still persist and stand in the way of cross-domain, interdisciplinary investigations. Through a common syntax and formal semantics, Semantic Web technology offers the ability to accurately represent, integrate, reason about and query across diverse chemical information. Results Here we specify and implement the Chemical Entity Semantic Specification (CHESS) for the representation of polyatomic chemical entities, their substructures, bonds, atoms, and reactions using Semantic Web technologies. CHESS provides means to capture aspects of their corresponding chemical descriptors, connectivity, functional composition, and geometric structure while specifying mechanisms for data provenance. We demonstrate that using our readily extensible specification, it is possible to efficiently integrate multiple disparate chemical data sources, while retaining appropriate correspondence of chemical descriptors, with very little additional effort. We demonstrate the impact of some of our representational decisions on the performance of chemically-aware knowledgebase searching and rudimentary reaction candidate selection. Finally, we provide access to the tools necessary to carry out chemical entity encoding in CHESS, along with a sample knowledgebase. Conclusions By harnessing the power of Semantic Web technologies with CHESS, it is possible to provide a means of facile cross-domain chemical knowledge integration with full preservation of data correspondence and provenance. Our representation builds on existing cheminformatics technologies and, by the virtue of RDF specification, remains flexible and amenable to application- and domain-specific annotations without compromising chemical data integration. We conclude that the adoption of a consistent and semantically-enabled chemical specification is imperative for surviving the coming chemical data deluge and supporting systems science research. PMID:21595881

Chemical Entity Semantic Specification: Knowledge representation for efficient semantic cheminformatics and facile data integration.

PubMed

Chepelev, Leonid L; Dumontier, Michel

2011-05-19

Over the past several centuries, chemistry has permeated virtually every facet of human lifestyle, enriching fields as diverse as medicine, agriculture, manufacturing, warfare, and electronics, among numerous others. Unfortunately, application-specific, incompatible chemical information formats and representation strategies have emerged as a result of such diverse adoption of chemistry. Although a number of efforts have been dedicated to unifying the computational representation of chemical information, disparities between the various chemical databases still persist and stand in the way of cross-domain, interdisciplinary investigations. Through a common syntax and formal semantics, Semantic Web technology offers the ability to accurately represent, integrate, reason about and query across diverse chemical information. Here we specify and implement the Chemical Entity Semantic Specification (CHESS) for the representation of polyatomic chemical entities, their substructures, bonds, atoms, and reactions using Semantic Web technologies. CHESS provides means to capture aspects of their corresponding chemical descriptors, connectivity, functional composition, and geometric structure while specifying mechanisms for data provenance. We demonstrate that using our readily extensible specification, it is possible to efficiently integrate multiple disparate chemical data sources, while retaining appropriate correspondence of chemical descriptors, with very little additional effort. We demonstrate the impact of some of our representational decisions on the performance of chemically-aware knowledgebase searching and rudimentary reaction candidate selection. Finally, we provide access to the tools necessary to carry out chemical entity encoding in CHESS, along with a sample knowledgebase. By harnessing the power of Semantic Web technologies with CHESS, it is possible to provide a means of facile cross-domain chemical knowledge integration with full preservation of data correspondence and provenance. Our representation builds on existing cheminformatics technologies and, by the virtue of RDF specification, remains flexible and amenable to application- and domain-specific annotations without compromising chemical data integration. We conclude that the adoption of a consistent and semantically-enabled chemical specification is imperative for surviving the coming chemical data deluge and supporting systems science research.

Effect of cultivar and roasting technique on sensory quality of Bierzo roasted pepper.

PubMed

Guerra, Marcos; Sanz, Miguel A; Valenciano, José B; Casquero, Pedro A

2011-10-01

Pepper (Capsicum annuum L.) is one of the main horticultural products in the world. Roasted pepper is a high quality transformed product in the Iberian Peninsula, and obtained the recognition of 'Protected Geographical Indication' (PGI) of 'Pimiento Asado del Bierzo' in 2002. Roasted pepper has been traditionally processed with a steel-sheet hob. However, there are no data available about the effect of roasting technique in the quality of roasted pepper. The objective of this work was to compare the sensory quality of roasted pepper using industrial roasting techniques. Sensory properties that showed significant differences between roasting techniques were colour, thickness and charred remains (appearance descriptors), bitterness (taste descriptor) and smokiness (after-taste descriptor). Higher value of descriptors such as colour, charred remains and smokiness for peppers elaborated in a rotary oven, helped roasted pepper to reach a higher level of overall quality, although rotary oven samples reached the lowest roast yield. Roasting technique, rather than landrace, had the greatest effect on the sensory quality of roasted pepper, so the rotary oven was the roasting technique that achieved the highest quality score. This will contribute to improve sensory quality and marketing of PGI 'Pimiento Asado del Bierzo' in high quality markets. Copyright © 2011 Society of Chemical Industry.

Differentiating gold nanorod samples using particle size and shape distributions from transmission electron microscope images

NASA Astrophysics Data System (ADS)

Grulke, Eric A.; Wu, Xiaochun; Ji, Yinglu; Buhr, Egbert; Yamamoto, Kazuhiro; Song, Nam Woong; Stefaniak, Aleksandr B.; Schwegler-Berry, Diane; Burchett, Woodrow W.; Lambert, Joshua; Stromberg, Arnold J.

2018-04-01

Size and shape distributions of gold nanorod samples are critical to their physico-chemical properties, especially their longitudinal surface plasmon resonance. This interlaboratory comparison study developed methods for measuring and evaluating size and shape distributions for gold nanorod samples using transmission electron microscopy (TEM) images. The objective was to determine whether two different samples, which had different performance attributes in their application, were different with respect to their size and/or shape descriptor distributions. Touching particles in the captured images were identified using a ruggedness shape descriptor. Nanorods could be distinguished from nanocubes using an elongational shape descriptor. A non-parametric statistical test showed that cumulative distributions of an elongational shape descriptor, that is, the aspect ratio, were statistically different between the two samples for all laboratories. While the scale parameters of size and shape distributions were similar for both samples, the width parameters of size and shape distributions were statistically different. This protocol fulfills an important need for a standardized approach to measure gold nanorod size and shape distributions for applications in which quantitative measurements and comparisons are important. Furthermore, the validated protocol workflow can be automated, thus providing consistent and rapid measurements of nanorod size and shape distributions for researchers, regulatory agencies, and industry.

Development of Quantum Chemical Method to Calculate Half Maximal Inhibitory Concentration (IC50 ).

PubMed

Bag, Arijit; Ghorai, Pradip Kr

2016-05-01

Till date theoretical calculation of the half maximal inhibitory concentration (IC50 ) of a compound is based on different Quantitative Structure Activity Relationship (QSAR) models which are empirical methods. By using the Cheng-Prusoff equation it may be possible to compute IC50 , but this will be computationally very expensive as it requires explicit calculation of binding free energy of an inhibitor with respective protein or enzyme. In this article, for the first time we report an ab initio method to compute IC50 of a compound based only on the inhibitor itself where the effect of the protein is reflected through a proportionality constant. By using basic enzyme inhibition kinetics and thermodynamic relations, we derive an expression of IC50 in terms of hydrophobicity, electric dipole moment (μ) and reactivity descriptor (ω) of an inhibitor. We implement this theory to compute IC50 of 15 HIV-1 capsid inhibitors and compared them with experimental results and available other QASR based empirical results. Calculated values using our method are in very good agreement with the experimental values compared to the values calculated using other methods. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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.

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.

Comparison of computational methods to model DNA minor groove binders.

PubMed

Srivastava, Hemant Kumar; Chourasia, Mukesh; Kumar, Devesh; Sastry, G Narahari

2011-03-28

There has been a profound interest in designing small molecules that interact in sequence-selective fashion with DNA minor grooves. However, most in silico approaches have not been parametrized for DNA ligand interaction. In this regard, a systematic computational analysis of 57 available PDB structures of noncovalent DNA minor groove binders has been undertaken. The study starts with a rigorous benchmarking of GOLD, GLIDE, CDOCKER, and AUTODOCK docking protocols followed by developing QSSR models and finally molecular dynamics simulations. In GOLD and GLIDE, the orientation of the best score pose is closer to the lowest rmsd pose, and the deviation in the conformation of various poses is also smaller compared to other docking protocols. Efficient QSSR models were developed with constitutional, topological, and quantum chemical descriptors on the basis of B3LYP/6-31G* optimized geometries, and with this ΔT(m) values of 46 ligands were predicted. Molecular dynamics simulations of the 14 DNA-ligand complexes with Amber 8.0 show that the complexes are stable in aqueous conditions and do not undergo noticeable fluctuations during the 5 ns production run, with respect to their initial placement in the minor groove region.

Spectroscopic studies (FT-IR, FT-Raman, UV-Visible), normal co-ordinate analysis, first-order hyperpolarizability and HOMO, LUMO studies of 3,4-dichlorobenzophenone by using Density Functional Methods.

PubMed

Venkata Prasad, K; Samatha, K; Jagadeeswara Rao, D; Santhamma, C; Muthu, S; Mark Heron, B

2015-01-01

The vibrational frequencies of 3,4-dichlorobenzophenone (DCLBP) were obtained from the FT-IR and Raman spectral data, and evaluated based on the Density Functional Theory using the standard method B3LYP with 6-311+G(d,p) as the basis set. On the basis of potential energy distribution together with the normal-co-ordinate analysis and following the scaled quantum mechanical force methodology, the assignments for the various frequencies were described. The values of the electric dipole moment (μ) and the first-order hyperpolarizability (β) of the molecule were computed. The UV-absorption spectrum was also recorded to study the electronic transitions. The calculated HOMO and LUMO energies show that charge transfer occurs within the molecule. The NBO analysis, to study the intramolecular hyperconjugative interactions, was carried out. Mulliken's net charges were evaluated. The MEP and thermodynamic properties were also calculated. The electron density-based local reactivity descriptor, such as Fukui functions, was calculated to explain the chemical selectivity or reactivity site in 3,4-dichlorobenzophenone. Copyright © 2015 Elsevier B.V. All rights reserved.

Measurement of Temperature Dependence for Vapor Pressures of Seventeen OH-PBDEs and Eleven MeO-PBDEs by Gas Chromatographic Method.

PubMed

Zhao, Hongxia; Xie, Qing; Chen, Xiuying; Qu, Baocheng; Jiang, Jingqiu

2016-05-01

Hydroxylated polybromodiphenyl ethers (OH-PBDEs) and methoxylated polybrominated diphenyl ethers (MeO-PBDEs) are emerging organic pollutants. Supercooled liquid vapor pressures (p L) and enthalpies of vaporization (∆vap H) for seventeen OH-PBDEs and eleven MeO-PBDEs were determined by a gas chromatographic technique. p L at 298 K ranged from 0.0173 Pa for 2'-OH-BDE3 to 2.32 × 10(-7) Pa for 3'-OH-BDE154 and they are approximately one order of magnitude smaller than those determined for the counterpart polybrominated diphenyl ethers (PBDEs). ∆vap H was in the range of 76-121 kJ/mol. The temperature dependence of p L was measured by fitting the experimental data with the log(p L/Pa) = a/(T/K) + b equation, and this corresponds to a 50-265 times higher p L value at 0 versus 30°C. Using fundamental quantum chemical descriptors, two quantitative structure-property relationship models (Q cum > 0.935) were developed to estimate p L at any temperature for the additional OH- and MeO-PBDE congeners.

Eco-friendly synthesis, physicochemical studies, biological assay and molecular docking of steroidal oxime-ethers

PubMed Central

Alam, Mahboob; Lee, Dong-Ung

2015-01-01

The aim of this study was to report the synthesis of biologically active compounds; 7-(2′-aminoethoxyimino)-cholest-5-ene (4), a steroidal oxime-ether and its derivatives (5, 6) via a facile microwave assisted solvent free reaction methodology. This new synthetic, eco-friendly, sustainable protocol resulted in a remarkable improvement in the synthetic efficiency (85-93 % yield) and high purity using basic alumina. The synthesized compounds were screened for their antibacterial against six bacterial strains by disc diffusion method and antioxidant potential by DPPH assay. The binding capabilities of a compound 6 exhibiting good antibacterial potential were assessed on the basis of molecular docking studies and four types of three-dimensional molecular field descriptors. Moreover the structure-antimicrobial activity relationships were studied using some physicochemical and quantum-chemical parameters with GAMESS interface as well as WebMO Job Manager by using the basic level of theory. Hence, this synthetic approach is believed to provide a better scope for the synthesis of steroidal oxime-ether analogues and will be a more practical alternative to the presently existing procedures. Moreover, detailed in silico docking studies suggested the plausible mechanism of steroidal oxime-ethers as effective antimicrobial agents. PMID:27330525

Characterization of π-stacking interactions between aromatic amino acids and quercetagetin

NASA Astrophysics Data System (ADS)

Akher, Farideh Badichi; Ebrahimi, Ali; Mostafavi, Najmeh

2017-01-01

In the present study, the π-stacking interactions between quercetagetin (QUE), which is one of the most representative flavonol compounds with biological and chemical activities, and some aromatic amino acid (AA) residues has been investigated by the quantum mechanical calculations. The trend in the absolute value of stacking interaction energy |ΔE| with respect to AAs is HIS > PHE > TYR > TPR. The results show that the sum of donor-acceptor interaction energy between AAs and QUE (∑E2) and the sum of electron densities ρ calculated at BCPs and CCPs between the rings (∑ρBCPs and ∑ρCCP) can be useful descriptors for prediction of the ΔE values of the complexes. The Osbnd H bond dissociation enthalpy (BDE) slightly decreases by the π-stacking interaction, which confirms the positive effect of that interaction on the antioxidant activity of QUE. A reverse trend is observed for BDE when is compared with the |ΔE| values. A reliable relationship is also observed between the Muliken spin density (MSD) distributions of the radical species and the most convenient Osbnd H bond dissociations. In addition, reactivity is in good correlation with the antioxidant activity of the complexes.

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.

Towards a physical interpretation of substituent effect: Quantum chemical interpretation of Hammett substituent constants

NASA Astrophysics Data System (ADS)

Varaksin, Konstantin S.; Szatylowicz, Halina; Krygowski, Tadeusz M.

2017-06-01

Quantitative description of substituent effects is of a great importance especially in organic chemistry and QSAR-type treatments. The proposed approaches: substituent effect stabilization energy (SESE) and charge of the substituent active region (cSAR) provide substituent effect characteristics, physically independent of the Hammett's substituent constants, σ. To document abilities of these descriptors the B3LYP/6-311++G(d,p) method is employed to examine changes in properties of a reaction center Y (Y = COOH or COO- groups) and a transmitting moiety (benzene ring) due to substituent effects in a series of meta- and para-X-substituted benzoic acid and benzoate anion derivatives (X = NMe2, NH2, OH, OMe, CH3, H, F, Cl, CF3, CN, CHO, COMe, CONH2, COOH, NO2, NO). The transmitting moiety is described by aromaticity indices HOMA and NICS(1). Furthermore, an advantage of the cSAR characteristic is the ability to use it to describe both electron donating/accepting properties of a substituent as well as a reaction center. It allows demonstration of the reverse substituent effects of COOH and COO- groups on substituent X.

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.

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

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.

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.

A hierarchical clustering methodology for the estimation of toxicity.

PubMed

Martin, Todd M; Harten, Paul; Venkatapathy, Raghuraman; Das, Shashikala; Young, Douglas M

2008-01-01

ABSTRACT A quantitative structure-activity relationship (QSAR) methodology based on hierarchical clustering was developed to predict toxicological endpoints. This methodology utilizes Ward's method to divide a training set into a series of structurally similar clusters. The structural similarity is defined in terms of 2-D physicochemical descriptors (such as connectivity and E-state indices). A genetic algorithm-based technique is used to generate statistically valid QSAR models for each cluster (using the pool of descriptors described above). The toxicity for a given query compound is estimated using the weighted average of the predictions from the closest cluster from each step in the hierarchical clustering assuming that the compound is within the domain of applicability of the cluster. The hierarchical clustering methodology was tested using a Tetrahymena pyriformis acute toxicity data set containing 644 chemicals in the training set and with two prediction sets containing 339 and 110 chemicals. The results from the hierarchical clustering methodology were compared to the results from several different QSAR methodologies.

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.

Discriminating Drug-Like Compounds by Partition Trees with Quantum Similarity Indices and Graph Invariants.

PubMed

Julián-Ortiz, Jesus V de; Gozalbes, Rafael; Besalú, Emili

2016-01-01

The search for new drug candidates in databases is of paramount importance in pharmaceutical chemistry. The selection of molecular subsets is greatly optimized and much more promising when potential drug-like molecules are detected a priori. In this work, about one hundred thousand molecules are ranked following a new methodology: a drug/non-drug classifier constructed by a consensual set of classification trees. The classification trees arise from the stochastic generation of training sets, which in turn are used to estimate probability factors of test molecules to be drug-like compounds. Molecules were represented by Topological Quantum Similarity Indices and their Graph Theoretical counterparts. The contribution of the present paper consists of presenting an effective ranking method able to improve the probability of finding drug-like substances by using these types of molecular descriptors.

A combined Fisher and Laplacian score for feature selection in QSAR based drug design using compounds with known and unknown activities.

PubMed

Valizade Hasanloei, Mohammad Amin; Sheikhpour, Razieh; Sarram, Mehdi Agha; Sheikhpour, Elnaz; Sharifi, Hamdollah

2018-02-01

Quantitative structure-activity relationship (QSAR) is an effective computational technique for drug design that relates the chemical structures of compounds to their biological activities. Feature selection is an important step in QSAR based drug design to select the most relevant descriptors. One of the most popular feature selection methods for classification problems is Fisher score which aim is to minimize the within-class distance and maximize the between-class distance. In this study, the properties of Fisher criterion were extended for QSAR models to define the new distance metrics based on the continuous activity values of compounds with known activities. Then, a semi-supervised feature selection method was proposed based on the combination of Fisher and Laplacian criteria which exploits both compounds with known and unknown activities to select the relevant descriptors. To demonstrate the efficiency of the proposed semi-supervised feature selection method in selecting the relevant descriptors, we applied the method and other feature selection methods on three QSAR data sets such as serine/threonine-protein kinase PLK3 inhibitors, ROCK inhibitors and phenol compounds. The results demonstrated that the QSAR models built on the selected descriptors by the proposed semi-supervised method have better performance than other models. This indicates the efficiency of the proposed method in selecting the relevant descriptors using the compounds with known and unknown activities. The results of this study showed that the compounds with known and unknown activities can be helpful to improve the performance of the combined Fisher and Laplacian based feature selection methods.

A combined Fisher and Laplacian score for feature selection in QSAR based drug design using compounds with known and unknown activities

NASA Astrophysics Data System (ADS)

Valizade Hasanloei, Mohammad Amin; Sheikhpour, Razieh; Sarram, Mehdi Agha; Sheikhpour, Elnaz; Sharifi, Hamdollah

2018-02-01

Quantitative structure-activity relationship (QSAR) is an effective computational technique for drug design that relates the chemical structures of compounds to their biological activities. Feature selection is an important step in QSAR based drug design to select the most relevant descriptors. One of the most popular feature selection methods for classification problems is Fisher score which aim is to minimize the within-class distance and maximize the between-class distance. In this study, the properties of Fisher criterion were extended for QSAR models to define the new distance metrics based on the continuous activity values of compounds with known activities. Then, a semi-supervised feature selection method was proposed based on the combination of Fisher and Laplacian criteria which exploits both compounds with known and unknown activities to select the relevant descriptors. To demonstrate the efficiency of the proposed semi-supervised feature selection method in selecting the relevant descriptors, we applied the method and other feature selection methods on three QSAR data sets such as serine/threonine-protein kinase PLK3 inhibitors, ROCK inhibitors and phenol compounds. The results demonstrated that the QSAR models built on the selected descriptors by the proposed semi-supervised method have better performance than other models. This indicates the efficiency of the proposed method in selecting the relevant descriptors using the compounds with known and unknown activities. The results of this study showed that the compounds with known and unknown activities can be helpful to improve the performance of the combined Fisher and Laplacian based feature selection methods.

Chemical Reactivity Theory Study of Advanced Glycation Endproduct Inhibitors.

PubMed

Frau, Juan; Glossman-Mitnik, Daniel

2017-02-02

Several compounds with the known ability to perform as inhibitors of advanced glycation endproducts (AGE) have been studied with Density Functional Theory (DFT) through the use of anumberofdensityfunctionalswhoseaccuracyhasbeentestedacrossabroadspectrumofdatabases in Chemistry and Physics. The chemical reactivity descriptors for these systems have been calculated through Conceptual DFT in an attempt to relate their intrinsic chemical reactivity with the ability to inhibit the action of glycating carbonyl compounds on amino acids and proteins. This knowledge could be useful in the design and development of new drugs which can be potential medicines for diabetes and Alzheimer's disease.

Quantitative structure-cytotoxicity relationship of phenylpropanoid amides.

PubMed

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

2014-07-01

A total of 12 phenylpropanoid amides were subjected to quantitative structure-activity relationship (QSAR) analysis, based on their cytotoxicity, tumor selectivity and anti-HIV activity, in order to investigate on their 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 CC50 (50% cytotoxic concentration) against normal oral cells to that against OSCC cell lines. Anti-HIV activity was evaluated by the ratio of CC50 to EC50 (50% cytoprotective concentration from HIV infection). Physicochemical, structural, and quantum-chemical parameters were calculated based on the conformations optimized by the LowModeMD method followed by density functional theory (DFT) method. Twelve phenylpropanoid amides showed moderate cytotoxicity against both normal and OSCC cell lines. N-Caffeoyl derivatives coupled with vanillylamine and tyramine exhibited relatively higher tumor selectivity. Cytotoxicity against normal cells was correlated with descriptors related to electrostatic interaction such as polar surface area and chemical hardness, whereas cytotoxicity against tumor cells correlated with free energy, surface area and ellipticity. The tumor-selective cytotoxicity correlated with molecular size (surface area) and electrostatic interaction (the maximum electrostatic potential). The molecular size, shape and ability for electrostatic interaction are useful parameters for estimating the tumor selectivity of phenylpropanoid amides. Copyright© 2014 International Institute of Anticancer Research (Dr. John G. Delinassios), All rights reserved.

An extensible framework for capturing solvent effects in computer generated kinetic models.

PubMed

Jalan, Amrit; West, Richard H; Green, William H

2013-03-14

Detailed kinetic models provide useful mechanistic insight into a chemical system. Manual construction of such models is laborious and error-prone, which has led to the development of automated methods for exploring chemical pathways. These methods rely on fast, high-throughput estimation of species thermochemistry and kinetic parameters. In this paper, we present a methodology for extending automatic mechanism generation to solution phase systems which requires estimation of solvent effects on reaction rates and equilibria. The linear solvation energy relationship (LSER) method of Abraham and co-workers is combined with Mintz correlations to estimate ΔG(solv)°(T) in over 30 solvents using solute descriptors estimated from group additivity. Simple corrections are found to be adequate for the treatment of radical sites, as suggested by comparison with known experimental data. The performance of scaled particle theory expressions for enthalpic-entropic decomposition of ΔG(solv)°(T) is also presented along with the associated computational issues. Similar high-throughput methods for solvent effects on free-radical kinetics are only available for a handful of reactions due to lack of reliable experimental data, and continuum dielectric calculations offer an alternative method for their estimation. For illustration, we model liquid phase oxidation of tetralin in different solvents computing the solvent dependence for ROO• + ROO• and ROO• + solvent reactions using polarizable continuum quantum chemistry methods. The resulting kinetic models show an increase in oxidation rate with solvent polarity, consistent with experiment. Further work needed to make this approach more generally useful is outlined.

Advances in Quantum Mechanochemistry: Electronic Structure Methods and Force Analysis.

PubMed

Stauch, Tim; Dreuw, Andreas

2016-11-23

In quantum mechanochemistry, quantum chemical methods are used to describe molecules under the influence of an external force. The calculation of geometries, energies, transition states, reaction rates, and spectroscopic properties of molecules on the force-modified potential energy surfaces is the key to gain an in-depth understanding of mechanochemical processes at the molecular level. In this review, we present recent advances in the field of quantum mechanochemistry and introduce the quantum chemical methods used to calculate the properties of molecules under an external force. We place special emphasis on quantum chemical force analysis tools, which can be used to identify the mechanochemically relevant degrees of freedom in a deformed molecule, and spotlight selected applications of quantum mechanochemical methods to point out their synergistic relationship with experiments.

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.

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.

New public QSAR model for carcinogenicity

PubMed Central

2010-01-01

Background One of the main goals of the new chemical regulation REACH (Registration, Evaluation and Authorization of Chemicals) is to fulfill the gaps in data concerned with properties of chemicals affecting the human health. (Q)SAR models are accepted as a suitable source of information. The EU funded CAESAR project aimed to develop models for prediction of 5 endpoints for regulatory purposes. Carcinogenicity is one of the endpoints under consideration. Results Models for prediction of carcinogenic potency according to specific requirements of Chemical regulation were developed. The dataset of 805 non-congeneric chemicals extracted from Carcinogenic Potency Database (CPDBAS) was used. Counter Propagation Artificial Neural Network (CP ANN) algorithm was implemented. In the article two alternative models for prediction carcinogenicity are described. The first model employed eight MDL descriptors (model A) and the second one twelve Dragon descriptors (model B). CAESAR's models have been assessed according to the OECD principles for the validation of QSAR. For the model validity we used a wide series of statistical checks. Models A and B yielded accuracy of training set (644 compounds) equal to 91% and 89% correspondingly; the accuracy of the test set (161 compounds) was 73% and 69%, while the specificity was 69% and 61%, respectively. Sensitivity in both cases was equal to 75%. The accuracy of the leave 20% out cross validation for the training set of models A and B was equal to 66% and 62% respectively. To verify if the models perform correctly on new compounds the external validation was carried out. The external test set was composed of 738 compounds. We obtained accuracy of external validation equal to 61.4% and 60.0%, sensitivity 64.0% and 61.8% and specificity equal to 58.9% and 58.4% respectively for models A and B. Conclusion Carcinogenicity is a particularly important endpoint and it is expected that QSAR models will not replace the human experts opinions and conventional methods. However, we believe that combination of several methods will provide useful support to the overall evaluation of carcinogenicity. In present paper models for classification of carcinogenic compounds using MDL and Dragon descriptors were developed. Models could be used to set priorities among chemicals for further testing. The models at the CAESAR site were implemented in java and are publicly accessible. PMID:20678182

Structural insights, protein-ligand interactions and spectroscopic characterization of isoformononetin

NASA Astrophysics Data System (ADS)

Srivastava, Anubha; Singh, Harshita; Mishra, Rashmi; Dev, Kapil; Tandon, Poonam; Maurya, Rakesh

2017-04-01

Isoformononetin, a methoxylated isoflavone present in medicinal plants, has non-estrogenic bone forming effect via differential mitogen-activated protein kinase (MAPK) signaling. Spectroscopic (FT-Raman, FT-IR, UV-vis and NMR spectra) and quantum chemical calculations using density functional theory (DFT) and 6-311++G(d,p) as a large basis set have been employed to study the structural and electronic properties of isoformononetin. A detailed conformational analysis is performed to determine the stability among conformers and the various possibilities of intramolecular hydrogen bonding formation. Molecular docking studies with different protein kinases were performed on isoformononetin and previously studied isoflavonoid, formononetin in order to understand their inhibitory nature and the effect of functional groups on osteogenic or osteoporosis associated proteins. It is found that the oxygen atoms of methoxy, hydroxyl groups attached to phenyl rings R1, R3 and carbonyl group attached to pyran ring R2, play a major role in binding with the protein kinases that is responsible for the osteoporosis; however, no hydrophobic interactions are observed between rings of ligand and protein. The electronic properties such as HOMO and LUMO energies were determined by time-dependent TD-DFT which predict that conformer II is a little bit more stable and chemically low reactive than conformer I of isoformononetin. To estimate the structure-activity relationship, the molecular electrostatic potential (MEP) surface map, and reactivity descriptors are calculated from the optimized geometry of the molecule. From these results, it is also found that isoformononetin is kinetically more stable, less toxic, weak electrophile and chemically less reactive than formononetin. The atoms in molecules and natural bond orbital analysis are applied for the detailed analysis of intra and intermolecular hydrogen bonding interactions.

Global structure–activity relationship model for nonmutagenic carcinogens using virtual ligand-protein interactions as model descriptors

PubMed Central

Cunningham, Albert R.; Trent, John O.

2012-01-01

Structure–activity relationship (SAR) models are powerful tools to investigate the mechanisms of action of chemical carcinogens and to predict the potential carcinogenicity of untested compounds. We describe the use of a traditional fragment-based SAR approach along with a new virtual ligand-protein interaction-based approach for modeling of nonmutagenic carcinogens. The ligand-based SAR models used descriptors derived from computationally calculated ligand-binding affinities for learning set agents to 5495 proteins. Two learning sets were developed. One set was from the Carcinogenic Potency Database, where chemicals tested for rat carcinogenesis along with Salmonella mutagenicity data were provided. The second was from Malacarne et al. who developed a learning set of nonalerting compounds based on rodent cancer bioassay data and Ashby’s structural alerts. When the rat cancer models were categorized based on mutagenicity, the traditional fragment model outperformed the ligand-based model. However, when the learning sets were composed solely of nonmutagenic or nonalerting carcinogens and noncarcinogens, the fragment model demonstrated a concordance of near 50%, whereas the ligand-based models demonstrated a concordance of 71% for nonmutagenic carcinogens and 74% for nonalerting carcinogens. Overall, these findings suggest that expert system analysis of virtual chemical protein interactions may be useful for developing predictive SAR models for nonmutagenic carcinogens. Moreover, a more practical approach for developing SAR models for carcinogenesis may include fragment-based models for chemicals testing positive for mutagenicity and ligand-based models for chemicals devoid of DNA reactivity. PMID:22678118

Global structure-activity relationship model for nonmutagenic carcinogens using virtual ligand-protein interactions as model descriptors.

PubMed

Cunningham, Albert R; Carrasquer, C Alex; Qamar, Shahid; Maguire, Jon M; Cunningham, Suzanne L; Trent, John O

2012-10-01

Structure-activity relationship (SAR) models are powerful tools to investigate the mechanisms of action of chemical carcinogens and to predict the potential carcinogenicity of untested compounds. We describe the use of a traditional fragment-based SAR approach along with a new virtual ligand-protein interaction-based approach for modeling of nonmutagenic carcinogens. The ligand-based SAR models used descriptors derived from computationally calculated ligand-binding affinities for learning set agents to 5495 proteins. Two learning sets were developed. One set was from the Carcinogenic Potency Database, where chemicals tested for rat carcinogenesis along with Salmonella mutagenicity data were provided. The second was from Malacarne et al. who developed a learning set of nonalerting compounds based on rodent cancer bioassay data and Ashby's structural alerts. When the rat cancer models were categorized based on mutagenicity, the traditional fragment model outperformed the ligand-based model. However, when the learning sets were composed solely of nonmutagenic or nonalerting carcinogens and noncarcinogens, the fragment model demonstrated a concordance of near 50%, whereas the ligand-based models demonstrated a concordance of 71% for nonmutagenic carcinogens and 74% for nonalerting carcinogens. Overall, these findings suggest that expert system analysis of virtual chemical protein interactions may be useful for developing predictive SAR models for nonmutagenic carcinogens. Moreover, a more practical approach for developing SAR models for carcinogenesis may include fragment-based models for chemicals testing positive for mutagenicity and ligand-based models for chemicals devoid of DNA reactivity.

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.

Prediction of pesticide acute toxicity using two-dimensional chemical descriptors and target species classification

EPA Science Inventory

Previous modelling of the median lethal dose (oral rat LD50) has indicated that local class-based models yield better correlations than global models. We evaluated the hypothesis that dividing the dataset by pesticidal mechanisms would improve prediction accuracy. A linear discri...

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.

FLASHFLOOD: A 3D Field-based similarity search and alignment method for flexible molecules

NASA Astrophysics Data System (ADS)

Pitman, Michael C.; Huber, Wolfgang K.; Horn, Hans; Krämer, Andreas; Rice, Julia E.; Swope, William C.

2001-07-01

A three-dimensional field-based similarity search and alignment method for flexible molecules is introduced. The conformational space of a flexible molecule is represented in terms of fragments and torsional angles of allowed conformations. A user-definable property field is used to compute features of fragment pairs. Features are generalizations of CoMMA descriptors (Silverman, B.D. and Platt, D.E., J. Med. Chem., 39 (1996) 2129.) that characterize local regions of the property field by its local moments. The features are invariant under coordinate system transformations. Features taken from a query molecule are used to form alignments with fragment pairs in the database. An assembly algorithm is then used to merge the fragment pairs into full structures, aligned to the query. Key to the method is the use of a context adaptive descriptor scaling procedure as the basis for similarity. This allows the user to tune the weights of the various feature components based on examples relevant to the particular context under investigation. The property fields may range from simple, phenomenological fields, to fields derived from quantum mechanical calculations. We apply the method to the dihydrofolate/methotrexate benchmark system, and show that when one injects relevant contextual information into the descriptor scaling procedure, better results are obtained more efficiently. We also show how the method works and include computer times for a query from a database that represents approximately 23 million conformers of seventeen flexible molecules.

Modeling the Biodegradability of Chemical Compounds Using the Online CHEmical Modeling Environment (OCHEM).

PubMed

Vorberg, Susann; Tetko, Igor V

2014-01-01

Biodegradability describes the capacity of substances to be mineralized by free-living bacteria. It is a crucial property in estimating a compound's long-term impact on the environment. The ability to reliably predict biodegradability would reduce the need for laborious experimental testing. However, this endpoint is difficult to model due to unavailability or inconsistency of experimental data. Our approach makes use of the Online Chemical Modeling Environment (OCHEM) and its rich supply of machine learning methods and descriptor sets to build classification models for ready biodegradability. These models were analyzed to determine the relationship between characteristic structural properties and biodegradation activity. The distinguishing feature of the developed models is their ability to estimate the accuracy of prediction for each individual compound. The models developed using seven individual descriptor sets were combined in a consensus model, which provided the highest accuracy. The identified overrepresented structural fragments can be used by chemists to improve the biodegradability of new chemical compounds. The consensus model, the datasets used, and the calculated structural fragments are publicly available at http://ochem.eu/article/31660. © 2014 The Authors. Published by Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

On the Development and Use of Large Chemical Similarity Networks, Informatics Best Practices and Novel Chemical Descriptors Towards Materials Quantitative Structure Property Relationships

NASA Astrophysics Data System (ADS)

Krein, Michael

After decades of development and use in a variety of application areas, Quantitative Structure Property Relationships (QSPRs) and related descriptor-based statistical learning methods have achieved a level of infamy due to their misuse. The field is rife with past examples of overtrained models, overoptimistic performance assessment, and outright cheating in the form of explicitly removing data to fit models. These actions do not serve the community well, nor are they beneficial to future predictions based on established models. In practice, in order to select combinations of descriptors and machine learning methods that might work best, one must consider the nature and size of the training and test datasets, be aware of existing hypotheses about the data, and resist the temptation to bias structure representation and modeling to explicitly fit the hypotheses. The definition and application of these best practices is important for obtaining actionable modeling outcomes, and for setting user expectations of modeling accuracy when predicting the endpoint values of unknowns. A wide variety of statistical learning approaches, descriptor types, and model validation strategies are explored herein, with the goals of helping end users understand the factors involved in creating and using QSPR models effectively, and to better understand relationships within the data, especially by looking at the problem space from multiple perspectives. Molecular relationships are commonly envisioned in a continuous high-dimensional space of numerical descriptors, referred to as chemistry space. Descriptor and similarity metric choice influence the partitioning of this space into regions corresponding to local structural similarity. These regions, known as domains of applicability, are most likely to be successfully modeled by a QSPR. In Chapter 2, the network topology and scaling relationships of several chemistry spaces are thoroughly investigated. Chemistry spaces studied include the ZINC data set, a qHTS PubChem bioassay, as well as the protein binding sites from the PDB. The characteristics of these networks are compared and contrasted with those of the bioassay Structure Activity Landscape Index (SALI) subnetwork, which maps discontinuities or cliffs in the structure activity landscape. Mapping this newly generated information over underlying chemistry space networks generated using different descriptors demonstrates local modeling capacity and can guide the choice of better local representations of chemistry space. Chapter 2 introduces and demonstrates this novel concept, which also enables future work in visualization and interpretation of chemical spaces. Initially, it was discovered that there were no community-available tools to leverage best-practice ideas to comprehensively build, compare, and interpret QSPRs. The Yet Another Modeling System (YAMS) tool performs a series of balanced, rational decisions in dataset preprocessing and parameter/feature selection over a choice of modeling methods. To date, YAMS is the only community-available informatics tool that performs such decisions consistently between methods while also providing multiple model performance comparisons and detailed descriptor importance information. The focus of the tool is thus to convey rich information about model quality and predictions that help to "close the loop" between modeling and experimental efforts, for example, in tailoring nanocomposite properties. Polymer nanocomposites (PNC) are complex material systems encompassing many potential structures, chemistries, and self assembled morphologies that could significantly impact commercial and military applications. There is a strong desire to characterize and understand the tradespace of nanocomposites, to identify the important factors relating nanostructure to materials properties and determine an effective way to control materials properties at the manufacturing scale. Due to the complexity of the systems, existing design approaches rely heavily on trial-and-error learning. By leveraging existing experimental data, Materials Quantitative Structure-Property Relationships (MQSPRs) relate molecular structures to the polar and dispersive components of corresponding surface tensions. In turn, existing theories relate polymer and nanofiller polar and dispersive surface tension components to the dispersion state and interfacial polymer relaxation times. These quantities may, in the future, be used as input to continuum mechanics approaches shown able to predict the thermomechanical response of nanocomposites. For a polymer dataset and a particle dataset, multiple structural representations and descriptor sets are benchmarked, including a set of high performance surface-property descriptors developed as part of this work. The systematic variation of structural representations as part of the informatics approach reveals important insight in modeling polymers, and should become common practice when defining new problem spaces.

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

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.

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.

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.

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.

Structure-reactivity modeling using mixture-based representation of chemical reactions.

PubMed

Polishchuk, Pavel; Madzhidov, Timur; Gimadiev, Timur; Bodrov, Andrey; Nugmanov, Ramil; Varnek, Alexandre

2017-09-01

We describe a novel approach of reaction representation as a combination of two mixtures: a mixture of reactants and a mixture of products. In turn, each mixture can be encoded using an earlier reported approach involving simplex descriptors (SiRMS). The feature vector representing these two mixtures results from either concatenated product and reactant descriptors or the difference between descriptors of products and reactants. This reaction representation doesn't need an explicit labeling of a reaction center. The rigorous "product-out" cross-validation (CV) strategy has been suggested. Unlike the naïve "reaction-out" CV approach based on a random selection of items, the proposed one provides with more realistic estimation of prediction accuracy for reactions resulting in novel products. The new methodology has been applied to model rate constants of E2 reactions. It has been demonstrated that the use of the fragment control domain applicability approach significantly increases prediction accuracy of the models. The models obtained with new "mixture" approach performed better than those required either explicit (Condensed Graph of Reaction) or implicit (reaction fingerprints) reaction center labeling.

Informing the Human Plasma Protein Binding of Environmental Chemicals by Machine Learning in the Pharmaceutical Space: Applicability Domain and Limits of Predictability.

PubMed

Ingle, Brandall L; Veber, Brandon C; Nichols, John W; Tornero-Velez, Rogelio

2016-11-28

The free fraction of a xenobiotic in plasma (F ub ) is an important determinant of chemical adsorption, distribution, metabolism, elimination, and toxicity, yet experimental plasma protein binding data are scarce for environmentally relevant chemicals. The presented work explores the merit of utilizing available pharmaceutical data to predict F ub for environmentally relevant chemicals via machine learning techniques. Quantitative structure-activity relationship (QSAR) models were constructed with k nearest neighbors (kNN), support vector machines (SVM), and random forest (RF) machine learning algorithms from a training set of 1045 pharmaceuticals. The models were then evaluated with independent test sets of pharmaceuticals (200 compounds) and environmentally relevant ToxCast chemicals (406 total, in two groups of 238 and 168 compounds). The selection of a minimal feature set of 10-15 2D molecular descriptors allowed for both informative feature interpretation and practical applicability domain assessment via a bounded box of descriptor ranges and principal component analysis. The diverse pharmaceutical and environmental chemical sets exhibit similarities in terms of chemical space (99-82% overlap), as well as comparable bias and variance in constructed learning curves. All the models exhibit significant predictability with mean absolute errors (MAE) in the range of 0.10-0.18F ub . The models performed best for highly bound chemicals (MAE 0.07-0.12), neutrals (MAE 0.11-0.14), and acids (MAE 0.14-0.17). A consensus model had the highest accuracy across both pharmaceuticals (MAE 0.151-0.155) and environmentally relevant chemicals (MAE 0.110-0.131). The inclusion of the majority of the ToxCast test sets within the AD of the consensus model, coupled with high prediction accuracy for these chemicals, indicates the model provides a QSAR for F ub that is broadly applicable to both pharmaceuticals and environmentally relevant chemicals.

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.

Quantum-chemical Calculations in the Study of Antitumour Compounds

NASA Astrophysics Data System (ADS)

Luzhkov, V. B.; Bogdanov, G. N.

1986-01-01

The results of quantum-chemical calculations on antitumour preparations concerning the mechanism of their action at the electronic and molecular levels and structure-activity correlations are discussed in this review. Preparations whose action involves alkylating and free-radial mechanisms, complex-forming agents, and antimetabolites are considered. Modern quantum-chemical methods for calculations on biologically active substances are described. The bibliography includes 106 references.

On the physical nature of halogen bonds: a QTAIM study.

PubMed

Syzgantseva, Olga A; Tognetti, Vincent; Joubert, Laurent

2013-09-12

In this article, we report a detailed study on halogen bonds in complexes of CHCBr, CHCCl, CH2CHBr, FBr, FCl, and ClBr with a set of Lewis bases (NH3, OH2, SH2, OCH2, OH(-), Br(-)). To obtain insight into the physical nature of these bonds, we extensively used Bader's Quantum Theory of Atoms-in-Molecules (QTAIM). With this aim, in addition to the examination of the bond critical points properties, we apply Pendás' Interacting Quantum Atoms (IQA) scheme, which enables rigorous and physical study of each interaction at work in the formation of the halogen-bonded complexes. In particular, the influence of primary and secondary interactions on the stability of the complexes is analyzed, and the roles of electrostatics and exchange are notably discussed and compared. Finally, relationships between QTAIM descriptors and binding energies are inspected.

Quantum-chemical insights from deep tensor neural networks

PubMed Central

Schütt, Kristof T.; Arbabzadah, Farhad; Chmiela, Stefan; Müller, Klaus R.; Tkatchenko, Alexandre

2017-01-01

Learning from data has led to paradigm shifts in a multitude of disciplines, including web, text and image search, speech recognition, as well as bioinformatics. Can machine learning enable similar breakthroughs in understanding quantum many-body systems? Here we develop an efficient deep learning approach that enables spatially and chemically resolved insights into quantum-mechanical observables of molecular systems. We unify concepts from many-body Hamiltonians with purpose-designed deep tensor neural networks, which leads to size-extensive and uniformly accurate (1 kcal mol−1) predictions in compositional and configurational chemical space for molecules of intermediate size. As an example of chemical relevance, the model reveals a classification of aromatic rings with respect to their stability. Further applications of our model for predicting atomic energies and local chemical potentials in molecules, reliable isomer energies, and molecules with peculiar electronic structure demonstrate the potential of machine learning for revealing insights into complex quantum-chemical systems. PMID:28067221

Quantum-chemical insights from deep tensor neural networks.

PubMed

Schütt, Kristof T; Arbabzadah, Farhad; Chmiela, Stefan; Müller, Klaus R; Tkatchenko, Alexandre

2017-01-09

Learning from data has led to paradigm shifts in a multitude of disciplines, including web, text and image search, speech recognition, as well as bioinformatics. Can machine learning enable similar breakthroughs in understanding quantum many-body systems? Here we develop an efficient deep learning approach that enables spatially and chemically resolved insights into quantum-mechanical observables of molecular systems. We unify concepts from many-body Hamiltonians with purpose-designed deep tensor neural networks, which leads to size-extensive and uniformly accurate (1 kcal mol -1 ) predictions in compositional and configurational chemical space for molecules of intermediate size. As an example of chemical relevance, the model reveals a classification of aromatic rings with respect to their stability. Further applications of our model for predicting atomic energies and local chemical potentials in molecules, reliable isomer energies, and molecules with peculiar electronic structure demonstrate the potential of machine learning for revealing insights into complex quantum-chemical systems.

Quantum-chemical insights from deep tensor neural networks

NASA Astrophysics Data System (ADS)

Schütt, Kristof T.; Arbabzadah, Farhad; Chmiela, Stefan; Müller, Klaus R.; Tkatchenko, Alexandre

2017-01-01

Learning from data has led to paradigm shifts in a multitude of disciplines, including web, text and image search, speech recognition, as well as bioinformatics. Can machine learning enable similar breakthroughs in understanding quantum many-body systems? Here we develop an efficient deep learning approach that enables spatially and chemically resolved insights into quantum-mechanical observables of molecular systems. We unify concepts from many-body Hamiltonians with purpose-designed deep tensor neural networks, which leads to size-extensive and uniformly accurate (1 kcal mol-1) predictions in compositional and configurational chemical space for molecules of intermediate size. As an example of chemical relevance, the model reveals a classification of aromatic rings with respect to their stability. Further applications of our model for predicting atomic energies and local chemical potentials in molecules, reliable isomer energies, and molecules with peculiar electronic structure demonstrate the potential of machine learning for revealing insights into complex quantum-chemical systems.

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

Evaluating the Energetic Driving Force for Cocrystal Formation

PubMed Central

2017-01-01

We present a periodic density functional theory study of the stability of 350 organic cocrystals relative to their pure single-component structures, the largest study of cocrystals yet performed with high-level computational methods. Our calculations demonstrate that cocrystals are on average 8 kJ mol–1 more stable than their constituent single-component structures and are very rarely (<5% of cases) less stable; cocrystallization is almost always a thermodynamically favorable process. We consider the variation in stability between different categories of systems—hydrogen-bonded, halogen-bonded, and weakly bound cocrystals—finding that, contrary to chemical intuition, the presence of hydrogen or halogen bond interactions is not necessarily a good predictor of stability. Finally, we investigate the correlation of the relative stability with simple chemical descriptors: changes in packing efficiency and hydrogen bond strength. We find some broad qualitative agreement with chemical intuition—more densely packed cocrystals with stronger hydrogen bonding tend to be more stable—but the relationship is weak, suggesting that such simple descriptors do not capture the complex balance of interactions driving cocrystallization. Our conclusions suggest that while cocrystallization is often a thermodynamically favorable process, it remains difficult to formulate general rules to guide synthesis, highlighting the continued importance of high-level computation in predicting and rationalizing such systems. PMID:29445316

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.

Evaluating the Energetic Driving Force for Cocrystal Formation.

PubMed

Taylor, Christopher R; Day, Graeme M

2018-02-07

We present a periodic density functional theory study of the stability of 350 organic cocrystals relative to their pure single-component structures, the largest study of cocrystals yet performed with high-level computational methods. Our calculations demonstrate that cocrystals are on average 8 kJ mol -1 more stable than their constituent single-component structures and are very rarely (<5% of cases) less stable; cocrystallization is almost always a thermodynamically favorable process. We consider the variation in stability between different categories of systems-hydrogen-bonded, halogen-bonded, and weakly bound cocrystals-finding that, contrary to chemical intuition, the presence of hydrogen or halogen bond interactions is not necessarily a good predictor of stability. Finally, we investigate the correlation of the relative stability with simple chemical descriptors: changes in packing efficiency and hydrogen bond strength. We find some broad qualitative agreement with chemical intuition-more densely packed cocrystals with stronger hydrogen bonding tend to be more stable-but the relationship is weak, suggesting that such simple descriptors do not capture the complex balance of interactions driving cocrystallization. Our conclusions suggest that while cocrystallization is often a thermodynamically favorable process, it remains difficult to formulate general rules to guide synthesis, highlighting the continued importance of high-level computation in predicting and rationalizing such systems.

Antimicrobial Peptides Containing Unnatural Amino Acid Exhibit Potent Bactericidal Activity against ESKAPE Pathogens

DTIC Science & Technology

2013-01-01

compositions of these twobacteria’s cellmembranes are very differ- ent. The results of two 3D- QSARs (quantitative structure–activity relationship) studies...determined that there are five major physico- chemical descriptors necessary to define the activity of these AMPs in the S. aureus QSAR model.62 Five

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.

Mechanistic quantitative structure-activity relationship model for the photoinduced toxicity of polycyclic aromatic hydrocarbons. 2: An empirical model for the toxicity of 16 polycyclic aromatic hydrocarbons to the duckweed Lemna gibba L. G-3

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

Huang, X.D.; Krylov, S.N.; Ren, L.

1997-11-01

Photoinduced toxicity of polycyclic aromatic hydrocarbons (PAHs) occurs via photosensitization reactions (e.g., generation of singlet-state oxygen) and by photomodification (photooxidation and/or photolysis) of the chemicals to more toxic species. The quantitative structure-activity relationship (QSAR) described in the companion paper predicted, in theory, that photosensitization and photomodification additively contribute to toxicity. To substantiate this QSAR modeling exercise it was necessary to show that toxicity can be described by empirically derived parameters. The toxicity of 16 PAHs to the duckweed Lemna gibba was measured as inhibition of leaf production in simulated solar radiation (a light source with a spectrum similar to thatmore » of sunlight). A predictive model for toxicity was generated based on the theoretical model developed in the companion paper. The photophysical descriptors required of each PAH for modeling were efficiency of photon absorbance, relative uptake, quantum yield for triplet-state formation, and the rate of photomodification. The photomodification rates of the PAHs showed a moderate correlation to toxicity, whereas a derived photosensitization factor (PSF; based on absorbance, triplet-state quantum yield, and uptake) for each PAH showed only a weak, complex correlation to toxicity. However, summing the rate of photomodification and the PSF resulted in a strong correlation to toxicity that had predictive value. When the PSF and a derived photomodification factor (PMF; based on the photomodification rate and toxicity of the photomodified PAHs) were summed, an excellent explanatory model of toxicity was produced, substantiating the additive contributions of the two factors.« less

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.

Quantum chemical studies of estrogenic compounds

USDA-ARS?s Scientific Manuscript database

Quantum chemical methods are potent tools to provide information on the chemical structure and electronic properties of organic molecules. Modern computational chemistry methods have provided a great deal of insight into the binding of estrogenic compounds to estrogenic receptors (ER), an important ...

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.

Discovery of Novel HIV-1 Integrase Inhibitors Using QSAR-Based Virtual Screening of the NCI Open Database.

PubMed

Ko, Gene M; Garg, Rajni; Bailey, Barbara A; Kumar, Sunil

2016-01-01

Quantitative structure-activity relationship (QSAR) models can be used as a predictive tool for virtual screening of chemical libraries to identify novel drug candidates. The aims of this paper were to report the results of a study performed for descriptor selection, QSAR model development, and virtual screening for identifying novel HIV-1 integrase inhibitor drug candidates. First, three evolutionary algorithms were compared for descriptor selection: differential evolution-binary particle swarm optimization (DE-BPSO), binary particle swarm optimization, and genetic algorithms. Next, three QSAR models were developed from an ensemble of multiple linear regression, partial least squares, and extremely randomized trees models. A comparison of the performances of three evolutionary algorithms showed that DE-BPSO has a significant improvement over the other two algorithms. QSAR models developed in this study were used in consensus as a predictive tool for virtual screening of the NCI Open Database containing 265,242 compounds to identify potential novel HIV-1 integrase inhibitors. Six compounds were predicted to be highly active (plC50 > 6) by each of the three models. The use of a hybrid evolutionary algorithm (DE-BPSO) for descriptor selection and QSAR model development in drug design is a novel approach. Consensus modeling may provide better predictivity by taking into account a broader range of chemical properties within the data set conducive for inhibition that may be missed by an individual model. The six compounds identified provide novel drug candidate leads in the design of next generation HIV- 1 integrase inhibitors targeting drug resistant mutant viruses.

Integrating in Silico and in Vitro Approaches To Predict Drug Accessibility to the Central Nervous System.

PubMed

Zhang, Yan-Yan; Liu, Houfu; Summerfield, Scott G; Luscombe, Christopher N; Sahi, Jasminder

2016-05-02

Estimation of uptake across the blood-brain barrier (BBB) is key to designing central nervous system (CNS) therapeutics. In silico approaches ranging from physicochemical rules to quantitative structure-activity relationship (QSAR) models are utilized to predict potential for CNS penetration of new chemical entities. However, there are still gaps in our knowledge of (1) the relationship between marketed human drug derived CNS-accessible chemical space and preclinical neuropharmacokinetic (neuroPK) data, (2) interpretability of the selected physicochemical descriptors, and (3) correlation of the in vitro human P-glycoprotein (P-gp) efflux ratio (ER) and in vivo rodent unbound brain-to-blood ratio (Kp,uu), as these are assays routinely used to predict clinical CNS exposure, during drug discovery. To close these gaps, we explored the CNS druglike property boundaries of 920 market oral drugs (315 CNS and 605 non-CNS) and 846 compounds (54 CNS drugs and 792 proprietary GlaxoSmithKline compounds) with available rat Kp,uu data. The exact permeability coefficient (Pexact) and P-gp ER were determined for 176 compounds from the rat Kp,uu data set. Receiver operating characteristic curves were performed to evaluate the predictive power of human P-gp ER for rat Kp,uu. Our data demonstrates that simple physicochemical rules (most acidic pKa ≥ 9.5 and TPSA < 100) in combination with P-gp ER < 1.5 provide mechanistic insights for filtering BBB permeable compounds. For comparison, six classification modeling methods were investigated using multiple sets of in silico molecular descriptors. We present a random forest model with excellent predictive power (∼0.75 overall accuracy) using the rat neuroPK data set. We also observed good concordance between the structural interpretation results and physicochemical descriptor importance from the Kp,uu classification QSAR model. In summary, we propose a novel, hybrid in silico/in vitro approach and an in silico screening model for the effective development of chemical series with the potential to achieve optimal CNS exposure.

Development of QSAR models for predicting the binding affinity of endocrine disrupting chemicals to eight fish estrogen receptor.

PubMed

He, Junyi; Peng, Tao; Yang, Xianhai; Liu, Huihui

2018-02-01

Endocrine disrupting effect has become a central point of concern, and various biological mechanisms involve in the disruption of endocrine system. Recently, we have explored the mechanism of disrupting hormonal transport protein, through the binding affinity of sex hormone-binding globulin in different fish species. This study, serving as a companion article, focused on the mechanism of activating/inhibiting hormone receptor, by investigating the binding interaction of chemicals with the estrogen receptor (ER) of different fish species. We collected the relative binding affinity (RBA) of chemicals with 17β-estradiol binding to the ER of eight fish species. With this parameter as the endpoints, quantitative structure-activity relationship (QSAR) models were established using DRAGON descriptors. Statistical results indicated that the developed models had satisfactory goodness of fit, robustness and predictive ability. The Euclidean distance and Williams plot verified that these models had wide application domains, which covered a large number of structurally diverse chemicals. Based on the screened descriptors, we proposed an appropriate mechanism interpretation for the binding potency. Additionally, even though the same chemical had different affinities for ER from different fish species, the affinity of ER exhibited a high correlation for fish species within the same Order (i.e., Salmoniformes, Cypriniformes, Perciformes), which consistent with that in our previous study. Hence, when performing the endocrine disrupting effect assessment, the species diversity should be taken into account, but maybe the fish species in the same Order can be grouped together. Copyright © 2017 Elsevier Inc. All rights reserved.

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.

Quantum Monte Carlo tunneling from quantum chemistry to quantum annealing

NASA Astrophysics Data System (ADS)

Mazzola, Guglielmo; Smelyanskiy, Vadim N.; Troyer, Matthias

2017-10-01

Quantum tunneling is ubiquitous across different fields, from quantum chemical reactions and magnetic materials to quantum simulators and quantum computers. While simulating the real-time quantum dynamics of tunneling is infeasible for high-dimensional systems, quantum tunneling also shows up in quantum Monte Carlo (QMC) simulations, which aim to simulate quantum statistics with resources growing only polynomially with the system size. Here we extend the recent results obtained for quantum spin models [Phys. Rev. Lett. 117, 180402 (2016), 10.1103/PhysRevLett.117.180402], and we study continuous-variable models for proton transfer reactions. We demonstrate that QMC simulations efficiently recover the scaling of ground-state tunneling rates due to the existence of an instanton path, which always connects the reactant state with the product. We discuss the implications of our results in the context of quantum chemical reactions and quantum annealing, where quantum tunneling is expected to be a valuable resource for solving combinatorial optimization problems.

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.

Chemosensory characteristics of regional Vidal icewines from China and Canada.

PubMed

Huang, Ling; Ma, Yue; Tian, Xin; Li, Ji-Ming; Li, Lan-Xiao; Tang, Ke; Xu, Yan

2018-09-30

This work aimed to compare the flavor characteristics of Vidal icewines from China and Canada and to establish relationships between sensory descriptors and chemical composition. Descriptive analysis was performed with a trained panel to obtain the sensory profiles. Thirty important aroma-active compounds were quantified by four different methodologies. Partial least squares discriminant analysis was used to identify candidate compounds, which were unique to certain sensory descriptors. The sensory profiles of icewines from China were characterized by nut and honey aromas, while icewines from Canada expressed caramel and rose aromas. Nut and honey aromas had a close correlation with 1-hexanol, isoamyl acetate, phenethyl acetate and phenylethyl alcohol. Caramel aroma was correlated with ethyl esters and lactones and rose aroma was correlated with terpenes. Copyright © 2018 Elsevier Ltd. All rights reserved.

Macrophytes, epipelic biofilm, and invertebrates as biotic indicators of physical habitat degradation of lowland streams (Argentina).

PubMed

Cortelezzi, Agustina; Sierra, María Victoria; Gómez, Nora; Marinelli, Claudia; Rodrigues Capítulo, Alberto

2013-07-01

Our objective was to assess the effect of the physical habitat degradation in three lowland streams of Argentina that are subject to different land uses. To address this matter, we looked into some physical habitat alterations, mainly the water quality and channel changes, the impact on macrophytes' community, and the structural and functional descriptors of the epipelic biofilm and invertebrate assemblages. As a consequence of physical and chemical perturbations, we differentiated sampling sites with different degradation levels. The low degraded sites were affected mainly for the suburban land use, the moderately degraded sites for the rural land use, and the highly degraded sites for the urban land use. The data shows that the biotic descriptors that best reflected the environmental degradation were vegetation cover and macrophytes richness, the dominance of tolerant species (epipelic biofilm and invertebrates), algal biomass, O2 consumption by the epipelic biofilm, and invertebrates' richness and diversity. Furthermore, the results obtained highlight the importance of the macrophytes in the lowland streams, where there is a poor diversification of abiotic substrates and where the macrophytes not only provide shelter but also a food source for invertebrates and other trophic levels such as fish. We also noted that both in benthic communities, invertebrates and epipelic biofilm supplied different information: the habitat's physical structure provided by the macrophytes influenced mainly the invertebrate descriptors; meanwhile, the water quality mainly influenced most of the epipelic biofilm descriptors.

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.

A combined experimental (IR, Raman and UV-Vis) and quantum chemical study of canadine

NASA Astrophysics Data System (ADS)

Joshi, Bhawani Datt; Srivastava, Anubha; Tandon, Poonam; Jain, Sudha; Ayala, A. P.

2018-02-01

Plant based natural products cover a major sector of the medicinal field, as such focus on plant research has been increased all over the world. As an attempt to aid that research, we have performed structural and spectroscopic analysis of a natural product, an alkaloid: canadine. Both ab initio Hartree-Fock (HF) and density functional theory (DFT) employing B3LYP using 6-311 ++G(d,p) basis set were used for the calculations. The calculated vibrational frequencies were scaled and compared with the experimental infrared and Raman spectra. The complete vibrational assignments were made using potential energy distribution. The structure-activity relation has also been interpreted by mapping electrostatic potential surface and evaluating the reactivity descriptors, which are valuable information for quality control of medicines and drug-receptor interactions. Natural bond orbital analysis has also been performed to understand the stability and hyperconjugative interactions of the molecule. Furthermore, UV-Vis spectra have been recorded in an ethanol solvent (EtOH) and the electronic property has been analyzed employing TD-DFT for both gaseous and solvent phase. The HOMO and LUMO calculation with their energy gap show that charge transfer occurs within the molecule. Additionally, the nonlinear optical properties of the title compound have been interpreted that predicts it's the best candidate for the NLO materials.

Analysis and Identification of Aptamer-Compound Interactions with a Maximum Relevance Minimum Redundancy and Nearest Neighbor Algorithm

PubMed Central

Wang, ShaoPeng; Zhang, Yu-Hang; Lu, Jing; Cui, Weiren; Hu, Jerry; Cai, Yu-Dong

2016-01-01

The development of biochemistry and molecular biology has revealed an increasingly important role of compounds in several biological processes. Like the aptamer-protein interaction, aptamer-compound interaction attracts increasing attention. However, it is time-consuming to select proper aptamers against compounds using traditional methods, such as exponential enrichment. Thus, there is an urgent need to design effective computational methods for searching effective aptamers against compounds. This study attempted to extract important features for aptamer-compound interactions using feature selection methods, such as Maximum Relevance Minimum Redundancy, as well as incremental feature selection. Each aptamer-compound pair was represented by properties derived from the aptamer and compound, including frequencies of single nucleotides and dinucleotides for the aptamer, as well as the constitutional, electrostatic, quantum-chemical, and space conformational descriptors of the compounds. As a result, some important features were obtained. To confirm the importance of the obtained features, we further discussed the associations between them and aptamer-compound interactions. Simultaneously, an optimal prediction model based on the nearest neighbor algorithm was built to identify aptamer-compound interactions, which has the potential to be a useful tool for the identification of novel aptamer-compound interactions. The program is available upon the request. PMID:26955638

Predicting the photoinduced electron transfer thermodynamics in polyfluorinated 1,3,5-triarylpyrazolines based on multiple linear free energy relationships†

PubMed Central

Verma, Manjusha; Chaudhry, Aneese F.; Fahrni, Christoph J.

2010-01-01

The photophysical properties of 1,3,5-triarylpyrazolines are strongly influenced by the nature and position of substituents attached to the aryl-rings, rendering this fluorophore platform well suited for the design of fluorescent probes utilizing a photoinduced electron transfer (PET) switching mechanism. To explore the tunability of two key parameters that govern the PET thermodynamics, the excited state energy ΔE00 and acceptor potential E(A/A−), a library of polyfluoro-substituted 1,3-diaryl-5-phenyl-pyrazolines was synthesized and characterized. The observed trends for the PET parameters were effectively captured through multiple Hammett linear free energy relationships (LFER) using a set of independent substituent constants for each of the two aryl rings. Given the lack of experimental Hammett constants for polyfluoro substituted aromatics, theoretically derived constants based on the electrostatic potential at the nucleus (EPN) of carbon atoms were employed as quantum chemical descriptors. The performance of the LFER was evaluated with a set of compounds that were not included in the training set, yielding a mean unsigned error of 0.05 eV for the prediction of the combined PET parameters. The outlined LFER approach should be well suited to design and optimize the performance of cation-responsive 1,3,5-triarylpyrazolines. PMID:19343239

An accurate and efficient method to predict the electronic excitation energies of BODIPY fluorescent dyes.

PubMed

Wang, Jia-Nan; Jin, Jun-Ling; Geng, Yun; Sun, Shi-Ling; Xu, Hong-Liang; Lu, Ying-Hua; Su, Zhong-Min

2013-03-15

Recently, the extreme learning machine neural network (ELMNN) as a valid computing method has been proposed to predict the nonlinear optical property successfully (Wang et al., J. Comput. Chem. 2012, 33, 231). In this work, first, we follow this line of work to predict the electronic excitation energies using the ELMNN method. Significantly, the root mean square deviation of the predicted electronic excitation energies of 90 4,4-difluoro-4-bora-3a,4a-diaza-s-indacene (BODIPY) derivatives between the predicted and experimental values has been reduced to 0.13 eV. Second, four groups of molecule descriptors are considered when building the computing models. The results show that the quantum chemical descriptions have the closest intrinsic relation with the electronic excitation energy values. Finally, a user-friendly web server (EEEBPre: Prediction of electronic excitation energies for BODIPY dyes), which is freely accessible to public at the web site: http://202.198.129.218, has been built for prediction. This web server can return the predicted electronic excitation energy values of BODIPY dyes that are high consistent with the experimental values. We hope that this web server would be helpful to theoretical and experimental chemists in related research. Copyright © 2012 Wiley Periodicals, Inc.

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.

Analysis and Identification of Aptamer-Compound Interactions with a Maximum Relevance Minimum Redundancy and Nearest Neighbor Algorithm.

PubMed

Wang, ShaoPeng; Zhang, Yu-Hang; Lu, Jing; Cui, Weiren; Hu, Jerry; Cai, Yu-Dong

2016-01-01

The development of biochemistry and molecular biology has revealed an increasingly important role of compounds in several biological processes. Like the aptamer-protein interaction, aptamer-compound interaction attracts increasing attention. However, it is time-consuming to select proper aptamers against compounds using traditional methods, such as exponential enrichment. Thus, there is an urgent need to design effective computational methods for searching effective aptamers against compounds. This study attempted to extract important features for aptamer-compound interactions using feature selection methods, such as Maximum Relevance Minimum Redundancy, as well as incremental feature selection. Each aptamer-compound pair was represented by properties derived from the aptamer and compound, including frequencies of single nucleotides and dinucleotides for the aptamer, as well as the constitutional, electrostatic, quantum-chemical, and space conformational descriptors of the compounds. As a result, some important features were obtained. To confirm the importance of the obtained features, we further discussed the associations between them and aptamer-compound interactions. Simultaneously, an optimal prediction model based on the nearest neighbor algorithm was built to identify aptamer-compound interactions, which has the potential to be a useful tool for the identification of novel aptamer-compound interactions. The program is available upon the request.

Electron-beam generated porous dextran gels: experimental and quantum chemical studies.

PubMed

Naumov, Sergej; Knolle, Wolfgang; Becher, Jana; Schnabelrauch, Matthias; Reichelt, Senta

2014-06-01

The aim of this work was to investigate the reaction mechanism of electron-beam generated macroporous dextran cryogels by quantum chemical calculation and electron paramagnetic resonance measurements. Electron-beam radiation was used to initiate the cross-linking reaction of methacrylated dextran in semifrozen aqueous solutions. The pore morphology of the resulting cryogels was visualized by scanning electron microscopy. Quantum chemical calculations and electron paramagnetic resonance studies provided information on the most probable reaction pathway and the chain growth radicals. The most probable reaction pathway was a ring opening reaction and the addition of a C-atom to the double-bond of the methacrylated dextran molecule. First detailed quantum chemical calculation on the reaction mechanism of electron-beam initiated cross-linking reaction of methacrylated dextran are presented.

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...

Superlattice photoelectrodes for photoelectrochemical cells

DOEpatents

Nozik, Arthur J.

1987-01-01

A superlattice or multiple-quantum-well semiconductor is used as a photoelectrode in a photoelectrochemical process for converting solar energy into useful fuels or chemicals. The quantum minibands of the superlattice or multiple-quantum-well semiconductor effectively capture hot-charge carriers at or near their discrete quantum energies and deliver them to drive a chemical reaction in an electrolyte. The hot-charge carries can be injected into the electrolyte at or near the various discrete multiple energy levels quantum minibands, or they can be equilibrated among themselves to a hot-carrier pool and then injected into the electrolyte at one average energy that is higher than the lowest quantum band gap in the semiconductor.

Exploring the potential of 3D Zernike descriptors and SVM for protein-protein interface prediction.

PubMed

Daberdaku, Sebastian; Ferrari, Carlo

2018-02-06

The correct determination of protein-protein interaction interfaces is important for understanding disease mechanisms and for rational drug design. To date, several computational methods for the prediction of protein interfaces have been developed, but the interface prediction problem is still not fully understood. Experimental evidence suggests that the location of binding sites is imprinted in the protein structure, but there are major differences among the interfaces of the various protein types: the characterising properties can vary a lot depending on the interaction type and function. The selection of an optimal set of features characterising the protein interface and the development of an effective method to represent and capture the complex protein recognition patterns are of paramount importance for this task. In this work we investigate the potential of a novel local surface descriptor based on 3D Zernike moments for the interface prediction task. Descriptors invariant to roto-translations are extracted from circular patches of the protein surface enriched with physico-chemical properties from the HQI8 amino acid index set, and are used as samples for a binary classification problem. Support Vector Machines are used as a classifier to distinguish interface local surface patches from non-interface ones. The proposed method was validated on 16 classes of proteins extracted from the Protein-Protein Docking Benchmark 5.0 and compared to other state-of-the-art protein interface predictors (SPPIDER, PrISE and NPS-HomPPI). The 3D Zernike descriptors are able to capture the similarity among patterns of physico-chemical and biochemical properties mapped on the protein surface arising from the various spatial arrangements of the underlying residues, and their usage can be easily extended to other sets of amino acid properties. The results suggest that the choice of a proper set of features characterising the protein interface is crucial for the interface prediction task, and that optimality strongly depends on the class of proteins whose interface we want to characterise. We postulate that different protein classes should be treated separately and that it is necessary to identify an optimal set of features for each protein class.

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.

Integration of Quantum Confinement and Alloy Effect to Modulate Electronic Properties of RhW Nanocrystals for Improved Catalytic Performance toward CO2 Hydrogenation.

PubMed

Zhang, Wenbo; Wang, Liangbing; Liu, Haoyu; Hao, Yiping; Li, Hongliang; Khan, Munir Ullah; Zeng, Jie

2017-02-08

The d-band center and surface negative charge density generally determine the adsorption and activation of CO 2 , thus serving as important descriptors of the catalytic activity toward CO 2 hydrogenation. Herein, we engineered the d-band center and negative charge density of Rh-based catalysts by tuning their dimensions and introducing non-noble metals to form an alloy. During the hydrogenation of CO 2 into methanol, the catalytic activity of Rh 75 W 25 nanosheets was 5.9, 4.0, and 1.7 times as high as that of Rh nanoparticles, Rh nanosheets, and Rh 73 W 27 nanoparticles, respectively. Mechanistic studies reveal that the remarkable activity of Rh 75 W 25 nanosheets is owing to the integration of quantum confinement and alloy effect. Specifically, the quantum confinement in one dimension shifts up the d-band center of Rh 75 W 25 nanosheets, strengthening the adsorption of CO 2 . Moreover, the alloy effect not only promotes the activation of CO 2 to form CO 2 δ- but also enhances the adsorption of intermediates to facilitate further hydrogenation of the intermediates into methanol.

Extraction of information from major element chemical analyses of lunar basalts

NASA Technical Reports Server (NTRS)

Butler, J. C.

1985-01-01

Major element chemical analyses often form the framework within which similarities and differences of analyzed specimens are noted and used to propose or devise models. When percentages are formed the ratios of pairs of components are preserved whereas many familiar statistical and geometrical descriptors are likely to exhibit major changes. This ratio preserving aspect forms the basis for a proposed framework. An analysis of compositional variability within the data set of 42 major element analyses of lunar reference samples was selected to investigate this proposal.

Automated first-principles mapping for phase-change materials.

PubMed

Esser, Marc; Maintz, Stefan; Dronskowski, Richard

2017-04-05

Plotting materials on bi-coordinate maps according to physically meaningful descriptors has a successful tradition in computational solid-state science spanning more than four decades. Equipped with new ab initio techniques introduced in this work, we generate an improved version of the treasure map for phase-change materials (PCMs) as introduced previously by Lencer et al. which, other than before, charts all industrially used PCMs correctly. Furthermore, we suggest seven new PCM candidates, namely SiSb 4 Te 7 , Si 2 Sb 2 Te 5 , SiAs 2 Te 4 , PbAs 2 Te 4 , SiSb 2 Te 4 , Sn 2 As 2 Te 5 , and PbAs 4 Te 7 , to be used as synthetic targets. To realize aforementioned maps based on orbital mixing (or "hybridization") and ionicity coordinates, structural information was first included into an ab initio numerical descriptor for sp 3 orbital mixing and then generalized beyond high-symmetry structures. In addition, a simple, yet powerful quantum-mechanical ionization measure also including structural information was introduced. Taken together, these tools allow for (automatically) generating materials maps solely relying on first-principles calculations. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Unveiling the nature of post-linear response Z-vector method for time-dependent density functional theory.

PubMed

Pastore, Mariachiara; Assfeld, Xavier; Mosconi, Edoardo; Monari, Antonio; Etienne, Thibaud

2017-07-14

We report a theoretical study on the analysis of the relaxed one-particle difference density matrix characterizing the passage from the ground to the excited state of a molecular system, as obtained from time-dependent density functional theory. In particular, this work aims at using the physics contained in the so-called Z-vector, which differentiates between unrelaxed and relaxed difference density matrices to analyze excited states' nature. For this purpose, we introduce novel quantum-mechanical quantities, based on the detachment/attachment methodology, for analysing the Z-vector transformation for different molecules and density functional theory functionals. A derivation pathway of these novel descriptors is reported, involving a numerical integration to be performed in the Euclidean space on the density functions. This topological analysis is then applied to two sets of chromophores, and the correlation between the level of theory and the behavior of our descriptors is properly rationalized. In particular, the effect of range-separation on the relaxation amplitude is discussed. The relaxation term is finally shown to be system-specific (for a given level of theory) and independent of the number of electrons (i.e., the relaxation amplitude is not simply the result of a collective phenomenon).

Revisiting caffeate's capabilities as a complexation agent to silver cation in mining processes by means of the dual descriptor--a conceptual DFT approach.

PubMed

Martínez-Araya, Jorge Ignacio

2012-09-01

Caffeic acid (C(9)H(8)O(4)) and its conjugate base C(9)H(7)O(4) (-) (anionic form-known as caffeate) were analyzed computationally through the use of quantum chemistry to assess their intrinsic global and local reactivity using the tools of conceptual density functional theory. The anionic form was found to be better at coordinating the silver cation than caffeic acid thus suggesting the use of caffeate as a complexation agent. The complexation capability of caffeate was compared with that of some of the most common ligand agents used to coordinate silver cations. Local reactivity descriptors allowed identification of the preferred sites on caffeate for silver cation coordination thus generating a plausible silver complex. All silver complexes were analyzed thermodynamically considering interaction energies in both gas and aqueous phases; the complexation free energy in aqueous phase was also determined. These results suggest that more attention be paid to the caffeate anion and its derivatives because this work has shed new light on the behavior of this anion in the recovery of silver cations that could be exploited in silver mining processes in a environmentally friendly way.

Quantum chemical approach to estimating the thermodynamics of metabolic reactions.

PubMed

Jinich, Adrian; Rappoport, Dmitrij; Dunn, Ian; Sanchez-Lengeling, Benjamin; Olivares-Amaya, Roberto; Noor, Elad; Even, Arren Bar; Aspuru-Guzik, Alán

2014-11-12

Thermodynamics plays an increasingly important role in modeling and engineering metabolism. We present the first nonempirical computational method for estimating standard Gibbs reaction energies of metabolic reactions based on quantum chemistry, which can help fill in the gaps in the existing thermodynamic data. When applied to a test set of reactions from core metabolism, the quantum chemical approach is comparable in accuracy to group contribution methods for isomerization and group transfer reactions and for reactions not including multiply charged anions. The errors in standard Gibbs reaction energy estimates are correlated with the charges of the participating molecules. The quantum chemical approach is amenable to systematic improvements and holds potential for providing thermodynamic data for all of metabolism.

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.

Developing Enhanced Blood–Brain Barrier Permeability Models: Integrating External Bio-Assay Data in QSAR Modeling

PubMed Central

Wang, Wenyi; Kim, Marlene T.; Sedykh, Alexander

2015-01-01

Purpose Experimental Blood–Brain Barrier (BBB) permeability models for drug molecules are expensive and time-consuming. As alternative methods, several traditional Quantitative Structure-Activity Relationship (QSAR) models have been developed previously. In this study, we aimed to improve the predictivity of traditional QSAR BBB permeability models by employing relevant public bio-assay data in the modeling process. Methods We compiled a BBB permeability database consisting of 439 unique compounds from various resources. The database was split into a modeling set of 341 compounds and a validation set of 98 compounds. Consensus QSAR modeling workflow was employed on the modeling set to develop various QSAR models. A five-fold cross-validation approach was used to validate the developed models, and the resulting models were used to predict the external validation set compounds. Furthermore, we used previously published membrane transporter models to generate relevant transporter profiles for target compounds. The transporter profiles were used as additional biological descriptors to develop hybrid QSAR BBB models. Results The consensus QSAR models have R2=0.638 for fivefold cross-validation and R2=0.504 for external validation. The consensus model developed by pooling chemical and transporter descriptors showed better predictivity (R2=0.646 for five-fold cross-validation and R2=0.526 for external validation). Moreover, several external bio-assays that correlate with BBB permeability were identified using our automatic profiling tool. Conclusions The BBB permeability models developed in this study can be useful for early evaluation of new compounds (e.g., new drug candidates). The combination of chemical and biological descriptors shows a promising direction to improve the current traditional QSAR models. PMID:25862462

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.

Quantum probability ranking principle for ligand-based virtual screening.

PubMed

Al-Dabbagh, Mohammed Mumtaz; Salim, Naomie; Himmat, Mubarak; Ahmed, Ali; Saeed, Faisal

2017-04-01

Chemical libraries contain thousands of compounds that need screening, which increases the need for computational methods that can rank or prioritize compounds. The tools of virtual screening are widely exploited to enhance the cost effectiveness of lead drug discovery programs by ranking chemical compounds databases in decreasing probability of biological activity based upon probability ranking principle (PRP). In this paper, we developed a novel ranking approach for molecular compounds inspired by quantum mechanics, called quantum probability ranking principle (QPRP). The QPRP ranking criteria would make an attempt to draw an analogy between the physical experiment and molecular structure ranking process for 2D fingerprints in ligand based virtual screening (LBVS). The development of QPRP criteria in LBVS has employed the concepts of quantum at three different levels, firstly at representation level, this model makes an effort to develop a new framework of molecular representation by connecting the molecular compounds with mathematical quantum space. Secondly, estimate the similarity between chemical libraries and references based on quantum-based similarity searching method. Finally, rank the molecules using QPRP approach. Simulated virtual screening experiments with MDL drug data report (MDDR) data sets showed that QPRP outperformed the classical ranking principle (PRP) for molecular chemical compounds.

Quantum probability ranking principle for ligand-based virtual screening

NASA Astrophysics Data System (ADS)

Al-Dabbagh, Mohammed Mumtaz; Salim, Naomie; Himmat, Mubarak; Ahmed, Ali; Saeed, Faisal

2017-04-01

Chemical libraries contain thousands of compounds that need screening, which increases the need for computational methods that can rank or prioritize compounds. The tools of virtual screening are widely exploited to enhance the cost effectiveness of lead drug discovery programs by ranking chemical compounds databases in decreasing probability of biological activity based upon probability ranking principle (PRP). In this paper, we developed a novel ranking approach for molecular compounds inspired by quantum mechanics, called quantum probability ranking principle (QPRP). The QPRP ranking criteria would make an attempt to draw an analogy between the physical experiment and molecular structure ranking process for 2D fingerprints in ligand based virtual screening (LBVS). The development of QPRP criteria in LBVS has employed the concepts of quantum at three different levels, firstly at representation level, this model makes an effort to develop a new framework of molecular representation by connecting the molecular compounds with mathematical quantum space. Secondly, estimate the similarity between chemical libraries and references based on quantum-based similarity searching method. Finally, rank the molecules using QPRP approach. Simulated virtual screening experiments with MDL drug data report (MDDR) data sets showed that QPRP outperformed the classical ranking principle (PRP) for molecular chemical compounds.

On the quantum mechanics of consciousness, with application to anomalous phenomena

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

Jahn, R.G.; Dunne, B.J.

1986-08-01

Theoretical explication of a growing body of empirical data on consciousness-related anomalous phenomena is unlikely to be achieved in terms of known physical processes. Rather, it will first be necessary to formulate the basic role of consciousness in the definition of reality before such anomalous experience can adequately be represented. This paper takes the position that reality is constituted only in the interaction of consciousness with its environment, and therefore that any scheme of conceptual organization developed to represent that reality must reflect the processes of consciousness as well as those of its environment. In this spirit, the concepts andmore » formalisms of elementary quantum mechanics, as originally proposed to explain anomalous atomic-scale physical phenomena, are appropriated via metaphor to represent the general characteristics of consciousness interacting with any environment. More specifically, if consciousness is represented by a quantum mechanical wave function, and its environment by an appropriate potential profile, Schrodinger wave mechanics defines eigenfunctions and eigenvalues that can be associated with the cognitive and emotional experiences of that consciousness in that environment. To articulate this metaphor it is necessary to associate certain aspects of the formalism, such as the coordinate system, the quantum numbers, and even the metric itself, with various impressionistic descriptors of consciousness, such as its intensity, perspective, approach/avoidance attitude, balance between cognitive and emotional activity, and receptive/assertive disposition.« less

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).

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.

Predicting the aquatic toxicity mode of action using logistic regression and linear discriminant analysis.

PubMed

Ren, Y Y; Zhou, L C; Yang, L; Liu, P Y; Zhao, B W; Liu, H X

2016-09-01

The paper highlights the use of the logistic regression (LR) method in the construction of acceptable statistically significant, robust and predictive models for the classification of chemicals according to their aquatic toxic modes of action. Essentials accounting for a reliable model were all considered carefully. The model predictors were selected by stepwise forward discriminant analysis (LDA) from a combined pool of experimental data and chemical structure-based descriptors calculated by the CODESSA and DRAGON software packages. Model predictive ability was validated both internally and externally. The applicability domain was checked by the leverage approach to verify prediction reliability. The obtained models are simple and easy to interpret. In general, LR performs much better than LDA and seems to be more attractive for the prediction of the more toxic compounds, i.e. compounds that exhibit excess toxicity versus non-polar narcotic compounds and more reactive compounds versus less reactive compounds. In addition, model fit and regression diagnostics was done through the influence plot which reflects the hat-values, studentized residuals, and Cook's distance statistics of each sample. Overdispersion was also checked for the LR model. The relationships between the descriptors and the aquatic toxic behaviour of compounds are also discussed.

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.

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.

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.

Application of classification trees for the qualitative differentiation of focal liver lesions suspicious for metastasis in gadolinium-EOB-DTPA-enhanced liver MR imaging.

PubMed

Schelhorn, J; Benndorf, M; Dietzel, M; Burmeister, H P; Kaiser, W A; Baltzer, P A T

2012-09-01

To evaluate the diagnostic accuracy of qualitative descriptors alone and in combination for the classification of focal liver lesions (FLLs) suspicious for metastasis in gadolinium-EOB-DTPA-enhanced liver MR imaging. Consecutive patients with clinically suspected liver metastases were eligible for this retrospective investigation. 50 patients met the inclusion criteria. All underwent Gd-EOB-DTPA-enhanced liver MRI (T2w, chemical shift T1w, dynamic T1w). Primary liver malignancies or treated lesions were excluded. All investigations were read by two blinded observers (O1, O2). Both independently identified the presence of lesions and evaluated predefined qualitative lesion descriptors (signal intensities, enhancement pattern and morphology). A reference standard was determined under consideration of all clinical and follow-up information. Statistical analysis besides contingency tables (chi square, kappa statistics) included descriptor combinations using classification trees (CHAID methodology) as well as ROC analysis. In 38 patients, 120 FLLs (52 benign, 68 malignant) were present. 115 (48 benign, 67 malignant) were identified by the observers. The enhancement pattern, relative SI upon T2w and late enhanced T1w images contributed significantly to the differentiation of FLLs. The overall classification accuracy was 91.3 % (O1) and 88.7 % (O2), kappa = 0.902. The combination of qualitative lesion descriptors proposed in this work revealed high diagnostic accuracy and interobserver agreement in the differentiation of focal liver lesions suspicious for metastases using Gd-EOB-DTPA-enhanced liver MRI. © Georg Thieme Verlag KG Stuttgart · New York.

Ionic and Covalent Stabilization of Intermediates and Transition States in Catalysis by Solid Acids

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

Deshlahra, Prashant; Carr, Robert T.; Iglesia, Enrique

Reactivity descriptors describe catalyst properties that determine the stability of kinetically relevant transition states and adsorbed intermediates. Theoretical descriptors, such as deprotonation energies (DPE), rigorously account for Brønsted acid strength for catalytic solids with known structure. Here, mechanistic interpretations of methanol dehydration turnover rates are used to assess how charge reorganization (covalency) and electrostatic interactions determine DPE and how such interactions are recovered when intermediates and transition states interact with the conjugate anion in W and Mo polyoxometalate (POM) clusters and gaseous mineral acids. Turnover rates are lower and kinetically relevant species are less stable on Mo than W POMmore » clusters with similar acid strength, and such species are more stable on mineral acids than that predicted from W-POM DPE–reactivity trends, indicating that DPE and acid strength are essential but incomplete reactivity descriptors. Born–Haber thermochemical cycles indicate that these differences reflect more effective charge reorganization upon deprotonation of Mo than W POM clusters and the much weaker reorganization in mineral acids. Such covalency is disrupted upon deprotonation but cannot be recovered fully upon formation of ion pairs at transition states. Predictive descriptors of reactivity for general classes of acids thus require separate assessments of the covalent and ionic DPE components. Here, we describe methods to estimate electrostatic interactions, which, taken together with energies derived from density functional theory, give the covalent and ionic energy components of protons, intermediates, and transition states. In doing so, we provide a framework to predict the reactive properties of protons for chemical reactions mediated by ion-pair transition states.« less

New molecular descriptors based on local properties at the molecular surface and a boiling-point model derived from them.

PubMed

Ehresmann, Bernd; de Groot, Marcel J; Alex, Alexander; Clark, Timothy

2004-01-01

New molecular descriptors based on statistical descriptions of the local ionization potential, local electron affinity, and the local polarizability at the surface of the molecule are proposed. The significance of these descriptors has been tested by calculating them for the Maybridge database in addition to our set of 26 descriptors reported previously. The new descriptors show little correlation with those already in use. Furthermore, the principal components of the extended set of descriptors for the Maybridge data show that especially the descriptors based on the local electron affinity extend the variance in our set of descriptors, which we have previously shown to be relevant to physical properties. The first nine principal components are shown to be most significant. As an example of the usefulness of the new descriptors, we have set up a QSPR model for boiling points using both the old and new descriptors.

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.

Cheminformatics-aided pharmacovigilance: application to Stevens-Johnson Syndrome

PubMed Central

Low, Yen S; Caster, Ola; Bergvall, Tomas; Fourches, Denis; Zang, Xiaoling; Norén, G Niklas; Rusyn, Ivan; Edwards, Ralph

2016-01-01

Objective Quantitative Structure-Activity Relationship (QSAR) models can predict adverse drug reactions (ADRs), and thus provide early warnings of potential hazards. Timely identification of potential safety concerns could protect patients and aid early diagnosis of ADRs among the exposed. Our objective was to determine whether global spontaneous reporting patterns might allow chemical substructures associated with Stevens-Johnson Syndrome (SJS) to be identified and utilized for ADR prediction by QSAR models. Materials and Methods Using a reference set of 364 drugs having positive or negative reporting correlations with SJS in the VigiBase global repository of individual case safety reports (Uppsala Monitoring Center, Uppsala, Sweden), chemical descriptors were computed from drug molecular structures. Random Forest and Support Vector Machines methods were used to develop QSAR models, which were validated by external 5-fold cross validation. Models were employed for virtual screening of DrugBank to predict SJS actives and inactives, which were corroborated using knowledge bases like VigiBase, ChemoText, and MicroMedex (Truven Health Analytics Inc, Ann Arbor, Michigan). Results We developed QSAR models that could accurately predict if drugs were associated with SJS (area under the curve of 75%–81%). Our 10 most active and inactive predictions were substantiated by SJS reports (or lack thereof) in the literature. Discussion Interpretation of QSAR models in terms of significant chemical descriptors suggested novel SJS structural alerts. Conclusions We have demonstrated that QSAR models can accurately identify SJS active and inactive drugs. Requiring chemical structures only, QSAR models provide effective computational means to flag potentially harmful drugs for subsequent targeted surveillance and pharmacoepidemiologic investigations. PMID:26499102

Quantum Chemical Approach to Estimating the Thermodynamics of Metabolic Reactions

PubMed Central

Jinich, Adrian; Rappoport, Dmitrij; Dunn, Ian; Sanchez-Lengeling, Benjamin; Olivares-Amaya, Roberto; Noor, Elad; Even, Arren Bar; Aspuru-Guzik, Alán

2014-01-01

Thermodynamics plays an increasingly important role in modeling and engineering metabolism. We present the first nonempirical computational method for estimating standard Gibbs reaction energies of metabolic reactions based on quantum chemistry, which can help fill in the gaps in the existing thermodynamic data. When applied to a test set of reactions from core metabolism, the quantum chemical approach is comparable in accuracy to group contribution methods for isomerization and group transfer reactions and for reactions not including multiply charged anions. The errors in standard Gibbs reaction energy estimates are correlated with the charges of the participating molecules. The quantum chemical approach is amenable to systematic improvements and holds potential for providing thermodynamic data for all of metabolism. PMID:25387603

Potential energy profile, structural, vibrational and reactivity descriptors of trans-2-methoxycinnamic acid by FTIR, FT-Raman and quantum chemical studies

NASA Astrophysics Data System (ADS)

Arjunan, V.; Anitha, R.; Thenmozhi, S.; Marchewka, M. K.; Mohan, S.

2016-06-01

The stable conformers of trans-2-methoxycinnamic acid (trans-2MCA) are determined by potential energy profile analysis. The energies of the s-cis and s-trans conformers of trans-2MCA determined by B3LYP/cc-pVTZ method are -612.9788331 Hartrees and -612.9780953 Hartrees, respectively. The vibrational and electronic investigations of the stable s-cis and s-trans conformers of trans-2-methoxycinnamic acid have been carried out extensively with FTIR and FT-Raman spectral techniques. The s-cis conformer (I) with a (C16-C17-C18-O19) dihedral angle equal to 0° is found to be more favoured relative to the one s-trans (II) with (C16-C17-C18-O19) = 180°, possibly due to delocalization, hydrogen bonding and steric repulsion effects between the methoxy and acrylic acid groups. The DFT studies are performed with B3LYP method by utilizing 6-311++G** and cc-pVTZ basis sets to determine the structure, thermodynamic properties, vibrational characteristics and chemical shifts of the compound. The total dipole moments of the conformers determined by B3LYP/cc-pVTZ method are 3.35 D and 4.87 D for s-cis and s-trans, respectively. It reveals the higher polarity of s-trans conformer of trans-2MCA molecule. The electronic and steric influence of the methoxy group on the skeletal frequencies has been analysed. The energies of the frontier molecular orbitals and the LUMO-HOMO energy gap have been determined. The MEP of s-cis conformer lie in the range +1.374e × 10-2 to -1.374e × 10-2 while for s-trans it is +1.591e × 10-2 to -1.591e × 10-2. The total electron density of s-cis conformer lie in the range +5.273e × 10-2 to -5.273e × 10-2 while for s-trans it is +5.403e × 10-2 to -5.403e × 10-2. The MEP and total electron density shows that the s-cis conformer is less polar, less reactive and more stable than the s-trans conformer. All the reactivity descriptors of the molecule have been discussed. Intramolecular electronic interactions and their stabilisation energies have analysed by NBO method.

Polynomial-time quantum algorithm for the simulation of chemical dynamics

PubMed Central

Kassal, Ivan; Jordan, Stephen P.; Love, Peter J.; Mohseni, Masoud; Aspuru-Guzik, Alán

2008-01-01

The computational cost of exact methods for quantum simulation using classical computers grows exponentially with system size. As a consequence, these techniques can be applied only to small systems. By contrast, we demonstrate that quantum computers could exactly simulate chemical reactions in polynomial time. Our algorithm uses the split-operator approach and explicitly simulates all electron-nuclear and interelectronic interactions in quadratic time. Surprisingly, this treatment is not only more accurate than the Born–Oppenheimer approximation but faster and more efficient as well, for all reactions with more than about four atoms. This is the case even though the entire electronic wave function is propagated on a grid with appropriately short time steps. Although the preparation and measurement of arbitrary states on a quantum computer is inefficient, here we demonstrate how to prepare states of chemical interest efficiently. We also show how to efficiently obtain chemically relevant observables, such as state-to-state transition probabilities and thermal reaction rates. Quantum computers using these techniques could outperform current classical computers with 100 qubits. PMID:19033207

Reassigning the Structures of Natural Products Using NMR Chemical Shifts Computed with Quantum Mechanics: A Laboratory Exercise

ERIC Educational Resources Information Center

Palazzo, Teresa A.; Truong, Tiana T.; Wong, Shirley M. T.; Mack, Emma T.; Lodewyk, Michael W.; Harrison, Jason G.; Gamage, R. Alan; Siegel, Justin B.; Kurth, Mark J.; Tantillo, Dean J.

2015-01-01

An applied computational chemistry laboratory exercise is described in which students use modern quantum chemical calculations of chemical shifts to assign the structure of a recently isolated natural product. A pre/post assessment was used to measure student learning gains and verify that students demonstrated proficiency of key learning…

BioSig-Air-Force

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

2011-07-15

1) Configured servers: In coordination with the INSIGHT team, a hardware configuration was selected. Two nodes were purchased, configured, and shipped with compatible OS and database installation. The servers have been stress tested for reliability as they use leading edge technologies. Each node has two CPUs and 12 cores per CPU with maximum onboard memory for high performance. 2) LIM and Experimental module: The original BioSig system was developed for cancer research. Accordingly, the LIM system its corresponding web pages are being modified to facilitate (i) pathogene-donor interactions, (ii) media composition, (iii) chemical and siRNA plate configurations. The LIM systemmore » has been redesigned. The revised system allows design of new media and tracking it from lot-to-lot so that variations in the phenotypic responses can be tracked to a specific media and lot number. Similar associations are also possible with other experimental factors (e.g., donor-pathoge, siRNA, and chemical). Furthermore, the design of the experimental variables has also been revised to (i) interact with the newly developed LIM system, (ii) simplify experimental specifications, and (iii) test for potential operator's error during the data entry. Part of the complication has been due to the handshake between multiple teams that provide the small molecule plates and the team that creates assay plates. Our efforts have focused to harmonize these interactions (e.g., various data formats) so that each assay plate can be mapped to its source so that a correct set of experimental variables can be associated with each image. For example, depending upon the source of the chemical plates, they may have different formats. We have developed a canonical representation that registers SMILES code, for each chemical compound, along with its physiochemical properties. The schema for LIM conjunction with customized Web pages. 3) Import of Images and computed descriptors module: In coordination with the INSIGHT team, policies were designed to route images and computed representation into BioSig. This module (i) examines for completion of image analysis, and imports images, computed masks, and descriptors into BioSig. A database API for efficient retrieval of selection of descriptors (among thousands) was designed and implemented. 4) Computed segmentation masks from external software were imported, boundaries computed, and overlaid on images for quality control.« less

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.

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.

On Macroscopic Quantum Phenomena in Biomolecules and Cells: From Levinthal to Hopfield

PubMed Central

Raković, Dejan; Dugić, Miroljub; Jeknić-Dugić, Jasmina; Plavšić, Milenko; Jaćimovski, Stevo; Šetrajčić, Jovan

2014-01-01

In the context of the macroscopic quantum phenomena of the second kind, we hereby seek for a solution-in-principle of the long standing problem of the polymer folding, which was considered by Levinthal as (semi)classically intractable. To illuminate it, we applied quantum-chemical and quantum decoherence approaches to conformational transitions. Our analyses imply the existence of novel macroscopic quantum biomolecular phenomena, with biomolecular chain folding in an open environment considered as a subtle interplay between energy and conformation eigenstates of this biomolecule, governed by quantum-chemical and quantum decoherence laws. On the other hand, within an open biological cell, a system of all identical (noninteracting and dynamically noncoupled) biomolecular proteins might be considered as corresponding spatial quantum ensemble of these identical biomolecular processors, providing spatially distributed quantum solution to a single corresponding biomolecular chain folding, whose density of conformational states might be represented as Hopfield-like quantum-holographic associative neural network too (providing an equivalent global quantum-informational alternative to standard molecular-biology local biochemical approach in biomolecules and cells and higher hierarchical levels of organism, as well). PMID:25028662

Anomaly Detection Based on Local Nearest Neighbor Distance Descriptor in Crowded Scenes

PubMed Central

Hu, Shiqiang; Zhang, Huanlong; Luo, Lingkun

2014-01-01

We propose a novel local nearest neighbor distance (LNND) descriptor for anomaly detection in crowded scenes. Comparing with the commonly used low-level feature descriptors in previous works, LNND descriptor has two major advantages. First, LNND descriptor efficiently incorporates spatial and temporal contextual information around the video event that is important for detecting anomalous interaction among multiple events, while most existing feature descriptors only contain the information of single event. Second, LNND descriptor is a compact representation and its dimensionality is typically much lower than the low-level feature descriptor. Therefore, not only the computation time and storage requirement can be accordingly saved by using LNND descriptor for the anomaly detection method with offline training fashion, but also the negative aspects caused by using high-dimensional feature descriptor can be avoided. We validate the effectiveness of LNND descriptor by conducting extensive experiments on different benchmark datasets. Experimental results show the promising performance of LNND-based method against the state-of-the-art methods. It is worthwhile to notice that the LNND-based approach requires less intermediate processing steps without any subsequent processing such as smoothing but achieves comparable event better performance. PMID:25105164

Pacific Northwest Laboratory Annual Report for 1992 to the DOE Office of Energy Research

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

Kreml, S.A.; Park, J.F.

1993-06-01

This report summarizes progress in OHER biological research and general life sciences research programs conducted at PNL in FY 1992. The research develops the knowledge and fundamental principles necessary to identify, understand, and anticipate the long-term health consequences of energy-related radiation and chemicals. Our continuing emphasis is to decrease the uncertainty of health risk estimates from energy-related technologies through an increase understanding of the ways in which radiation and chemicals cause biological damage. Descriptors of individual research projects as detailed in this report one separately abstracted and indexed for the database.

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.

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.

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.

Categorical dimensions of human odor descriptor space revealed by non-negative matrix factorization

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

Chennubhotla, Chakra; Castro, Jason

2013-01-01

In contrast to most other sensory modalities, the basic perceptual dimensions of olfaction remain un- clear. Here, we use non-negative matrix factorization (NMF) - a dimensionality reduction technique - to uncover structure in a panel of odor profiles, with each odor defined as a point in multi-dimensional descriptor space. The properties of NMF are favorable for the analysis of such lexical and perceptual data, and lead to a high-dimensional account of odor space. We further provide evidence that odor di- mensions apply categorically. That is, odor space is not occupied homogenously, but rather in a discrete and intrinsically clustered manner.more » We discuss the potential implications of these results for the neural coding of odors, as well as for developing classifiers on larger datasets that may be useful for predicting perceptual qualities from chemical structures.« less

Host-guest complex of N-(2-chloroethyl), N-nitroso, N‧, N‧ -dicyclohexylsulfamid with β-cyclodextrin: Fluorescence, QTAIM analysis and structure-chemical reactivity

NASA Astrophysics Data System (ADS)

Bensouilah, Nadjia; Fisli, Hassina; Bensouilah, Hamza; Zaater, Sihem; Abdaoui, Mohamed; Boutemeur-Kheddis, Baya

2017-10-01

In this work, the inclusion complex of DCY/CENS: N-(2-chloroethyl), N-nitroso, N‧, N‧-dicyclohexylsulfamid and β-cyclodextrin (β-CD) is investigated using the fluorescence spectroscopy, PM3, ONIOM2 and DFT methods. The experimental part reveals that DCY/CENS forms a 1:1 stoichiometric ratio inclusion complex with β-CD. The constant of stability is evaluated using the Benesi-Hildebrand equation. The results of the theoretical optimization showed that the lipophilic fraction of molecule (cyclohexyl group) is inside of β-CD. Accordingly, the Nitroso-Chloroethyl moiety is situated outside the cavity of the macromolecule host. The favorable structure of the optimized complex indicates the existence of weak intermolecular hydrogen bonds and the most important van der Waals (vdW) interactions which are studied on the basis of Natural Bonding Orbital (NBO) analysis. The NBO is employed to compute the electronic donor-acceptor exchanges between drug and β-CD. Furthermore, a detailed topological charge density analysis based on the quantum theory of atoms in molecules (QTAIM), has been accomplished on the most favorable complex using B3LYP/6-31G(d) method. The presence of stabilizing intermolecular hydrogen bonds and van der Waals interactions in the most favorable complex is predicted. Also, the energies of these interactions are estimated with Espinosa's formula. The findings of this investigation reveal that the correlation between the structural parameters and the electronic density is good. Finally, and based on DFT calculations, the reactivity of the interesting molecule in free state was studied and compared with that in the complexed state using chemical potential, global hardness, global softness, electronegativity, electrophilicity and local reactivity descriptors.

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.

Path Integrals for Electronic Densities, Reactivity Indices, and Localization Functions in Quantum Systems

PubMed Central

Putz, Mihai V.

2009-01-01

The density matrix theory, the ancestor of density functional theory, provides the immediate framework for Path Integral (PI) development, allowing the canonical density be extended for the many-electronic systems through the density functional closure relationship. Yet, the use of path integral formalism for electronic density prescription presents several advantages: assures the inner quantum mechanical description of the system by parameterized paths; averages the quantum fluctuations; behaves as the propagator for time-space evolution of quantum information; resembles Schrödinger equation; allows quantum statistical description of the system through partition function computing. In this framework, four levels of path integral formalism were presented: the Feynman quantum mechanical, the semiclassical, the Feynman-Kleinert effective classical, and the Fokker-Planck non-equilibrium ones. In each case the density matrix or/and the canonical density were rigorously defined and presented. The practical specializations for quantum free and harmonic motions, for statistical high and low temperature limits, the smearing justification for the Bohr’s quantum stability postulate with the paradigmatic Hydrogen atomic excursion, along the quantum chemical calculation of semiclassical electronegativity and hardness, of chemical action and Mulliken electronegativity, as well as by the Markovian generalizations of Becke-Edgecombe electronic focalization functions – all advocate for the reliability of assuming PI formalism of quantum mechanics as a versatile one, suited for analytically and/or computationally modeling of a variety of fundamental physical and chemical reactivity concepts characterizing the (density driving) many-electronic systems. PMID:20087467

Path integrals for electronic densities, reactivity indices, and localization functions in quantum systems.

PubMed

Putz, Mihai V

2009-11-10

The density matrix theory, the ancestor of density functional theory, provides the immediate framework for Path Integral (PI) development, allowing the canonical density be extended for the many-electronic systems through the density functional closure relationship. Yet, the use of path integral formalism for electronic density prescription presents several advantages: assures the inner quantum mechanical description of the system by parameterized paths; averages the quantum fluctuations; behaves as the propagator for time-space evolution of quantum information; resembles Schrödinger equation; allows quantum statistical description of the system through partition function computing. In this framework, four levels of path integral formalism were presented: the Feynman quantum mechanical, the semiclassical, the Feynman-Kleinert effective classical, and the Fokker-Planck non-equilibrium ones. In each case the density matrix or/and the canonical density were rigorously defined and presented. The practical specializations for quantum free and harmonic motions, for statistical high and low temperature limits, the smearing justification for the Bohr's quantum stability postulate with the paradigmatic Hydrogen atomic excursion, along the quantum chemical calculation of semiclassical electronegativity and hardness, of chemical action and Mulliken electronegativity, as well as by the Markovian generalizations of Becke-Edgecombe electronic focalization functions - all advocate for the reliability of assuming PI formalism of quantum mechanics as a versatile one, suited for analytically and/or computationally modeling of a variety of fundamental physical and chemical reactivity concepts characterizing the (density driving) many-electronic systems.

[The use of Cantonese pain descriptors among healthy young adults in Hong Kong].

PubMed

Chung, W Y; Wong, C H; Yang, J C; Tan, P P

1998-12-01

The interpretation and expression of pain are closely related to an individual's social and cultural background. To convey messages on pain, language and words (pain descriptors) is particularly significant in assessment and evaluation of pain severity and its management. Therefore, the study of pain descriptors is crucial in clinical practice. It was of exploratory-descriptive design. Samples were recruited by convenience. Data were collected by structured self-administered questionnaire. Data obtained included demographic information and pain descriptors used by the subjects in various pain conditions. Data were analyzed by descriptive statistics. Pain descriptors were categorized according to nature, process, intensity, aggravating factors, accompanying symptoms and behavioral manifestation. Total number of pain descriptors (in Cantonese) based on real pain experience was 3017, mean was 3 (n = 986). The commonest used descriptors was the nature of pain (41%). The intensity of pain constituted 20%. There was no significant difference in the number of pain descriptors between male and female. However, there was a significant difference between the type of pain descriptors used (Mfemale = 526, Mmale = 453, Z = -2.9729, p = 0.0029). There were also significant differences in the use of pain descriptors among the various age groups (X2 = 15.0157, df = 4, P = 0.0047) and educational levels (X2 = 11.2443, df = 4, P = 0.0240). The types of descriptors used increased with an increase in age and education levels. This exploratory-descriptive study explores the use of pain descriptors among Chinese young adults in Hong Kong. The result shows that female use more pain descriptors than male. The pain descriptors that female used are mostly of nature type. The similarities and differences in findings with those of the Ho's (1991) are compared.

Towards quantum chemistry on a quantum computer.

PubMed

Lanyon, B P; Whitfield, J D; Gillett, G G; Goggin, M E; Almeida, M P; Kassal, I; Biamonte, J D; Mohseni, M; Powell, B J; Barbieri, M; Aspuru-Guzik, A; White, A G

2010-02-01

Exact first-principles calculations of molecular properties are currently intractable because their computational cost grows exponentially with both the number of atoms and basis set size. A solution is to move to a radically different model of computing by building a quantum computer, which is a device that uses quantum systems themselves to store and process data. Here we report the application of the latest photonic quantum computer technology to calculate properties of the smallest molecular system: the hydrogen molecule in a minimal basis. We calculate the complete energy spectrum to 20 bits of precision and discuss how the technique can be expanded to solve large-scale chemical problems that lie beyond the reach of modern supercomputers. These results represent an early practical step toward a powerful tool with a broad range of quantum-chemical applications.

Descriptor selection for banana accessions based on univariate and multivariate analysis.

PubMed

Brandão, L P; Souza, C P F; Pereira, V M; Silva, S O; Santos-Serejo, J A; Ledo, C A S; Amorim, E P

2013-05-14

Our objective was to establish a minimum number of morphological descriptors for the characterization of banana germplasm and evaluate the efficiency of removal of redundant characters, based on univariate and multivariate statistical analyses. Phenotypic characterization was made of 77 accessions from Bahia, Brazil, using 92 descriptors. The selection of the descriptors was carried out by principal components analysis (quantitative) and by entropy (multi-category). Efficiency of elimination was analyzed by a comparative study between the clusters formed, taking into consideration all 92 descriptors and smaller groups. The selected descriptors were analyzed with the Ward-MLM procedure and a combined matrix formed by the Gower algorithm. We were able to reduce the number of descriptors used for characterizing the banana germplasm (42%). The correlation between the matrices considering the 92 descriptors and the selected ones was 0.82, showing that the reduction in the number of descriptors did not influence estimation of genetic variability between the banana accessions. We conclude that removing these descriptors caused no loss of information, considering the groups formed from pre-established criteria, including subgroup/subspecies.

Genotoxicity Assessment of Drinking Water Disinfection Byproducts by DNA Damage and Repair Pathway Profiling Analysis.

PubMed

Lan, Jiaqi; Rahman, Sheikh Mokhlesur; Gou, Na; Jiang, Tao; Plewa, Micheal J; Alshawabkeh, Akram; Gu, April Z

2018-06-05

Genotoxicity is considered a major concern for drinking water disinfection byproducts (DBPs). Of over 700 DBPs identified to date, only a small number has been assessed with limited information for DBP genotoxicity mechanism(s). In this study, we evaluated genotoxicity of 20 regulated and unregulated DBPs applying a quantitative toxicogenomics approach. We used GFP-fused yeast strains that examine protein expression profiling of 38 proteins indicative of all known DNA damage and repair pathways. The toxicogenomics assay detected genotoxicity potential of these DBPs that is consistent with conventional genotoxicity assays end points. Furthermore, the high-resolution, real-time pathway activation and protein expression profiling, in combination with clustering analysis, revealed molecular level details in the genotoxicity mechanisms among different DBPs and enabled classification of DBPs based on their distinct DNA damage effects and repair mechanisms. Oxidative DNA damage and base alkylation were confirmed to be the main molecular mechanisms of DBP genotoxicity. Initial exploration of QSAR modeling using moleular genotoxicity end points (PELI) suggested that genotoxicity of DBPs in this study was correlated with topological and quantum chemical descriptors. This study presents a toxicogenomics-based assay for fast and efficient mechanistic genotoxicity screening and assessment of a large number of DBPs. The results help to fill in the knowledge gap in the understanding of the molecular mechanisms of DBP genotoxicity.

Experimentally determined soil organic matter-water sorption coefficients for different classes of natural toxins and comparison with estimated numbers.

PubMed

Schenzel, Judith; Goss, Kai-Uwe; Schwarzenbach, René P; Bucheli, Thomas D; Droge, Steven T J

2012-06-05

Although natural toxins, such as mycotoxins or phytoestrogens are widely studied and were recently identified as micropollutants in the environment, many of their environmentally relevant physicochemical properties have not yet been determined. Here, the sorption affinity to Pahokee peat, a model sorbent for soil organic matter, was investigated for 29 mycotoxins and two phytoestrogens. Sorption coefficients (K(oc)) were determined with a dynamic HPLC-based column method using a fully aqueous mobile phase with 5 mM CaCl(2) at pH 4.5. Sorption coefficients varied from less than 10(0.7) L/kg(oc) (e.g., all type B trichothecenes) to 10(4.0) L/kg(oc) (positively charged ergot alkaloids). For the neutral compounds the experimental sorption data set was compared with predicted sorption coefficients using various models, based on molecular fragment approaches (EPISuite's KOCWIN or SPARC), poly parameter linear free energy relationship (pp-LFER) in combination with predicted descriptors, and quantum-chemical based software (COSMOtherm)). None of the available models was able to adequately predict absolute K(oc) numbers and relative differences in sorption affinity for the whole set of neutral toxins, largely because mycotoxins exhibit highly complex structures. Hence, at present, for such compounds fast and consistent experimental techniques for determining sorption coefficients, as the one used in this study, are required.

Theoretical calculations of physico-chemical and spectroscopic properties of bioinorganic systems: current limits and perspectives.

PubMed

Rokob, Tibor András; Srnec, Martin; Rulíšek, Lubomír

2012-05-21

In the last decade, we have witnessed substantial progress in the development of quantum chemical methodologies. Simultaneously, robust solvation models and various combined quantum and molecular mechanical (QM/MM) approaches have become an integral part of quantum chemical programs. Along with the steady growth of computer power and, more importantly, the dramatic increase of the computer performance to price ratio, this has led to a situation where computational chemistry, when exercised with the proper amount of diligence and expertise, reproduces, predicts, and complements the experimental data. In this perspective, we review some of the latest achievements in the field of theoretical (quantum) bioinorganic chemistry, concentrating mostly on accurate calculations of the spectroscopic and physico-chemical properties of open-shell bioinorganic systems by wave-function (ab initio) and DFT methods. In our opinion, the one-to-one mapping between the calculated properties and individual molecular structures represents a major advantage of quantum chemical modelling since this type of information is very difficult to obtain experimentally. Once (and only once) the physico-chemical, thermodynamic and spectroscopic properties of complex bioinorganic systems are quantitatively reproduced by theoretical calculations may we consider the outcome of theoretical modelling, such as reaction profiles and the various decompositions of the calculated parameters into individual spatial or physical contributions, to be reliable. In an ideal situation, agreement between theory and experiment may imply that the practical problem at hand, such as the reaction mechanism of the studied metalloprotein, can be considered as essentially solved.

Quantum chemical parameters in QSAR: what do I use when?

USGS Publications Warehouse

Hickey, James P.; Ostrander, Gary K.

1996-01-01

This chapter provides a brief overview of the numerous quantum chemical parameters that have been/are currently being used in quantitative structure activity relationships (QSAR), along with a representative bibliography. The parameters will be grouped according to their mechanistic interpretations, and representative biological and physical chemical applications will be mentioned. Parmater computation methods and the appropriate software are highlighted, as are sources for software.

Autocorrelation descriptor improvements for QSAR: 2DA_Sign and 3DA_Sign

NASA Astrophysics Data System (ADS)

Sliwoski, Gregory; Mendenhall, Jeffrey; Meiler, Jens

2016-03-01

Quantitative structure-activity relationship (QSAR) is a branch of computer aided drug discovery that relates chemical structures to biological activity. Two well established and related QSAR descriptors are two- and three-dimensional autocorrelation (2DA and 3DA). These descriptors encode the relative position of atoms or atom properties by calculating the separation between atom pairs in terms of number of bonds (2DA) or Euclidean distance (3DA). The sums of all values computed for a given small molecule are collected in a histogram. Atom properties can be added with a coefficient that is the product of atom properties for each pair. This procedure can lead to information loss when signed atom properties are considered such as partial charge. For example, the product of two positive charges is indistinguishable from the product of two equivalent negative charges. In this paper, we present variations of 2DA and 3DA called 2DA_Sign and 3DA_Sign that avoid information loss by splitting unique sign pairs into individual histograms. We evaluate these variations with models trained on nine datasets spanning a range of drug target classes. Both 2DA_Sign and 3DA_Sign significantly increase model performance across all datasets when compared with traditional 2DA and 3DA. Lastly, we find that limiting 3DA_Sign to maximum atom pair distances of 6 Å instead of 12 Å further increases model performance, suggesting that conformational flexibility may hinder performance with longer 3DA descriptors. Consistent with this finding, limiting the number of bonds in 2DA_Sign from 11 to 5 fails to improve performance.

Examining the predictive accuracy of the novel 3D N-linear algebraic molecular codifications on benchmark datasets.

PubMed

García-Jacas, César R; Contreras-Torres, Ernesto; Marrero-Ponce, Yovani; Pupo-Meriño, Mario; Barigye, Stephen J; Cabrera-Leyva, Lisset

2016-01-01

Recently, novel 3D alignment-free molecular descriptors (also known as QuBiLS-MIDAS) based on two-linear, three-linear and four-linear algebraic forms have been introduced. These descriptors codify chemical information for relations between two, three and four atoms by using several (dis-)similarity metrics and multi-metrics. Several studies aimed at assessing the quality of these novel descriptors have been performed. However, a deeper analysis of their performance is necessary. Therefore, in the present manuscript an assessment and statistical validation of the performance of these novel descriptors in QSAR studies is performed. To this end, eight molecular datasets (angiotensin converting enzyme, acetylcholinesterase inhibitors, benzodiazepine receptor, cyclooxygenase-2 inhibitors, dihydrofolate reductase inhibitors, glycogen phosphorylase b, thermolysin inhibitors, thrombin inhibitors) widely used as benchmarks in the evaluation of several procedures are utilized. Three to nine variable QSAR models based on Multiple Linear Regression are built for each chemical dataset according to the original division into training/test sets. Comparisons with respect to leave-one-out cross-validation correlation coefficients[Formula: see text] reveal that the models based on QuBiLS-MIDAS indices possess superior predictive ability in 7 of the 8 datasets analyzed, outperforming methodologies based on similar or more complex techniques such as: Partial Least Square, Neural Networks, Support Vector Machine and others. On the other hand, superior external correlation coefficients[Formula: see text] are attained in 6 of the 8 test sets considered, confirming the good predictive power of the obtained models. For the [Formula: see text] values non-parametric statistic tests were performed, which demonstrated that the models based on QuBiLS-MIDAS indices have the best global performance and yield significantly better predictions in 11 of the 12 QSAR procedures used in the comparison. Lastly, a study concerning to the performance of the indices according to several conformer generation methods was performed. This demonstrated that the quality of predictions of the QSAR models based on QuBiLS-MIDAS indices depend on 3D structure generation method considered, although in this preliminary study the results achieved do not present significant statistical differences among them. As conclusions it can be stated that the QuBiLS-MIDAS indices are suitable for extracting structural information of the molecules and thus, constitute a promissory alternative to build models that contribute to the prediction of pharmacokinetic, pharmacodynamics and toxicological properties on novel compounds.Graphical abstractComparative graphical representation of the performance of the novel QuBiLS-MIDAS 3D-MDs with respect to other methodologies in QSAR modeling of eight chemical datasets.

A Global Covariance Descriptor for Nuclear Atypia Scoring in Breast Histopathology Images.

PubMed

Khan, Adnan Mujahid; Sirinukunwattana, Korsuk; Rajpoot, Nasir

2015-09-01

Nuclear atypia scoring is a diagnostic measure commonly used to assess tumor grade of various cancers, including breast cancer. It provides a quantitative measure of deviation in visual appearance of cell nuclei from those in normal epithelial cells. In this paper, we present a novel image-level descriptor for nuclear atypia scoring in breast cancer histopathology images. The method is based on the region covariance descriptor that has recently become a popular method in various computer vision applications. The descriptor in its original form is not suitable for classification of histopathology images as cancerous histopathology images tend to possess diversely heterogeneous regions in a single field of view. Our proposed image-level descriptor, which we term as the geodesic mean of region covariance descriptors, possesses all the attractive properties of covariance descriptors lending itself to tractable geodesic-distance-based k-nearest neighbor classification using efficient kernels. The experimental results suggest that the proposed image descriptor yields high classification accuracy compared to a variety of widely used image-level descriptors.

On the quantum mechanics of consciousness, with application to anomalous phenomena

NASA Astrophysics Data System (ADS)

Jahn, Robert G.; Dunne, Brenda J.

1986-08-01

Theoretical explication of a growing body of empirical data on consciousness-related anomalous phenomena is unlikely to be achieved in terms of known physical processes. Rather, it will first be necessary to formulate the basic role of consciousness in the definition of reality before such anomalous experience can adequately be represented. This paper takes the position that reality is constituted only in the interaction of consciousness with its environment, and therefore that any scheme of conceptual organization developed to represent that reality must reflect the processes of consciousness as well as those of its environment. In this spirit, the concepts and formalisms of elementary quantum mechanics, as originally proposed to explain anomalous atomic-scale physical phenomena, are appropriated via metaphor to represent the general characteristics of consciousness interacting with any environment. More specifically, if consciousness is represented by a quantum mechanical wave function, and its environment by an appropriate potential profile, Schrödinger wave mechanics defines eigenfunctions and eigenvalues that can be associated with the cognitive and emotional experiences of that consciousness in that environment. To articulate this metaphor it is necessary to associate certain aspects of the formalism, such as the coordinate system, the quantum numbers, and even the metric itself, with various impressionistic descriptors of consciousness, such as its intensity, perspective, approach/avoidance attitude, balance between cognitive and emotional activity, and receptive/assertive disposition. With these established, a number of the generic features of quantum mechanics, such as the wave/particle duality, and the uncertainty, indistinguishability, and exclusion principles, display metaphoric relevance to familiar individual and collective experiences. Similarly, such traditional quantum theoretic exercises as the central force field and atomic structure, covalent molecular bonds, barrier penetration, and quantum statistical collective behavior become useful analogies for representation of a variety of consciousness experiences, both normal and anomalous, and for the design of experiments to study these systematically.

Effect of Coulomb interaction on chemical potential of metal film

NASA Astrophysics Data System (ADS)

Kostrobij, P. P.; Markovych, B. M.

2018-07-01

The chemical potential of a metal film within the jellium model taking into account the Coulomb interaction between electrons is calculated. The surface potential is modelled as the infinite rectangular potential well. The behaviour of the chemical potential as a function of the film thickness is studied, the quantum size effect for this quantity is analysed. It is shown that taking into account the Coulomb interaction leads to a significant decrease of the chemical potential and to an enhancement of the quantum size effect.

Surface Traps in Colloidal Quantum Dots: A Combined Experimental and Theoretical Perspective.

PubMed

Giansante, Carlo; Infante, Ivan

2017-10-19

Surface traps are ubiquitous to nanoscopic semiconductor materials. Understanding their atomistic origin and manipulating them chemically have capital importance to design defect-free colloidal quantum dots and make a leap forward in the development of efficient optoelectronic devices. Recent advances in computing power established computational chemistry as a powerful tool to describe accurately complex chemical species and nowadays it became conceivable to model colloidal quantum dots with realistic sizes and shapes. In this Perspective, we combine the knowledge gathered in recent experimental findings with the computation of quantum dot electronic structures. We analyze three different systems: namely, CdSe, PbS, and CsPbI 3 as benchmark semiconductor nanocrystals showing how different types of trap states can form at their surface. In addition, we suggest experimental healing of such traps according to their chemical origin and nanocrystal composition.

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.

Intrinsic Atomic Orbitals: An Unbiased Bridge between Quantum Theory and Chemical Concepts.

PubMed

Knizia, Gerald

2013-11-12

Modern quantum chemistry can make quantitative predictions on an immense array of chemical systems. However, the interpretation of those predictions is often complicated by the complex wave function expansions used. Here we show that an exceptionally simple algebraic construction allows for defining atomic core and valence orbitals, polarized by the molecular environment, which can exactly represent self-consistent field wave functions. This construction provides an unbiased and direct connection between quantum chemistry and empirical chemical concepts, and can be used, for example, to calculate the nature of bonding in molecules, in chemical terms, from first principles. In particular, we find consistency with electronegativities (χ), C 1s core-level shifts, resonance substituent parameters (σR), Lewis structures, and oxidation states of transition-metal complexes.

Model-Free Stochastic Localization of CBRN Releases

DTIC Science & Technology

2013-01-01

Ioannis Ch. Paschalidis,‡ Senior Member, IEEE Abstract—We present a novel two-stage methodology for locating a Chemical, Biological, Radiological, or...Nuclear (CBRN) source in an urban area using a network of sensors. In contrast to earlier work, our approach does not solve an inverse dispersion problem...but relies on data obtained from a simulation of the CBRN dispersion to obtain probabilistic descriptors of sensor measurements under a variety of CBRN

Risk assessment of hazardous release in air due to the chemical production of "P. Karaminchev" company in the town of Rousse.

PubMed

Diankova, M

1998-09-01

A health risk evaluation of the lifetime population risk has been made, by using the US EPA's method of risk assessment. Several main steps have been conducted: --a hazard identification, by means of emission analysis and mathematical modeling of air concentration dispersion; a dose-response evaluation and exposure assessment, and finally--a risk characterization. The health risk evaluation was conducted, using lifetime reference concentrations and doses. As risk descriptors were applied: --the individual exposure coefficient (IEC), the hazard quotient (HQ) and the margin of exposure (MOE)--for system toxicants, and the excess lifetime cancer risk (ELCR)--for carcinogens. The method that was used provides an upperbound estimate, including all possible exposures. The results showed, that the emissions of hydrogen chloride, phthalates (DOF), nitrogen oxides and most of the organic solvents, released from this chemical plant, are not a source of lifetime chronic health risk for the population of any of the six evaluated residential areas of Rousse. The rest of the hazardous emissions cause a slightly increased lifetime health risk, which is entirely in the so called 'controlled risk zone' the risk descriptors vary from 1.00 to 5.00. A number of actions have been prescribed to the plant's government, most of which were realized in the short term.

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.

Genome scale enzyme–metabolite and drug–target interaction predictions using the signature molecular descriptor

DOE PAGES

Faulon, Jean-Loup; Misra, Milind; Martin, Shawn; ...

2007-11-23

Motivation: Identifying protein enzymatic or pharmacological activities are important areas of research in biology and chemistry. Biological and chemical databases are increasingly being populated with linkages between protein sequences and chemical structures. Additionally, there is now sufficient information to apply machine-learning techniques to predict interactions between chemicals and proteins at a genome scale. Current machine-learning techniques use as input either protein sequences and structures or chemical information. We propose here a method to infer protein–chemical interactions using heterogeneous input consisting of both protein sequence and chemical information. Results: Our method relies on expressing proteins and chemicals with a common cheminformaticsmore » representation. We demonstrate our approach by predicting whether proteins can catalyze reactions not present in training sets. We also predict whether a given drug can bind a target, in the absence of prior binding information for that drug and target. Lastly, such predictions cannot be made with current machine-learning techniques requiring binding information for individual reactions or individual targets.« less

Self-pacing direct memory access data transfer operations for compute nodes in a parallel computer

DOEpatents

Blocksome, Michael A

2015-02-17

Methods, apparatus, and products are disclosed for self-pacing DMA data transfer operations for nodes in a parallel computer that include: transferring, by an origin DMA on an origin node, a RTS message to a target node, the RTS message specifying an message on the origin node for transfer to the target node; receiving, in an origin injection FIFO for the origin DMA from a target DMA on the target node in response to transferring the RTS message, a target RGET descriptor followed by a DMA transfer operation descriptor, the DMA descriptor for transmitting a message portion to the target node, the target RGET descriptor specifying an origin RGET descriptor on the origin node that specifies an additional DMA descriptor for transmitting an additional message portion to the target node; processing, by the origin DMA, the target RGET descriptor; and processing, by the origin DMA, the DMA transfer operation descriptor.

Selection of morphoagronomic descriptors for the characterization of accessions of cassava of the Eastern Brazilian Amazon.

PubMed

Silva, R S; Moura, E F; Farias-Neto, J T; Ledo, C A S; Sampaio, J E

2017-04-13

The aim of this study was to select morphoagronomic descriptors to characterize cassava accessions representative of Eastern Brazilian Amazonia. It was characterized 262 accessions using 21 qualitative descriptors. The multiple-correspondence analysis (MCA) technique was applied using the criteria: contribution of the descriptor in the last factorial axis of analysis in successive cycles (SMCA); reverse order of the descriptor's contribution in the last factorial axis of analysis with all descriptors ('O'´p') of Jolliffe's method; mean of the contribution orders of the descriptor in the first three factorial axes in the analysis with all descriptors ('Os') together with ('O'´p'); and order of contribution of weighted mean in the first three factorial axes in the analysis of all descriptors ('Oz'). The dissimilarity coefficient was measured by the method of multicategorical variables. The correlation among the matrix generated with all descriptors and matrices based on each criteria varied (r = 0.21, r = 0.97, r = 0.98, r = 0.13 for SMCA, 'Os', 'Oz' and 'O'´p', respectively). The least informative descriptors were discarded independently and according to both 'Os' and 'Oz' criteria. Thirteen descriptors were capable to discriminate the accessions and to represent the morphological variability of accessions sampled in Brazilian Eastern Amazonia: color of apical leaves, petiole color, color of stem exterior, external color of storage root, color of stem cortex, color of root pulp, texture of root epidermis, color of leaf vein, color of stem epidermis, color of end branches of adult plant, branching habit, root shape, and constriction of root.

Intermolecular CH···O/N H-bonds in the biologically important pairs of natural nucleobases: a thorough quantum-chemical study.

PubMed

Brovarets', Ol'ha O; Yurenko, Yevgen P; Hovorun, Dmytro M

2014-01-01

This study aims to cast light on the physico-chemical nature and energetic of the non-conventional CH···O/N H-bonds in the biologically important natural nucleobase pairs using a comprehensive quantum-chemical approach. As a whole, the 36 biologically important pairs, involving canonical and rare tautomers of nucleobases, were studied by means of all available up-to-date state-of-the-art quantum-chemical techniques along with quantum theory "Atoms in molecules" (QTAIM), Natural Bond Orbital (NBO) analysis, Grunenberg's compliance constants theory, geometrical and vibrational analyses to identify the CH···O/N interactions, reveal their physico-chemical nature and estimate their strengths as well as contribution to the overall base-pairs stability. It was shown that all the 38 CH···O/N contacts (25 CH···O and 13 CH···N H-bonds) completely satisfy all classical geometrical, electron-topological, in particular Bader's and "two-molecule" Koch and Popelier's, and vibrational criteria of H-bonding. The positive values of Grunenberg's compliance constants prove that the CH···O/N contacts in nucleobase pairs are stabilizing interactions unlike electrostatic repulsion and anti-H-bonds. NBO analysis indicates the electron density transfer from the lone electron pair of the acceptor atom (O/N) to the antibonding orbital corresponding to the donor group σ(∗)(CH). Moreover, significant increase in the frequency of the out-of-plane deformation modes γ (CH) under the formation of the CH···O (by 17.2÷81.3/10.8÷84.7 cm(-1)) and CH···N (by 32.7÷85.9/9.0÷77.9 cm(-1)) H-bonds at the density functional theory (DFT)/second-order Møller-Plesset (MP2) levels of theory, respectively, and concomitant changes of their intensities can be considered as reliable indicators of H-bonding. The strengths of the CH···O/N interactions, evaluated by means of Espinosa-Molins-Lecomte formula, lie within the range 0.45÷3.89/0.62÷4.10 kcal/mol for the CH···O H-bonds and 1.45÷3.17/1.70÷3.43 kcal/mol for the CH···N H-bonds at the DFT/MP2 levels of theory, respectively. We revealed high linear mutual correlations between the H-bond energy and different physico-chemical parameters of the CH···O/N H-bonds. Based on these observations, the authors asserted that the most reliable descriptors of the H-bonding are the electron density ρ at the СН···О/N H-bond critical points and the NBO calculated stabilization energy E((2)). The linear dependence of the H-bond energy ECH···O/N (in kcal/mol) on the electron density ρ (in atomic units) was established (DFT/MP2): ECH···O = 248.501[Formula: see text]ρ-0.367/260.518[Formula: see text]ρ-0.373 and ECH···N = 218.125[Formula: see text]ρ-0.339/243.599[Formula: see text]ρ-0.441. Red-shifted and blue-shifted CH···O/N H-bonds behave in a similar way and can be described with the same fit parameters. It was found that the A-U HH2 and U-U3 nucleobase pairs are stabilized solely by the CH···O/N H-bonds. At the same time, in the A-U HH1, A-U HH2, A-Asyn 1, A-Asyn 2, A-Asyn 3, A-A4, A-G1, A-G2, G-U1, G-U2, G-U3, G-C HH1, U-U1, U-U2, U-U3 and A-C nucleobase pairs the CH···O/N H-bonds play a prominent role (>30%) in their stabilization. We suppose that unconventional CH···O/N H-bond plays the role of the third "fulcrum", ensuring structurally dynamic similarity of the isomorphic base pairs of different origin, which are incorporated equally well into the structure of the DNA double helix.

A comparison between space-time video descriptors

NASA Astrophysics Data System (ADS)

Costantini, Luca; Capodiferro, Licia; Neri, Alessandro

2013-02-01

The description of space-time patches is a fundamental task in many applications such as video retrieval or classification. Each space-time patch can be described by using a set of orthogonal functions that represent a subspace, for example a sphere or a cylinder, within the patch. In this work, our aim is to investigate the differences between the spherical descriptors and the cylindrical descriptors. In order to compute the descriptors, the 3D spherical and cylindrical Zernike polynomials are employed. This is important because both the functions are based on the same family of polynomials, and only the symmetry is different. Our experimental results show that the cylindrical descriptor outperforms the spherical descriptor. However, the performances of the two descriptors are similar.

Physicochemical descriptors of aromatic character and their use in drug discovery.

PubMed

Ritchie, Timothy J; Macdonald, Simon J F

2014-09-11

Published physicochemical descriptors of molecules that convey aromaticity-related character are reviewed in the context of drug design and discovery. Studies that have employed aromatic descriptors are discussed, and several descriptors are compared and contrasted.

Recent advances in quantum scattering calculations on polyatomic bimolecular reactions.

PubMed

Fu, Bina; Shan, Xiao; Zhang, Dong H; Clary, David C

2017-12-11

This review surveys quantum scattering calculations on chemical reactions of polyatomic molecules in the gas phase published in the last ten years. These calculations are useful because they provide highly accurate information on the dynamics of chemical reactions which can be compared in detail with experimental results. They also serve as quantum mechanical benchmarks for testing approximate theories which can more readily be applied to more complicated reactions. This review includes theories for calculating quantities such as rate constants which have many important scientific applications.

Tuning electronic properties in graphene quantum dots by chemical functionalization: Density functional theory calculations

NASA Astrophysics Data System (ADS)

Abdelsalam, Hazem; Elhaes, Hanan; Ibrahim, Medhat A.

2018-03-01

The energy gap and dipole moment of chemically functionalized graphene quantum dots are investigated by density functional theory. The energy gap can be tuned through edge passivation by different elements or groups. Edge passivation by oxygen considerably decreases the energy gap in hexagonal nanodots. Edge states in triangular quantum dots can also be manipulated by passivation with fluorine. The dipole moment depends on: (a) shape and edge termination of the quantum dot, (b) attached group, and (c) position to which the groups are attached. Depending on the position of attached groups, the total dipole can be increased, decreased, or eliminated.

Cation solvation with quantum chemical effects modeled by a size-consistent multi-partitioning quantum mechanics/molecular mechanics method.

PubMed

Watanabe, Hiroshi C; Kubillus, Maximilian; Kubař, Tomáš; Stach, Robert; Mizaikoff, Boris; Ishikita, Hiroshi

2017-07-21

In the condensed phase, quantum chemical properties such as many-body effects and intermolecular charge fluctuations are critical determinants of the solvation structure and dynamics. Thus, a quantum mechanical (QM) molecular description is required for both solute and solvent to incorporate these properties. However, it is challenging to conduct molecular dynamics (MD) simulations for condensed systems of sufficient scale when adapting QM potentials. To overcome this problem, we recently developed the size-consistent multi-partitioning (SCMP) quantum mechanics/molecular mechanics (QM/MM) method and realized stable and accurate MD simulations, using the QM potential to a benchmark system. In the present study, as the first application of the SCMP method, we have investigated the structures and dynamics of Na + , K + , and Ca 2+ solutions based on nanosecond-scale sampling, a sampling 100-times longer than that of conventional QM-based samplings. Furthermore, we have evaluated two dynamic properties, the diffusion coefficient and difference spectra, with high statistical certainty. Furthermore the calculation of these properties has not previously been possible within the conventional QM/MM framework. Based on our analysis, we have quantitatively evaluated the quantum chemical solvation effects, which show distinct differences between the cations.

Surface Traps in Colloidal Quantum Dots: A Combined Experimental and Theoretical Perspective

PubMed Central

2017-01-01

Surface traps are ubiquitous to nanoscopic semiconductor materials. Understanding their atomistic origin and manipulating them chemically have capital importance to design defect-free colloidal quantum dots and make a leap forward in the development of efficient optoelectronic devices. Recent advances in computing power established computational chemistry as a powerful tool to describe accurately complex chemical species and nowadays it became conceivable to model colloidal quantum dots with realistic sizes and shapes. In this Perspective, we combine the knowledge gathered in recent experimental findings with the computation of quantum dot electronic structures. We analyze three different systems: namely, CdSe, PbS, and CsPbI3 as benchmark semiconductor nanocrystals showing how different types of trap states can form at their surface. In addition, we suggest experimental healing of such traps according to their chemical origin and nanocrystal composition. PMID:28972763

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.

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.

The Harvard Clean Energy Project: High-throughput screening of organic photovoltaic materials using cheminformatics, machine learning, and pattern recognition

NASA Astrophysics Data System (ADS)

Olivares-Amaya, Roberto; Hachmann, Johannes; Amador-Bedolla, Carlos; Daly, Aidan; Jinich, Adrian; Atahan-Evrenk, Sule; Boixo, Sergio; Aspuru-Guzik, Alán

2012-02-01

Organic photovoltaic devices have emerged as competitors to silicon-based solar cells, currently reaching efficiencies of over 9% and offering desirable properties for manufacturing and installation. We study conjugated donor polymers for high-efficiency bulk-heterojunction photovoltaic devices with a molecular library motivated by experimental feasibility. We use quantum mechanics and a distributed computing approach to explore this vast molecular space. We will detail the screening approach starting from the generation of the molecular library, which can be easily extended to other kinds of molecular systems. We will describe the screening method for these materials which ranges from descriptor models, ubiquitous in the drug discovery community, to eventually reaching first principles quantum chemistry methods. We will present results on the statistical analysis, based principally on machine learning, specifically partial least squares and Gaussian processes. Alongside, clustering methods and the use of the hypergeometric distribution reveal moieties important for the donor materials and allow us to quantify structure-property relationships. These efforts enable us to accelerate materials discovery in organic photovoltaics through our collaboration with experimental groups.

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.

The chemical component dictionary: complete descriptions of constituent molecules in experimentally determined 3D macromolecules in the Protein Data Bank

PubMed Central

Westbrook, John D.; Shao, Chenghua; Feng, Zukang; Zhuravleva, Marina; Velankar, Sameer; Young, Jasmine

2015-01-01

Summary: The Chemical Component Dictionary (CCD) is a chemical reference data resource that describes all residue and small molecule components found in Protein Data Bank (PDB) entries. The CCD contains detailed chemical descriptions for standard and modified amino acids/nucleotides, small molecule ligands and solvent molecules. Each chemical definition includes descriptions of chemical properties such as stereochemical assignments, chemical descriptors, systematic chemical names and idealized coordinates. The content, preparation, validation and distribution of this CCD chemical reference dataset are described. Availability and implementation: The CCD is updated regularly in conjunction with the scheduled weekly release of new PDB structure data. The CCD and amino acid variant reference datasets are hosted in the public PDB ftp repository at ftp://ftp.wwpdb.org/pub/pdb/data/monomers/components.cif.gz, ftp://ftp.wwpdb.org/pub/pdb/data/monomers/aa-variants-v1.cif.gz, and its mirror sites, and can be accessed from http://wwpdb.org. Contact: jwest@rcsb.rutgers.edu. Supplementary information: Supplementary data are available at Bioinformatics online. PMID:25540181

Quantum-chemical calculations and electron diffraction study of the equilibrium molecular structure of vitamin K3

NASA Astrophysics Data System (ADS)

Khaikin, L. S.; Tikhonov, D. S.; Grikina, O. E.; Rykov, A. N.; Stepanov, N. F.

2014-05-01

The equilibrium molecular structure of 2-methyl-1,4-naphthoquinone (vitamin K3) having C s symmetry is experimentally characterized for the first time by means of gas-phase electron diffraction using quantum-chemical calculations and data on the vibrational spectra of related compounds.

Compactness Aromaticity of Atoms in Molecules

PubMed Central

Putz, Mihai V.

2010-01-01

A new aromaticity definition is advanced as the compactness formulation through the ratio between atoms-in-molecule and orbital molecular facets of the same chemical reactivity property around the pre- and post-bonding stabilization limit, respectively. Geometrical reactivity index of polarizability was assumed as providing the benchmark aromaticity scale, since due to its observable character; with this occasion new Hydrogenic polarizability quantum formula that recovers the exact value of 4.5 a03 for Hydrogen is provided, where a0 is the Bohr radius; a polarizability based–aromaticity scale enables the introduction of five referential aromatic rules (Aroma 1 to 5 Rules). With the help of these aromatic rules, the aromaticity scales based on energetic reactivity indices of electronegativity and chemical hardness were computed and analyzed within the major semi-empirical and ab initio quantum chemical methods. Results show that chemical hardness based-aromaticity is in better agreement with polarizability based-aromaticity than the electronegativity-based aromaticity scale, while the most favorable computational environment appears to be the quantum semi-empirical for the first and quantum ab initio for the last of them, respectively. PMID:20480020

Numeric promoter description - A comparative view on concepts and general application.

PubMed

Beier, Rico; Labudde, Dirk

2016-01-01

Nucleic acid molecules play a key role in a variety of biological processes. Starting from storage and transfer tasks, this also comprises the triggering of biological processes, regulatory effects and the active influence gained by target binding. Based on the experimental output (in this case promoter sequences), further in silico analyses aid in gaining new insights into these processes and interactions. The numerical description of nucleic acids thereby constitutes a bridge between the concrete biological issues and the analytical methods. Hence, this study compares 26 descriptor sets obtained by applying well-known numerical description concepts to an established dataset of 38 DNA promoter sequences. The suitability of the description sets was evaluated by computing partial least squares regression models and assessing the model accuracy. We conclude that the major importance regarding the descriptive power is attached to positional information rather than to explicitly incorporated physico-chemical information, since a sufficient amount of implicit physico-chemical information is already encoded in the nucleobase classification. The regression models especially benefited from employing the information that is encoded in the sequential and structural neighborhood of the nucleobases. Thus, the analyses of n-grams (short fragments of length n) suggested that they are valuable descriptors for DNA target interactions. A mixed n-gram descriptor set thereby yielded the best description of the promoter sequences. The corresponding regression model was checked and found to be plausible as it was able to reproduce the characteristic binding motifs of promoter sequences in a reasonable degree. As most functional nucleic acids are based on the principle of molecular recognition, the findings are not restricted to promoter sequences, but can rather be transferred to other kinds of functional nucleic acids. Thus, the concepts presented in this study could provide advantages for future nucleic acid-based technologies, like biosensoring, therapeutics and molecular imaging. Copyright © 2015 Elsevier Inc. All rights reserved.

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.

A contour-based shape descriptor for biomedical image classification and retrieval

NASA Astrophysics Data System (ADS)

You, Daekeun; Antani, Sameer; Demner-Fushman, Dina; Thoma, George R.

2013-12-01

Contours, object blobs, and specific feature points are utilized to represent object shapes and extract shape descriptors that can then be used for object detection or image classification. In this research we develop a shape descriptor for biomedical image type (or, modality) classification. We adapt a feature extraction method used in optical character recognition (OCR) for character shape representation, and apply various image preprocessing methods to successfully adapt the method to our application. The proposed shape descriptor is applied to radiology images (e.g., MRI, CT, ultrasound, X-ray, etc.) to assess its usefulness for modality classification. In our experiment we compare our method with other visual descriptors such as CEDD, CLD, Tamura, and PHOG that extract color, texture, or shape information from images. The proposed method achieved the highest classification accuracy of 74.1% among all other individual descriptors in the test, and when combined with CSD (color structure descriptor) showed better performance (78.9%) than using the shape descriptor alone.

Momentum Distribution as a Fingerprint of Quantum Delocalization in Enzymatic Reactions: Open-Chain Path-Integral Simulations of Model Systems and the Hydride Transfer in Dihydrofolate Reductase.

PubMed

Engel, Hamutal; Doron, Dvir; Kohen, Amnon; Major, Dan Thomas

2012-04-10

The inclusion of nuclear quantum effects such as zero-point energy and tunneling is of great importance in studying condensed phase chemical reactions involving the transfer of protons, hydrogen atoms, and hydride ions. In the current work, we derive an efficient quantum simulation approach for the computation of the momentum distribution in condensed phase chemical reactions. The method is based on a quantum-classical approach wherein quantum and classical simulations are performed separately. The classical simulations use standard sampling techniques, whereas the quantum simulations employ an open polymer chain path integral formulation which is computed using an efficient Monte Carlo staging algorithm. The approach is validated by applying it to a one-dimensional harmonic oscillator and symmetric double-well potential. Subsequently, the method is applied to the dihydrofolate reductase (DHFR) catalyzed reduction of 7,8-dihydrofolate by nicotinamide adenine dinucleotide phosphate hydride (NADPH) to yield S-5,6,7,8-tetrahydrofolate and NADP(+). The key chemical step in the catalytic cycle of DHFR involves a stereospecific hydride transfer. In order to estimate the amount of quantum delocalization, we compute the position and momentum distributions for the transferring hydride ion in the reactant state (RS) and transition state (TS) using a recently developed hybrid semiempirical quantum mechanics-molecular mechanics potential energy surface. Additionally, we examine the effect of compression of the donor-acceptor distance (DAD) in the TS on the momentum distribution. The present results suggest differential quantum delocalization in the RS and TS, as well as reduced tunneling upon DAD compression.

Chemical accuracy from quantum Monte Carlo for the benzene dimer.

PubMed

Azadi, Sam; Cohen, R E

2015-09-14

We report an accurate study of interactions between benzene molecules using variational quantum Monte Carlo (VMC) and diffusion quantum Monte Carlo (DMC) methods. We compare these results with density functional theory using different van der Waals functionals. In our quantum Monte Carlo (QMC) calculations, we use accurate correlated trial wave functions including three-body Jastrow factors and backflow transformations. We consider two benzene molecules in the parallel displaced geometry, and find that by highly optimizing the wave function and introducing more dynamical correlation into the wave function, we compute the weak chemical binding energy between aromatic rings accurately. We find optimal VMC and DMC binding energies of -2.3(4) and -2.7(3) kcal/mol, respectively. The best estimate of the coupled-cluster theory through perturbative triplets/complete basis set limit is -2.65(2) kcal/mol [Miliordos et al., J. Phys. Chem. A 118, 7568 (2014)]. Our results indicate that QMC methods give chemical accuracy for weakly bound van der Waals molecular interactions, comparable to results from the best quantum chemistry methods.

Quantum dot nanoparticle conjugation, characterization, and applications in neuroscience

NASA Astrophysics Data System (ADS)

Pathak, Smita

Quantum dot are semiconducting nanoparticles that have been used for decades in a variety of applications such as solar cells, LEDs and medical imaging. Their use in the last area, however, has been extremely limited despite their potential as revolutionary new biological labeling tools. Quantum dots are much brighter and more stable than conventional fluorophores, making them optimal for high resolution imaging and long term studies. Prior work in this area involves synthesizing and chemically conjugating quantum dots to molecules of interest in-house. However this method is both time consuming and prone to human error. Additionally, non-specific binding and nanoparticle aggregation currently prevent researchers from utilizing this system to its fullest capacity. Another critical issue that has not been addressed is determining the number of ligands bound to nanoparticles, which is crucial for proper interpretation of results. In this work, methods to label fixed cells using two types of chemically modified quantum dots are studied. Reproducible non-specific artifact labeling is consistently demonstrated if antibody-quantum dot conditions are less than optimal. In order to explain this, antibodies bound to quantum dots were characterized and quantified. While other groups have qualitatively characterized antibody functionalized quantum dots using TEM, AFM, UV spectroscopy and gel electrophoresis, and in some cases have reported calculated estimates of the putative number of total antibodies bound to quantum dots, no quantitative experimental results had been reported prior to this work. The chemical functionalization and characterization of quantum dot nanocrystals achieved in this work elucidates binding mechanisms of ligands to nanoparticles and allows researchers to not only translate our tools to studies in their own areas of interest but also derive quantitative results from these studies. This research brings ease of use and increased reliability to nanoparticles in medical imaging.

Quantitating the Absorption, Partitioning and Toxicity of Hydrocarbon Components of JP-8 Jet Fuel

DTIC Science & Technology

2007-08-24

with the skin. AFOSR Jet Fuel Toxicology Workshop. Tucson, AZ. October, 2004. 5. Basak SC, Riviere JE, Baynes RE, Xia XR, Gute BD. A hierarchical QSAR ... Toxicology Workshop, Tucson, AZ, 2005. 12. Basak SC, Riviere J, Baynes R, Gute BD: Theoretical descriptor based QSARs in predicting skin penetration of...NAME(S) AND ADDRESS(ES) 8. PERFORMING ORGANIZATION REPORT NUMBER Center for Chemical Toxicology Research and Pharmacokinetics College of Veterinary

Aryl-modified graphene quantum dots with enhanced photoluminescence and improved pH tolerance

NASA Astrophysics Data System (ADS)

Luo, Peihui; Ji, Zhe; Li, Chun; Shi, Gaoquan

2013-07-01

Chemical modification is an important technique to modulate the chemical and optical properties of graphene quantum dots (GQDs). In this paper, we report a versatile diazonium chemistry method to graft aryl groups including phenyl, 4-carboxyphenyl, 4-sulfophenyl and 5-sulfonaphthyl to GQDs via Gomberg-Bachmann reaction. The aryl-modified GQDs are nanocrystals with lateral dimensions in the range of 2-4 nm and an average thickness lower than 1 nm. Upon chemical modification with aryl groups, the photoluminescence (PL) bands of GQDs were tuned in the range of 418 and 447 nm, and their fluorescence quantum yields (QYs) were increased for up to about 6 times. Furthermore, the aryl-modified GQDs exhibited stable PL (both intensity and peak position) in a wide pH window of 1-11. The mechanism of improving the PL properties of GQDs by aryl-modification was also discussed.Chemical modification is an important technique to modulate the chemical and optical properties of graphene quantum dots (GQDs). In this paper, we report a versatile diazonium chemistry method to graft aryl groups including phenyl, 4-carboxyphenyl, 4-sulfophenyl and 5-sulfonaphthyl to GQDs via Gomberg-Bachmann reaction. The aryl-modified GQDs are nanocrystals with lateral dimensions in the range of 2-4 nm and an average thickness lower than 1 nm. Upon chemical modification with aryl groups, the photoluminescence (PL) bands of GQDs were tuned in the range of 418 and 447 nm, and their fluorescence quantum yields (QYs) were increased for up to about 6 times. Furthermore, the aryl-modified GQDs exhibited stable PL (both intensity and peak position) in a wide pH window of 1-11. The mechanism of improving the PL properties of GQDs by aryl-modification was also discussed. Electronic supplementary information (ESI) available: Fluorescence quantum yield measurements, estimation of grafting ratio, TEM images, FTIR spectra, PL spectra and zeta potentials. See DOI: 10.1039/c3nr02156d

Old Wine in New Bottles: Quantum Theory in Historical Perspective.

ERIC Educational Resources Information Center

Bent, Henry A.

1984-01-01

Discusses similarities between chemistry and three central concepts of quantum physics: (1) stationary states; (2) wave functions; and (3) complementarity. Based on these and other similarities, it is indicated that quantum physics is a chemical physics. (JN)

Exploring consumer exposure pathways and patterns of use for chemicals in the environment.

PubMed

Dionisio, Kathie L; Frame, Alicia M; Goldsmith, Michael-Rock; Wambaugh, John F; Liddell, Alan; Cathey, Tommy; Smith, Doris; Vail, James; Ernstoff, Alexi S; Fantke, Peter; Jolliet, Olivier; Judson, Richard S

2015-01-01

Humans are exposed to thousands of chemicals in the workplace, home, and via air, water, food, and soil. A major challenge in estimating chemical exposures is to understand which chemicals are present in these media and microenvironments. Here we describe the Chemical/Product Categories Database (CPCat), a new, publically available (http://actor.epa.gov/cpcat) database of information on chemicals mapped to "use categories" describing the usage or function of the chemical. CPCat was created by combining multiple and diverse sources of data on consumer- and industrial-process based chemical uses from regulatory agencies, manufacturers, and retailers in various countries. The database uses a controlled vocabulary of 833 terms and a novel nomenclature to capture and streamline descriptors of chemical use for 43,596 chemicals from the various sources. Examples of potential applications of CPCat are provided, including identifying chemicals to which children may be exposed and to support prioritization of chemicals for toxicity screening. CPCat is expected to be a valuable resource for regulators, risk assessors, and exposure scientists to identify potential sources of human exposures and exposure pathways, particularly for use in high-throughput chemical exposure assessment.

An effective content-based image retrieval technique for image visuals representation based on the bag-of-visual-words model

PubMed Central

Jabeen, Safia; Mehmood, Zahid; Mahmood, Toqeer; Saba, Tanzila; Rehman, Amjad; Mahmood, Muhammad Tariq

2018-01-01

For the last three decades, content-based image retrieval (CBIR) has been an active research area, representing a viable solution for retrieving similar images from an image repository. In this article, we propose a novel CBIR technique based on the visual words fusion of speeded-up robust features (SURF) and fast retina keypoint (FREAK) feature descriptors. SURF is a sparse descriptor whereas FREAK is a dense descriptor. Moreover, SURF is a scale and rotation-invariant descriptor that performs better in the case of repeatability, distinctiveness, and robustness. It is robust to noise, detection errors, geometric, and photometric deformations. It also performs better at low illumination within an image as compared to the FREAK descriptor. In contrast, FREAK is a retina-inspired speedy descriptor that performs better for classification-based problems as compared to the SURF descriptor. Experimental results show that the proposed technique based on the visual words fusion of SURF-FREAK descriptors combines the features of both descriptors and resolves the aforementioned issues. The qualitative and quantitative analysis performed on three image collections, namely Corel-1000, Corel-1500, and Caltech-256, shows that proposed technique based on visual words fusion significantly improved the performance of the CBIR as compared to the feature fusion of both descriptors and state-of-the-art image retrieval techniques. PMID:29694429

An effective content-based image retrieval technique for image visuals representation based on the bag-of-visual-words model.

PubMed

Jabeen, Safia; Mehmood, Zahid; Mahmood, Toqeer; Saba, Tanzila; Rehman, Amjad; Mahmood, Muhammad Tariq

2018-01-01

For the last three decades, content-based image retrieval (CBIR) has been an active research area, representing a viable solution for retrieving similar images from an image repository. In this article, we propose a novel CBIR technique based on the visual words fusion of speeded-up robust features (SURF) and fast retina keypoint (FREAK) feature descriptors. SURF is a sparse descriptor whereas FREAK is a dense descriptor. Moreover, SURF is a scale and rotation-invariant descriptor that performs better in the case of repeatability, distinctiveness, and robustness. It is robust to noise, detection errors, geometric, and photometric deformations. It also performs better at low illumination within an image as compared to the FREAK descriptor. In contrast, FREAK is a retina-inspired speedy descriptor that performs better for classification-based problems as compared to the SURF descriptor. Experimental results show that the proposed technique based on the visual words fusion of SURF-FREAK descriptors combines the features of both descriptors and resolves the aforementioned issues. The qualitative and quantitative analysis performed on three image collections, namely Corel-1000, Corel-1500, and Caltech-256, shows that proposed technique based on visual words fusion significantly improved the performance of the CBIR as compared to the feature fusion of both descriptors and state-of-the-art image retrieval techniques.

Algorithms Bridging Quantum Computation and Chemistry

NASA Astrophysics Data System (ADS)

McClean, Jarrod Ryan

The design of new materials and chemicals derived entirely from computation has long been a goal of computational chemistry, and the governing equation whose solution would permit this dream is known. Unfortunately, the exact solution to this equation has been far too expensive and clever approximations fail in critical situations. Quantum computers offer a novel solution to this problem. In this work, we develop not only new algorithms to use quantum computers to study hard problems in chemistry, but also explore how such algorithms can help us to better understand and improve our traditional approaches. In particular, we first introduce a new method, the variational quantum eigensolver, which is designed to maximally utilize the quantum resources available in a device to solve chemical problems. We apply this method in a real quantum photonic device in the lab to study the dissociation of the helium hydride (HeH+) molecule. We also enhance this methodology with architecture specific optimizations on ion trap computers and show how linear-scaling techniques from traditional quantum chemistry can be used to improve the outlook of similar algorithms on quantum computers. We then show how studying quantum algorithms such as these can be used to understand and enhance the development of classical algorithms. In particular we use a tool from adiabatic quantum computation, Feynman's Clock, to develop a new discrete time variational principle and further establish a connection between real-time quantum dynamics and ground state eigenvalue problems. We use these tools to develop two novel parallel-in-time quantum algorithms that outperform competitive algorithms as well as offer new insights into the connection between the fermion sign problem of ground states and the dynamical sign problem of quantum dynamics. Finally we use insights gained in the study of quantum circuits to explore a general notion of sparsity in many-body quantum systems. In particular we use developments from the field of compressed sensing to find compact representations of ground states. As an application we study electronic systems and find solutions dramatically more compact than traditional configuration interaction expansions, offering hope to extend this methodology to challenging systems in chemical and material design.

The Independent Gradient Model: A New Approach for Probing Strong and Weak Interactions in Molecules from Wave Function Calculations.

PubMed

Lefebvre, Corentin; Khartabil, Hassan; Boisson, Jean-Charles; Contreras-García, Julia; Piquemal, Jean-Philip; Hénon, Eric

2018-03-19

Extraction of the chemical interaction signature from local descriptors based on electron density (ED) is still a fruitful field of development in chemical interpretation. In a previous work that used promolecular ED (frozen ED), the new descriptor, δg , was defined. It represents the difference between a virtual upper limit of the ED gradient (∇ρIGM , IGM=independent gradient model) that represents a noninteracting system and the true ED gradient (∇ρ ). It can be seen as a measure of electron sharing brought by ED contragradience. A compelling feature of this model is to provide an automatic workflow that extracts the signature of interactions between selected groups of atoms. As with the noncovalent interaction (NCI) approach, it provides chemists with a visual understanding of the interactions present in chemical systems. ∇ρIGM is achieved simply by using absolute values upon summing the individual gradient contributions that make up the total ED gradient. Hereby, we extend this model to relaxed ED calculated from a wave function. To this end, we formulated gradient-based partitioning (GBP) to assess the contribution of each orbital to the total ED gradient. We highlight these new possibilities across two prototypical examples of organic chemistry: the unconventional hexamethylbenzene dication, with a hexa-coordinated carbon atom, and β-thioaminoacrolein. It will be shown how a bond-by-bond picture can be obtained from a wave function, which opens the way to monitor specific interactions along reaction paths. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Quantum Chemically Estimated Abraham Solute Parameters Using Multiple Solvent-Water Partition Coefficients and Molecular Polarizability.

PubMed

Liang, Yuzhen; Xiong, Ruichang; Sandler, Stanley I; Di Toro, Dominic M

2017-09-05

Polyparameter Linear Free Energy Relationships (pp-LFERs), also called Linear Solvation Energy Relationships (LSERs), are used to predict many environmentally significant properties of chemicals. A method is presented for computing the necessary chemical parameters, the Abraham parameters (AP), used by many pp-LFERs. It employs quantum chemical calculations and uses only the chemical's molecular structure. The method computes the Abraham E parameter using density functional theory computed molecular polarizability and the Clausius-Mossotti equation relating the index refraction to the molecular polarizability, estimates the Abraham V as the COSMO calculated molecular volume, and computes the remaining AP S, A, and B jointly with a multiple linear regression using sixty-five solvent-water partition coefficients computed using the quantum mechanical COSMO-SAC solvation model. These solute parameters, referred to as Quantum Chemically estimated Abraham Parameters (QCAP), are further adjusted by fitting to experimentally based APs using QCAP parameters as the independent variables so that they are compatible with existing Abraham pp-LFERs. QCAP and adjusted QCAP for 1827 neutral chemicals are included. For 24 solvent-water systems including octanol-water, predicted log solvent-water partition coefficients using adjusted QCAP have the smallest root-mean-square errors (RMSEs, 0.314-0.602) compared to predictions made using APs estimated using the molecular fragment based method ABSOLV (0.45-0.716). For munition and munition-like compounds, adjusted QCAP has much lower RMSE (0.860) than does ABSOLV (4.45) which essentially fails for these compounds.

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.

Automated chemical kinetic modeling via hybrid reactive molecular dynamics and quantum chemistry simulations.

PubMed

Döntgen, Malte; Schmalz, Felix; Kopp, Wassja A; Kröger, Leif C; Leonhard, Kai

2018-06-13

An automated scheme for obtaining chemical kinetic models from scratch using reactive molecular dynamics and quantum chemistry simulations is presented. This methodology combines the phase space sampling of reactive molecular dynamics with the thermochemistry and kinetics prediction capabilities of quantum mechanics. This scheme provides the NASA polynomial and modified Arrhenius equation parameters for all species and reactions that are observed during the simulation and supplies them in the ChemKin format. The ab initio level of theory for predictions is easily exchangeable and the presently used G3MP2 level of theory is found to reliably reproduce hydrogen and methane oxidation thermochemistry and kinetics data. Chemical kinetic models obtained with this approach are ready-to-use for, e.g., ignition delay time simulations, as shown for hydrogen combustion. The presented extension of the ChemTraYzer approach can be used as a basis for methodologically advancing chemical kinetic modeling schemes and as a black-box approach to generate chemical kinetic models.

Electronic properties and free radical production by nitrofuran compounds.

PubMed

Paulino-Blumenfeld, M; Hansz, M; Hikichi, N; Stoppani, A O

1992-01-01

Substitution of nifurtimox tetrahydrothiazine moiety by triazol-4-yl, benzimidazol-l-yl, pyrazol-l-yl or related aromatic nitrogen heterocycles determines changes in the quantum chemistry descriptors of the molecule, namely, (a) greater negative LUMO energy; (b) lesser electron density on specific atoms, especially on the nitro group atoms, and (c) modification of individual net atomic charges at relevant atoms. These variations correlate with the greater capability of nifurtimox analogues for redox-cycling and oxygen radical production, after one-electron reduction by ascorbate or reduced flavoenzymes. Variation of the nitrofurans electronic structure can also explain the greater activity of nifurtimox analogues as inhibitors of glutathione reductase and Trypanosoma cruzi growth, although other factors, such as molecular hydrophobicity and connectivity may contribute to the latter inhibition.

Use of external cavity quantum cascade laser compliance voltage in real-time trace gas sensing of multiple chemicals

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

Phillips, Mark C.; Taubman, Matthew S.; Kriesel, Jason M.

2015-02-08

We describe a prototype trace gas sensor designed for real-time detection of multiple chemicals. The sensor uses an external cavity quantum cascade laser (ECQCL) swept over its tuning range of 940-1075 cm-1 (9.30-10.7 µm) at a 10 Hz repetition rate.

Quantum chemical study of the inhibition of the corrosion of mild steel in H2SO4 by some antibiotics.

PubMed

Eddy, Nnabuk O; Ibok, Udo J; Ebenso, Eno E; El Nemr, Ahmed; El Ashry, El Sayed H

2009-09-01

The inhibition efficiency of some antibiotics against mild steel corrosion was studied using weight loss and quantum chemical techniques. Values of inhibition efficiency obtained from weight loss measurements correlated strongly with theoretical values obtained through semi empirical calculations. High correlation coefficients were also obtained between inhibition efficiency of the antibiotics and some quantum chemical parameters, including frontier orbital (E (HOMO) and E (LUMO)), dipole moment, log P, TNC and LSER parameters (critical volume and dipolar-polarisability factor), which indicated that these parameters affect the inhibition efficiency of the compounds. It was also found that quantitative structure activity relation can be used to adequately predict the inhibition effectiveness of these compounds.

QSAR, DFT and quantum chemical studies on the inhibition potentials of some carbozones for the corrosion of mild steel in HCl.

PubMed

Eddy, Nnabuk O; Ita, Benedict I

2011-02-01

Experimental aspects of the inhibition of the corrosion of mild steel in HCl solutions by some carbozones were studied using gravimetric, thermometric and gasometric methods, while a theoretical study was carried out using density functional theory, a quantitative structure-activity relation, and quantum chemical principles. The results obtained indicated that the studied carbozones are good adsorption inhibitors for the corrosion of mild steel in HCl. The inhibition efficiencies of the studied carbozones were found to increase with increasing concentration of the respective inhibitor. A strong correlation was found between the average inhibition efficiency and some quantum chemical parameters, and also between the experimental and theoretical inhibition efficiencies (obtained from the quantitative structure-activity relation).

Structural and vibrational spectral investigations of melaminium maleate monohydrate by FTIR, FT-Raman and quantum chemical calculations

NASA Astrophysics Data System (ADS)

Arjunan, V.; Kalaivani, M.; Marchewka, M. K.; Mohan, S.

2013-04-01

The structural investigations of the molecular complex of melamine with maleic acid, namely melaminium maleate monohydrate have been carried out by quantum chemical methods in addition to FTIR, FT-Raman and far-infrared spectral studies. The quantum chemical studies were performed with DFT (B3LYP) method using 6-31G**, cc-pVDZ and 6-311++G** basis sets to determine the energy, structural and thermodynamic parameters of melaminium maleate monohydrate. The hydrogen atom from maleic acid was transferred to the melamine molecule giving the singly protonated melaminium cation. The ability of ions to form spontaneous three-dimensional structure through weak Osbnd H⋯O and Nsbnd H⋯O hydrogen bonds shows notable vibrational effects.

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

Characterization of Virgin Olive Oils with Two Kinds of 'Frostbitten Olives' Sensory Defect.

PubMed

Romero, Inmaculada; Aparicio-Ruiz, Ramón; Oliver-Pozo, Celia; Aparicio, Ramón; García-González, Diego L

2016-07-13

The frost of olives on the tree due to drops of temperature can produce sensory defects in virgin olive oil (VOO). Temperature changes can be abrupt with freeze-thaw cycles or gradual, and they produce sensory and chemical variations in the oil. This study has analyzed the quality parameters (free fatty acids, peroxide value, UV absorption, and fatty acid ethyl esters) and phenols of VOOs described with the 'frostbitten olives' sensory defect. The phenol profiles allowed grouping these VOOs into two types. One of them, characterized with "soapy" and "strawberry-like" aroma descriptors, had higher values of 1-acetoxypinoresinol, pinoresinol, and aldehydic form of the ligstroside aglycon. The other one, characterized with "wood" and "humidity" descriptors, had higher concentrations of luteolin and apigenin. Most VOOs (75%) from the first group, associated with abrupt drops of temperature, have concentration of phenols higher than the value established by the health claim on olive oil polyphenols approved by the European Commission.

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

Signaling completion of a message transfer from an origin compute node to a target compute node

DOEpatents

Blocksome, Michael A [Rochester, MN; Parker, Jeffrey J [Rochester, MN

2011-05-24

Signaling completion of a message transfer from an origin node to a target node includes: sending, by an origin DMA engine, an RTS message, the RTS message specifying an application message for transfer to the target node from the origin node; receiving, by the origin DMA engine, a remote get message containing a data descriptor for the message and a completion notification descriptor, the completion notification descriptor specifying a local direct put transfer operation for transferring data locally on the origin node; inserting, by the origin DMA engine in an injection FIFO buffer, the data descriptor followed by the completion notification descriptor; transferring, by the origin DMA engine to the target node, the message in dependence upon the data descriptor; and notifying, by the origin DMA engine, the application that transfer of the message is complete in dependence upon the completion notification descriptor.

Direct memory access transfer completion notification

DOEpatents

Archer, Charles J. , Blocksome; Michael A. , Parker; Jeffrey, J [Rochester, MN

2011-02-15

Methods, systems, and products are disclosed for DMA transfer completion notification that include: inserting, by an origin DMA on an origin node in an origin injection FIFO, a data descriptor for an application message; inserting, by the origin DMA, a reflection descriptor in the origin injection FIFO, the reflection descriptor specifying a remote get operation for injecting a completion notification descriptor in a reflection injection FIFO on a reflection node; transferring, by the origin DMA to a target node, the message in dependence upon the data descriptor; in response to completing the message transfer, transferring, by the origin DMA to the reflection node, the completion notification descriptor in dependence upon the reflection descriptor; receiving, by the origin DMA from the reflection node, a completion packet; and notifying, by the origin DMA in response to receiving the completion packet, the origin node's processing core that the message transfer is complete.

Signaling completion of a message transfer from an origin compute node to a target compute node

DOEpatents

Blocksome, Michael A [Rochester, MN

2011-02-15

Signaling completion of a message transfer from an origin node to a target node includes: sending, by an origin DMA engine, an RTS message, the RTS message specifying an application message for transfer to the target node from the origin node; receiving, by the origin DMA engine, a remote get message containing a data descriptor for the message and a completion notification descriptor, the completion notification descriptor specifying a local memory FIFO data transfer operation for transferring data locally on the origin node; inserting, by the origin DMA engine in an injection FIFO buffer, the data descriptor followed by the completion notification descriptor; transferring, by the origin DMA engine to the target node, the message in dependence upon the data descriptor; and notifying, by the origin DMA engine, the application that transfer of the message is complete in dependence upon the completion notification descriptor.

Multi-Scale Surface Descriptors

PubMed Central

Cipriano, Gregory; Phillips, George N.; Gleicher, Michael

2010-01-01

Local shape descriptors compactly characterize regions of a surface, and have been applied to tasks in visualization, shape matching, and analysis. Classically, curvature has be used as a shape descriptor; however, this differential property characterizes only an infinitesimal neighborhood. In this paper, we provide shape descriptors for surface meshes designed to be multi-scale, that is, capable of characterizing regions of varying size. These descriptors capture statistically the shape of a neighborhood around a central point by fitting a quadratic surface. They therefore mimic differential curvature, are efficient to compute, and encode anisotropy. We show how simple variants of mesh operations can be used to compute the descriptors without resorting to expensive parameterizations, and additionally provide a statistical approximation for reduced computational cost. We show how these descriptors apply to a number of uses in visualization, analysis, and matching of surfaces, particularly to tasks in protein surface analysis. PMID:19834190

Respiratory complaints in Chinese: cultural and diagnostic specificities.

PubMed

Han, Jiangna; Zhu, Yuanjue; Li, Shunwei; Chen, Xiansheng; Put, Claudia; Van de Woestijne, Karel P; Van den Bergh, Omer

2005-06-01

We investigated the qualitative components of a wide range of Chinese descriptors of dyspnea and associated symptoms, and their relevance for clinical diagnosis. Sixty-one spontaneously reported descriptors were elicited in Chinese patients to make a symptom checklist, which was administered to new groups of patients with different cardiopulmonary diseases, to patients with medically unexplained dyspnea and to healthy subjects. Test-retest reliability was satisfactory for most of the descriptors. A principal component analysis on 61 descriptors yielded the following eight factors: dyspnea-effort of breathing; dyspnea-affective aspect; wheezing; anxiety; tingling; palpitation; coughing and sputum; and dying experience. Although the descriptors of dyspnea-effort of breathing resembled Western wordings and were shared by patients with a variety of diseases, the descriptors of dyspnea-affective aspect appeared to be more culturally specific and were primarily linked to the diagnosis of medically unexplained dyspnea, whereas wheezing was specifically linked to asthma. Three factors of breathlessness were found in Chinese. The descriptors of dyspnea-effort of breathing and wheezing appear to be similar to Western descriptors, whereas the dyspnea-affective aspect seems to bear cultural specificity.

Receptive fields selection for binary feature description.

PubMed

Fan, Bin; Kong, Qingqun; Trzcinski, Tomasz; Wang, Zhiheng; Pan, Chunhong; Fua, Pascal

2014-06-01

Feature description for local image patch is widely used in computer vision. While the conventional way to design local descriptor is based on expert experience and knowledge, learning-based methods for designing local descriptor become more and more popular because of their good performance and data-driven property. This paper proposes a novel data-driven method for designing binary feature descriptor, which we call receptive fields descriptor (RFD). Technically, RFD is constructed by thresholding responses of a set of receptive fields, which are selected from a large number of candidates according to their distinctiveness and correlations in a greedy way. Using two different kinds of receptive fields (namely rectangular pooling area and Gaussian pooling area) for selection, we obtain two binary descriptors RFDR and RFDG .accordingly. Image matching experiments on the well-known patch data set and Oxford data set demonstrate that RFD significantly outperforms the state-of-the-art binary descriptors, and is comparable with the best float-valued descriptors at a fraction of processing time. Finally, experiments on object recognition tasks confirm that both RFDR and RFDG successfully bridge the performance gap between binary descriptors and their floating-point competitors.

Alignment-independent comparison of binding sites based on DrugScore potential fields encoded by 3D Zernike descriptors.

PubMed

Nisius, Britta; Gohlke, Holger

2012-09-24

Analyzing protein binding sites provides detailed insights into the biological processes proteins are involved in, e.g., into drug-target interactions, and so is of crucial importance in drug discovery. Herein, we present novel alignment-independent binding site descriptors based on DrugScore potential fields. The potential fields are transformed to a set of information-rich descriptors using a series expansion in 3D Zernike polynomials. The resulting Zernike descriptors show a promising performance in detecting similarities among proteins with low pairwise sequence identities that bind identical ligands, as well as within subfamilies of one target class. Furthermore, the Zernike descriptors are robust against structural variations among protein binding sites. Finally, the Zernike descriptors show a high data compression power, and computing similarities between binding sites based on these descriptors is highly efficient. Consequently, the Zernike descriptors are a useful tool for computational binding site analysis, e.g., to predict the function of novel proteins, off-targets for drug candidates, or novel targets for known drugs.

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.

Dyspnea descriptors developed in Brazil: application in obese patients and in patients with cardiorespiratory diseases.

PubMed

Teixeira, Christiane Aires; Rodrigues Júnior, Antonio Luiz; Straccia, Luciana Cristina; Vianna, Elcio Dos Santos Oliveira; Silva, Geruza Alves da; Martinez, José Antônio Baddini

2011-01-01

To develop a set of descriptive terms applied to the sensation of dyspnea (dyspnea descriptors) for use in Brazil and to investigate the usefulness of these descriptors in four distinct clinical conditions that can be accompanied by dyspnea. We collected 111 dyspnea descriptors from 67 patients and 10 health professionals. These descriptors were analyzed and reduced to 15 based on their frequency of use, similarity of meaning, and potential pathophysiological value. Those 15 descriptors were applied in 50 asthma patients, 50 COPD patients, 30 patients with heart failure, and 50 patients with class II or III obesity. The three best descriptors, as selected by the patients, were studied by cluster analysis. Potential associations between the identified clusters and the four clinical conditions were also investigated. The use of this set of descriptors led to a solution with seven clusters, designated sufoco (suffocating), aperto (tight), rápido (rapid), fadiga (fatigue), abafado (stuffy), trabalho/inspiração (work/inhalation), and falta de ar (shortness of breath). Overlapping of descriptors was quite common among the patients, regardless of their clinical condition. Asthma was significantly associated with the sufoco and trabalho/inspiração clusters, whereas COPD and heart failure were associated with the sufoco, trabalho/inspiração, and falta de ar clusters. Obesity was associated only with the falta de ar cluster. In Brazil, patients who are accustomed to perceiving dyspnea employ various descriptors in order to describe the symptom, and these descriptors can be grouped into similar clusters. In our study sample, such clusters showed no usefulness in differentiating among the four clinical conditions evaluated.

Reproducibility of the NEPTUNE descriptor-based scoring system on whole-slide images and histologic and ultrastructural digital images.

PubMed

Barisoni, Laura; Troost, Jonathan P; Nast, Cynthia; Bagnasco, Serena; Avila-Casado, Carmen; Hodgin, Jeffrey; Palmer, Matthew; Rosenberg, Avi; Gasim, Adil; Liensziewski, Chrysta; Merlino, Lino; Chien, Hui-Ping; Chang, Anthony; Meehan, Shane M; Gaut, Joseph; Song, Peter; Holzman, Lawrence; Gibson, Debbie; Kretzler, Matthias; Gillespie, Brenda W; Hewitt, Stephen M

2016-07-01

The multicenter Nephrotic Syndrome Study Network (NEPTUNE) digital pathology scoring system employs a novel and comprehensive methodology to document pathologic features from whole-slide images, immunofluorescence and ultrastructural digital images. To estimate inter- and intra-reader concordance of this descriptor-based approach, data from 12 pathologists (eight NEPTUNE and four non-NEPTUNE) with experience from training to 30 years were collected. A descriptor reference manual was generated and a webinar-based protocol for consensus/cross-training implemented. Intra-reader concordance for 51 glomerular descriptors was evaluated on jpeg images by seven NEPTUNE pathologists scoring 131 glomeruli three times (Tests I, II, and III), each test following a consensus webinar review. Inter-reader concordance of glomerular descriptors was evaluated in 315 glomeruli by all pathologists; interstitial fibrosis and tubular atrophy (244 cases, whole-slide images) and four ultrastructural podocyte descriptors (178 cases, jpeg images) were evaluated once by six and five pathologists, respectively. Cohen's kappa for inter-reader concordance for 48/51 glomerular descriptors with sufficient observations was moderate (0.40

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

Bohm's Quantum Potential and the Visualization of Molecular Structure

NASA Technical Reports Server (NTRS)

Levit, Creon; Chancellor, Marisa K. (Technical Monitor)

1997-01-01

David Bohm's ontological interpretation of quantum theory can shed light on otherwise counter-intuitive quantum mechanical phenomena including chemical bonding. In the field of quantum chemistry, Richard Bader has shown that the topology of the Laplacian of the electronic charge density characterizes many features of molecular structure and reactivity. Visual and computational examination suggests that the Laplacian of Bader and the quantum potential of Bohm are morphologically equivalent. It appears that Bohmian mechanics and the quantum potential can make chemistry as clear as they makes physics.

Fungal bis-Naphthopyrones as Inhibitors of Botulinum Neurotoxin Serotype A

DTIC Science & Technology

2012-04-02

Ashish G. Soman,§ Biren K. Joshi,§ Sara M. Hein,§ Donald T. Wicklow,∥ and Leonard A. Smith*,⊥ †Division of Integrated Toxicology , U.S. Army Medical...of chemicals for bacterial mutagenicity using electrotopological E-state indices and MDL QSAR software. Regul. Toxicol. Pharmacol. 2005, 43, 313−323...12) Feng, J.; Lurati, L.; Ouyang, H.; Robinson, T.; Wang, Y.; Yuan, S.; Young, S. S. Predictive toxicology : Benchmarking molecular descriptors and

lazar: a modular predictive toxicology framework

PubMed Central

Maunz, Andreas; Gütlein, Martin; Rautenberg, Micha; Vorgrimmler, David; Gebele, Denis; Helma, Christoph

2013-01-01

lazar (lazy structure–activity relationships) is a modular framework for predictive toxicology. Similar to the read across procedure in toxicological risk assessment, lazar creates local QSAR (quantitative structure–activity relationship) models for each compound to be predicted. Model developers can choose between a large variety of algorithms for descriptor calculation and selection, chemical similarity indices, and model building. This paper presents a high level description of the lazar framework and discusses the performance of example classification and regression models. PMID:23761761

The association between negative self-descriptions and depressive symptomology: does culture make a difference?

PubMed

Saint Arnault, Denise; Sakamoto, Shinji; Moriwaki, Aiko

2005-04-01

Research findings that depressed Americans endorse more negative self-related adjectives than controls may be related to a shared self-enhancement cultural frame. This study examines the relationship between negative core self-descriptors and depressive symptoms in 79 Japanese and 50 American women. Americans had more positive self-descriptions and core self-descriptors; however, there were no cultural group differences in number of negative self-descriptors or core self-descriptors. There was a significant correlation between negative core self-descriptor and Beck Depression Inventory (BDI) for Americans only, explaining 10.6% of the BDI variance. Analysis of variance revealed that there was significant BDI group differences for American negative core self-descriptor only. Theoretical possibilities are discussed.

The Association Between Negative Self-Descriptions and Depressive Symptomology: Does Culture Make a Difference?

PubMed Central

Sakamoto, Shinji; Moriwaki, Aiko

2007-01-01

Research findings that depressed Americans endorse more negative self-related adjectives than controls may be related to a shared self-enhancement cultural frame. This study examines the relationship between negative core self-descriptors and depressive symptoms in 79 Japanese and 50 American women. Americans had more positive self-descriptions and core self-descriptors; however, there were no cultural group differences in number of negative self-descriptors or core self-descriptors. There was a significant correlation between negative core self-descriptor and Beck Depression Inventory (BDI) for Americans only, explaining 10.6% of the BDI variance. Analysis of variance revealed that there was significant BDI group differences for American negative core self-descriptor only. Theoretical possibilities are discussed. PMID:15902678

Towards first-principles calculation of electronic excitations in the ring of the protein-bound bacteriochlorophylls

NASA Astrophysics Data System (ADS)

Polyakov, Igor V.; Khrenova, Maria G.; Moskovsky, Alexander A.; Shabanov, Boris M.; Nemukhin, Alexander V.

2018-04-01

Modeling electronic excitation of bacteriochlorophyll (BChl) molecules in light-harvesting (LH) antennae from photosynthetic centers presents a challenge for the quantum theory. We report on a quantum chemical study of the ring of 32 BChl molecules from the bacterial core complex LH1-RC. Diagonal and off-diagonal elements of the excitonic Hamiltonian matrices are estimated in quantum chemical calculations of relevant fragments using the TD-DFT and CIS approaches. The deviation of the computed excitation energy of this BChl system from the experimental data related to the Qy band maximum of this LH1-RC complex is about 0.2 eV. We demonstrate that corrections due to improvement in modeling of an individual BChl molecule and due to contributions from the protein environment are in the range of the obtained discrepancy between theory and experiment. Differences between results of the excitonic model and direct quantum chemical calculations of BChl aggregates fall in the same range.

Quantum chemical methods for the investigation of photoinitiated processes in biological systems: theory and applications.

PubMed

Dreuw, Andreas

2006-11-13

With the advent of modern computers and advances in the development of efficient quantum chemical computer codes, the meaningful computation of large molecular systems at a quantum mechanical level became feasible. Recent experimental effort to understand photoinitiated processes in biological systems, for instance photosynthesis or vision, at a molecular level also triggered theoretical investigations in this field. In this Minireview, standard quantum chemical methods are presented that are applicable and recently used for the calculation of excited states of photoinitiated processes in biological molecular systems. These methods comprise configuration interaction singles, the complete active space self-consistent field method, and time-dependent density functional theory and its variants. Semiempirical approaches are also covered. Their basic theoretical concepts and mathematical equations are briefly outlined, and their properties and limitations are discussed. Recent successful applications of the methods to photoinitiated processes in biological systems are described and theoretical tools for the analysis of excited states are presented.

Quantum Degeneracy in Atomic Point Contacts Revealed by Chemical Force and Conductance

NASA Astrophysics Data System (ADS)

Sugimoto, Yoshiaki; Ondráček, Martin; Abe, Masayuki; Pou, Pablo; Morita, Seizo; Perez, Ruben; Flores, Fernando; Jelínek, Pavel

2013-09-01

Quantum degeneracy is an important concept in quantum mechanics with large implications to many processes in condensed matter. Here, we show the consequences of electron energy level degeneracy on the conductance and the chemical force between two bodies at the atomic scale. We propose a novel way in which a scanning probe microscope can detect the presence of degenerate states in atomic-sized contacts even at room temperature. The tunneling conductance G and chemical binding force F between two bodies both tend to decay exponentially with distance in a certain distance range, usually maintaining direct proportionality G∝F. However, we show that a square relation G∝F2 arises as a consequence of quantum degeneracy between the interacting frontier states of the scanning tip and a surface atom. We demonstrate this phenomenon on the Si(111)-(7×7) surface reconstruction where the Si adatom possesses a strongly localized dangling-bond state at the Fermi level.

Simulating the control of molecular reactions via modulated light fields: from gas phase to solution

NASA Astrophysics Data System (ADS)

Thallmair, Sebastian; Keefer, Daniel; Rott, Florian; de Vivie-Riedle, Regina

2017-04-01

Over the past few years quantum control has proven to be very successful in steering molecular processes. By combining theory with experiment, even highly complex control aims were realized in the gas phase. In this topical review, we illustrate the past achievements on several examples in the molecular context. The next step for the quantum control of chemical processes is to translate the fruitful interplay between theory and experiment to the condensed phase and thus to the regime where chemical synthesis can be supported. On the theory side, increased efforts to include solvent effects in quantum control simulations were made recently. We discuss two major concepts, namely an implicit description of the environment via the density matrix algorithm and an explicit inclusion of solvent molecules. By application to chemical reactions, both concepts conclude that despite environmental perturbations leading to more complex control tasks, efficient quantum control in the condensed phase is still feasible.

An efficient matrix product operator representation of the quantum chemical Hamiltonian

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

Keller, Sebastian, E-mail: sebastian.keller@phys.chem.ethz.ch; Reiher, Markus, E-mail: markus.reiher@phys.chem.ethz.ch; Dolfi, Michele, E-mail: dolfim@phys.ethz.ch

We describe how to efficiently construct the quantum chemical Hamiltonian operator in matrix product form. We present its implementation as a density matrix renormalization group (DMRG) algorithm for quantum chemical applications. Existing implementations of DMRG for quantum chemistry are based on the traditional formulation of the method, which was developed from the point of view of Hilbert space decimation and attained higher performance compared to straightforward implementations of matrix product based DMRG. The latter variationally optimizes a class of ansatz states known as matrix product states, where operators are correspondingly represented as matrix product operators (MPOs). The MPO construction schememore » presented here eliminates the previous performance disadvantages while retaining the additional flexibility provided by a matrix product approach, for example, the specification of expectation values becomes an input parameter. In this way, MPOs for different symmetries — abelian and non-abelian — and different relativistic and non-relativistic models may be solved by an otherwise unmodified program.« less

The First Attempt at Non-Linear in Silico Prediction of Sampling Rates for Polar Organic Chemical Integrative Samplers (POCIS)

PubMed Central

2016-01-01

Modeling and prediction of polar organic chemical integrative sampler (POCIS) sampling rates (Rs) for 73 compounds using artificial neural networks (ANNs) is presented for the first time. Two models were constructed: the first was developed ab initio using a genetic algorithm (GSD-model) to shortlist 24 descriptors covering constitutional, topological, geometrical and physicochemical properties and the second model was adapted for Rs prediction from a previous chromatographic retention model (RTD-model). Mechanistic evaluation of descriptors showed that models did not require comprehensive a priori information to predict Rs. Average predicted errors for the verification and blind test sets were 0.03 ± 0.02 L d–1 (RTD-model) and 0.03 ± 0.03 L d–1 (GSD-model) relative to experimentally determined Rs. Prediction variability in replicated models was the same or less than for measured Rs. Networks were externally validated using a measured Rs data set of six benzodiazepines. The RTD-model performed best in comparison to the GSD-model for these compounds (average absolute errors of 0.0145 ± 0.008 L d–1 and 0.0437 ± 0.02 L d–1, respectively). Improvements to generalizability of modeling approaches will be reliant on the need for standardized guidelines for Rs measurement. The use of in silico tools for Rs determination represents a more economical approach than laboratory calibrations. PMID:27363449

Lighting up micromotors with quantum dots for smart chemical sensing.

PubMed

Jurado-Sánchez, B; Escarpa, A; Wang, J

2015-09-25

A new "on-the-fly" chemical optical detection strategy based on the incorporation of fluorescence CdTe quantum dots (QDs) on the surface of self-propelled tubular micromotors is presented. The motion-accelerated binding of trace Hg to the QDs selectively quenches the fluorescence emission and leads to an effective discrimination between different mercury species and other co-existing ions.

Quantum chemical determination of young?s modulus of lignin. Calculations on ß-O-4' model compound

Treesearch

Thomas Elder

2007-01-01

The calculation of Young?s modulus of lignin has been examined by subjecting a dimeric model compound to strain, coupled with the determination of energy and stress. The computational results, derived from quantum chemical calculations, are in agreement with available experimental results. Changes in geometry indicate that modifications in dihedral angles occur in...

Chemical processing of three-dimensional graphene networks on transparent conducting electrodes for depleted-heterojunction quantum dot solar cells.

PubMed

Tavakoli, Mohammad Mahdi; Simchi, Abdolreza; Fan, Zhiyong; Aashuri, Hossein

2016-01-07

We present a novel chemical procedure to prepare three-dimensional graphene networks (3DGNs) as a transparent conductive film to enhance the photovoltaic performance of PbS quantum-dot (QD) solar cells. It is shown that 3DGN electrodes enhance electron extraction, yielding a 30% improvement in performance compared with the conventional device.

Sensitivity of ab Initio vs Empirical Methods in Computing Structural Effects on NMR Chemical Shifts for the Example of Peptides.

PubMed

Sumowski, Chris Vanessa; Hanni, Matti; Schweizer, Sabine; Ochsenfeld, Christian

2014-01-14

The structural sensitivity of NMR chemical shifts as computed by quantum chemical methods is compared to a variety of empirical approaches for the example of a prototypical peptide, the 38-residue kaliotoxin KTX comprising 573 atoms. Despite the simplicity of empirical chemical shift prediction programs, the agreement with experimental results is rather good, underlining their usefulness. However, we show in our present work that they are highly insensitive to structural changes, which renders their use for validating predicted structures questionable. In contrast, quantum chemical methods show the expected high sensitivity to structural and electronic changes. This appears to be independent of the quantum chemical approach or the inclusion of solvent effects. For the latter, explicit solvent simulations with increasing number of snapshots were performed for two conformers of an eight amino acid sequence. In conclusion, the empirical approaches neither provide the expected magnitude nor the patterns of NMR chemical shifts determined by the clearly more costly ab initio methods upon structural changes. This restricts the use of empirical prediction programs in studies where peptide and protein structures are utilized for the NMR chemical shift evaluation such as in NMR refinement processes, structural model verifications, or calculations of NMR nuclear spin relaxation rates.

A heteronuclear zero quantum coherence Nz-exchange experiment that resolves resonance overlap and its application to measure the rates of heme binding to the IsdC protein.

PubMed

Robson, Scott A; Peterson, Robert; Bouchard, Louis-S; Villareal, Valerie A; Clubb, Robert T

2010-07-21

Chemical exchange phenomena in NMR spectra can be quantitatively interpreted to measure the rates of ligand binding, as well as conformational and chemical rearrangements. In macromolecules, processes that occur slowly on the chemical shift time scale are frequently studied using 2D heteronuclear ZZ or N(z)-exchange spectroscopy. However, to successfully apply this method, peaks arising from each exchanging species must have unique chemical shifts in both dimensions, a condition that is often not satisfied in protein-ligand binding equilibria for (15)N nuclei. To overcome the problem of (15)N chemical shift degeneracy we developed a heteronuclear zero-quantum (and double-quantum) coherence N(z)-exchange experiment that resolves (15)N chemical shift degeneracy in the indirect dimension. We demonstrate the utility of this new experiment by measuring the heme binding kinetics of the IsdC protein from Staphylococcus aureus. Because of peak overlap, we could not reliably analyze binding kinetics using conventional methods. However, our new experiment resulted in six well-resolved systems that yielded interpretable data. We measured a relatively slow k(off) rate of heme from IsdC (<10 s(-1)), which we interpret as necessary so heme loaded IsdC has time to encounter downstream binding partners to which it passes the heme. The utility of using this new exchange experiment can be easily expanded to (13)C nuclei. We expect our heteronuclear zero-quantum coherence N(z)-exchange experiment will expand the usefulness of exchange spectroscopy to slow chemical exchange events that involve ligand binding.

Valley-orbit splitting in doped nanocrystalline silicon: k•p calculations

NASA Astrophysics Data System (ADS)

Belyakov, Vladimir A.; Burdov, Vladimir A.

2007-07-01

The valley-orbit splitting in silicon quantum dots with shallow donors has been theoretically studied. In particular, the chemical-shift calculation was carried out within the frames of k•p approximation for single- and many-donor cases. For both cases, the great value of the chemical shift has been obtained compared to its bulk value. Such increase of the chemical shift becomes possible due to the quantum confinement effect in a dot. It is shown for the single-donor case that the level splitting and chemical shift strongly depend on the dot radius and donor position inside the nanocrystal. In the many-donor case, the chemical shift is almost proportional to the number of donors.

Prediction of chemical biodegradability using support vector classifier optimized with differential evolution.

PubMed

Cao, Qi; Leung, K M

2014-09-22

Reliable computer models for the prediction of chemical biodegradability from molecular descriptors and fingerprints are very important for making health and environmental decisions. Coupling of the differential evolution (DE) algorithm with the support vector classifier (SVC) in order to optimize the main parameters of the classifier resulted in an improved classifier called the DE-SVC, which is introduced in this paper for use in chemical biodegradability studies. The DE-SVC was applied to predict the biodegradation of chemicals on the basis of extensive sample data sets and known structural features of molecules. Our optimization experiments showed that DE can efficiently find the proper parameters of the SVC. The resulting classifier possesses strong robustness and reliability compared with grid search, genetic algorithm, and particle swarm optimization methods. The classification experiments conducted here showed that the DE-SVC exhibits better classification performance than models previously used for such studies. It is a more effective and efficient prediction model for chemical biodegradability.

Investigation of the Redox Chemistry of Anthraquinone Derivatives Using Density Functional Theory

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

Bachman, Jonathan E.; Curtiss, Larry A.; Assary, Rajeev S.

2014-09-25

Application of density functional calculations to compute electrochemical properties such as redox windows, effect of substitution by electron donating and electron withdrawing groups on redox windows, and solvation free energies for ~50 anthraquinone (AQ) derivatives are presented because of their potential as anolytes in all-organic redox flow batteries. Computations suggest that lithium ions can increase (by ~0.4 V) the reduction potential of anthraquinone due to the lithium ion pairing by forming a Lewis base-Lewis acid complex. To design new redox active species, the substitution by electron donating groups are essential to improve the reduction window of AQ with adequate oxidativemore » stability. For instance, a complete methylation of AQ can improve its reduction window by ~0.4 V. The quantum chemical studies of the ~50 AQ derivatives are used to derive a relationship that connects the computed LUMO energy and the reduction potential that can be applied as a descriptor for screening thousands of AQ derivatives. Our computations also suggest that incorporating oxy-methyl dioxolane substituents in the AQ framework can increase its interaction with non-aqueous solvent and improve its solubility. Thermochemical calculations for likely bond breaking decomposition reactions of un-substituted AQ anions suggest that the dianions are relatively stable in the solution. These studies provide ideal platform to perform further combined experimental and theoretical studies to understand the electrochemical reversibility and solubility of new quinone molecules as energy storage materials.« less