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Sample records for activity relationships qsar

  1. Multiobjective optimization in quantitative structure-activity relationships: deriving accurate and interpretable QSARs.

    PubMed

    Nicolotti, Orazio; Gillet, Valerie J; Fleming, Peter J; Green, Darren V S

    2002-11-01

    Deriving quantitative structure-activity relationship (QSAR) models that are accurate, reliable, and easily interpretable is a difficult task. In this study, two new methods have been developed that aim to find useful QSAR models that represent an appropriate balance between model accuracy and complexity. Both methods are based on genetic programming (GP). The first method, referred to as genetic QSAR (or GPQSAR), uses a penalty function to control model complexity. GPQSAR is designed to derive a single linear model that represents an appropriate balance between the variance and the number of descriptors selected for the model. The second method, referred to as multiobjective genetic QSAR (MoQSAR), is based on multiobjective GP and represents a new way of thinking of QSAR. Specifically, QSAR is considered as a multiobjective optimization problem that comprises a number of competitive objectives. Typical objectives include model fitting, the total number of terms, and the occurrence of nonlinear terms. MoQSAR results in a family of equivalent QSAR models where each QSAR represents a different tradeoff in the objectives. A practical consideration often overlooked in QSAR studies is the need for the model to promote an understanding of the biochemical response under investigation. To accomplish this, chemically intuitive descriptors are needed but do not always give rise to statistically robust models. This problem is addressed by the addition of a further objective, called chemical desirability, that aims to reward models that consist of descriptors that are easily interpretable by chemists. GPQSAR and MoQSAR have been tested on various data sets including the Selwood data set and two different solubility data sets. The study demonstrates that the MoQSAR method is able to find models that are at least as good as models derived using standard statistical approaches and also yields models that allow a medicinal chemist to trade statistical robustness for chemical

  2. FISH ACUTE TOXICITY SYNDROMES: APPLICATION TO THE DEVELOPMENT OF MECHANISM-SPECIFIC QSARS (QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIPS)

    EPA Science Inventory

    Predictive models based on quantitative structure activity relationships (QSARs), are used as rapid screening tools to identify potentially hazardous chemicals. Several QSARs are now available that predict the acute toxicity of narcotic-industrial chemicals. Predictions for compo...

  3. 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...

  4. Developing sensor activity relationships for the JPL electronic nose sensors using molecular modeling and QSAR techniques

    NASA Technical Reports Server (NTRS)

    Shevade, A. V.; Ryan, M. A.; Homer, M. L.; Jewell, A. D.; Zhou, H.; Manatt, K.; Kisor, A. K.

    2005-01-01

    We report a Quantitative Structure-Activity Relationships (QSAR) study using Genetic Function Approximations (GFA) to describe the polymer-carbon composite sensor activities in the JPL Electronic Nose, when exposed to chemical vapors at parts-per-million concentration levels.

  5. Quantitative Structure-Activity Relationships (QSARs) - Applications and Methodology

    NASA Astrophysics Data System (ADS)

    Cronin, Mark T. D.

    The aim of this introduction is to describe briefly the applications and methodologies involved in (Q)SAR and relate these to the various chapters in this volume. This chapter gives the reader an overview of how, why and where in silico methods, including (Q)SAR, have been utilized to predict endpoints as diverse as those from pharmacology and toxicology. It provides an illustration of how all the various topics in this book interweave to form a single coherent area of science.

  6. Development of quantitative structure activity relationship (QSAR) model for disinfection byproduct (DBP) research: A review of methods and resources.

    PubMed

    Chen, Baiyang; Zhang, Tian; Bond, Tom; Gan, Yiqun

    2015-12-15

    Quantitative structure-activity relationship (QSAR) models are tools for linking chemical activities with molecular structures and compositions. Due to the concern about the proliferating number of disinfection byproducts (DBPs) in water and the associated financial and technical burden, researchers have recently begun to develop QSAR models to investigate the toxicity, formation, property, and removal of DBPs. However, there are no standard procedures or best practices regarding how to develop QSAR models, which potentially limit their wide acceptance. In order to facilitate more frequent use of QSAR models in future DBP research, this article reviews the processes required for QSAR model development, summarizes recent trends in QSAR-DBP studies, and shares some important resources for QSAR development (e.g., free databases and QSAR programs). The paper follows the four steps of QSAR model development, i.e., data collection, descriptor filtration, algorithm selection, and model validation; and finishes by highlighting several research needs. Because QSAR models may have an important role in progressing our understanding of DBP issues, it is hoped that this paper will encourage their future use for this application. PMID:26142156

  7. Quantitative structure-activity relationships (QSARs) for the transformation of organic micropollutants during oxidative water treatment.

    PubMed

    Lee, Yunho; von Gunten, Urs

    2012-12-01

    Various oxidants such as chlorine, chlorine dioxide, ferrate(VI), ozone, and hydroxyl radicals can be applied for eliminating organic micropollutant by oxidative transformation during water treatment in systems such as drinking water, wastewater, and water reuse. Over the last decades, many second-order rate constants (k) have been determined for the reaction of these oxidants with model compounds and micropollutants. Good correlations (quantitative structure-activity relationships or QSARs) are often found between the k-values for an oxidation reaction of closely related compounds (i.e. having a common organic functional group) and substituent descriptor variables such as Hammett or Taft sigma constants. In this study, we developed QSARs for the oxidation of organic and some inorganic compounds and organic micropollutants transformation during oxidative water treatment. A number of 18 QSARs were developed based on overall 412 k-values for the reaction of chlorine, chlorine dioxide, ferrate, and ozone with organic compounds containing electron-rich moieties such as phenols, anilines, olefins, and amines. On average, 303 out of 412 (74%) k-values were predicted by these QSARs within a factor of 1/3-3 compared to the measured values. For HO(·) reactions, some principles and estimation methods of k-values (e.g. the Group Contribution Method) are discussed. The developed QSARs and the Group Contribution Method could be used to predict the k-values for various emerging organic micropollutants. As a demonstration, 39 out of 45 (87%) predicted k-values were found within a factor 1/3-3 compared to the measured values for the selected emerging micropollutants. Finally, it is discussed how the uncertainty in the predicted k-values using the QSARs affects the accuracy of prediction for micropollutant elimination during oxidative water treatment. PMID:22939392

  8. Synthesis, biological activities, and quantitative structure-activity relationship (QSAR) study of novel camptothecin analogues.

    PubMed

    Wu, Dan; Zhang, Shao-Yong; Liu, Ying-Qian; Wu, Xiao-Bing; Zhu, Gao-Xiang; Zhang, Yan; Wei, Wei; Liu, Huan-Xiang; Chen, An-Liang

    2015-01-01

    In continuation of our program aimed at the development of natural product-based pesticidal agents, three series of novel camptothecin derivatives were designed, synthesized, and evaluated for their biological activities against T. Cinnabarinus, B. brassicae, and B. xylophilus. All of the derivatives showed good-to-excellent activity against three insect species tested, with LC50 values ranging from 0.00761 to 0.35496 mmol/L. Remarkably, all of the compounds were more potent than CPT against T. Cinnabarinus, and compounds 4d and 4c displayed superior activity (LC50 0.00761 mmol/L and 0.00942 mmol/L, respectively) compared with CPT (LC50 0.19719 mmol/L) against T. Cinnabarinus. Based on the observed bioactivities, preliminary structure-activity relationship (SAR) correlations were also discussed. Furthermore, a three-dimensional quantitative structure-activity relationship (3D-QSAR) model using comparative molecular field analysis (CoMFA) was built. The model gave statistically significant results with the cross-validated q2 values of 0.580 and correlation coefficient r2 of 0.991 and  of 0.993. The QSAR analysis indicated that the size of the substituents play an important in the activity of 7-modified camptothecin derivatives. These findings will pave the way for further design, structural optimization, and development of camptothecin-derived compounds as pesticidal agents. PMID:25985362

  9. Toxicity challenges in environmental chemicals: Prediction of human plasma protein binding through quantitative structure-activity relationship (QSAR) models

    EPA Science Inventory

    The present study explores the merit of utilizing available pharmaceutical data to construct a quantitative structure-activity relationship (QSAR) for prediction of the fraction of a chemical unbound to plasma protein (Fub) in environmentally relevant compounds. Independent model...

  10. 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. PMID:26451961

  11. Quantitative structure-activity relationships (QSARs) using the novel marine algal toxicity data of phenols.

    PubMed

    Ertürk, M Doğa; Saçan, Melek Türker; Novic, Marjana; Minovski, Nikola

    2012-09-01

    The present study reports for the first time in its entirety the toxicity of 30 phenolic compounds to marine alga Dunaliella tertiolecta. Toxicity of polar narcotics and respiratory uncouplers was strongly correlated to hydrophobicity as described by the logarithm of the octanol/water partition coefficient (Log P). Compounds expected to act by more reactive mechanisms, particularly hydroquinones, were shown to have toxicity in excess of that predicted by Log P. A quality quantitative structure-activity relationship (QSAR) was obtained with Log P and a 2D autocorrelation descriptor weighted by atomic polarizability (MATS3p) only after the removal of hydroquinones from the data set. In an attempt to model the whole data set including hydroquinones, 3D descriptors were included in the modeling process and three quality QSARs were developed using multiple linear regression (MLR). One of the most significant results of the present study was the superior performance of the consensus MLR model, obtained by averaging the predictions from each individual linear model, which provided excellent prediction accuracy for the test set (Q(test)²=0.94). The four-parameter Counter Propagation Artificial Neural Network (CP ANN) model, which was constructed using four out of six descriptors that appeared in the linear models, also provided an excellent external predictivity (Q(test)²=0.93). The proposed algal QSARs were further tested in their predictivity using an external set comprising toxicity data of 44 chemicals on freshwater alga Pseudokirchneriella subcapitata. The two-parameter global model employing a 3D descriptor (Mor24m) and a charge-related descriptor (C(ortho)) not only had high external predictivity (Q(ext)²=0.74), but it also had excellent external data set coverage (%97). PMID:23085159

  12. Development of acute toxicity quantitative structure activity relationships (QSAR) and their use in linear alkylbenzene sulfonate species sensitivity distributions.

    PubMed

    Belanger, Scott E; Brill, Jessica L; Rawlings, Jane M; Price, Brad B

    2016-07-01

    Linear Alkylbenzene Sulfonate (LAS) is high tonnage and widely dispersed anionic surfactant used by the consumer products sector. A range of homologous structures are used in laundry applications that differ primarily on the length of the hydrophobic alkyl chain. This research summarizes the development of a set of acute toxicity QSARs (Quantitative Structure Activity Relationships) for fathead minnows (Pimephales promelas) and daphnids (Daphnia magna, Ceriodaphnia dubia) using accepted test guideline approaches. A series of studies on pure chain length LAS from C10 to C14 were used to develop the QSARs and the robustness of the QSARs was tested by evaluation of two technical mixtures of differing compositions. All QSARs were high quality (R(2) were 0.965-0.997, p < 0.0001). Toxicity normalization employing QSARs is used to interpret a broader array of tests on LAS chain length materials to a diverse group of test organisms with the objective of developing Species Sensitivity Distributions (SSDs) for various chain lengths of interest. Mixtures include environmental distributions measured from exposure monitoring surveys of wastewater effluents, various commercial mixtures, or specific chain lengths. SSD 5th percentile hazardous concentrations (HC5s) ranged from 0.129 to 0.254 mg/L for wastewater effluents containing an average of 11.26-12 alkyl carbons. The SSDs are considered highly robust given the breadth of species (n = 19), use of most sensitive endpoints from true chronic studies and the quality of the underlying statistical properties of the SSD itself. The data continue to indicate a low hazard to the environment relative to expected environmental concentrations. PMID:27105149

  13. Quantitative Structure-Activity Relationship (QSAR) of indoloacetamides as inhibitors of human isoprenylcysteine carboxyl methyltransferase

    PubMed Central

    Leow, Jo-Lene; Baron, Rudi; Casey, Patrick C; Go, Mei-Lin

    2007-01-01

    A QSAR is developed for the isoprenylcysteine carboxyl methyltransferase (ICMT) inhibitory activities of a series of indoloacetamides (n = 71) that are structurally related to cysmethynil, a selective ICMT inhibitor. Multivariate analytical tools (principal component analysis and projection to latent structures), multi-linear regression and comparative molecular field analysis (CoMFA) are used to develop a suitably predictive model for the purpose of optimizing and identifying members with more potent inhibitory activity. The resulting model shows that good activity is determined largely by the characteristics of the substituent attached to the indole nitrogen, which should be a lipophilic residue with fairly wide dimensions. In contrast, the substituted phenyl ring attached to the indole ring must be of limited dimensions and lipophilicity. PMID:17157012

  14. Cellular Quantitative Structure–Activity Relationship (Cell-QSAR): Conceptual Dissection of Receptor Binding and Intracellular Disposition in Antifilarial Activities of Selwood Antimycins

    PubMed Central

    2012-01-01

    We present the cellular quantitative structure–activity relationship (cell-QSAR) concept that adapts ligand-based and receptor-based 3D-QSAR methods for use with cell-level activities. The unknown intracellular drug disposition is accounted for by the disposition function (DF), a model-based, nonlinear function of a drug’s lipophilicity, acidity, and other properties. We conceptually combined the DF with our multispecies, multimode version of the frequently used ligand-based comparative molecular field analysis (CoMFA) method, forming a single correlation function for fitting the cell-level activities. The resulting cell-QSAR model was applied to the Selwood data on filaricidal activities of antimycin analogues. Their molecules are flexible, ionize under physiologic conditions, form different intramolecular H-bonds for neutral and ionized species, and cross several membranes to reach unknown receptors. The calibrated cell-QSAR model is significantly more predictive than other models lacking the disposition part and provides valuable structure optimization clues by factorizing the cell-level activity of each compound into the contributions of the receptor binding and disposition. PMID:22468611

  15. Semisynthesis and quantitative structure-activity relationship (QSAR) study of some cholesterol-based hydrazone derivatives as insecticidal agents.

    PubMed

    Yang, Chun; Shao, Yonghua; Zhi, Xiaoyan; Huan, Qu; Yu, Xiang; Yao, Xiaojun; Xu, Hui

    2013-09-01

    In continuation of our program aimed at the discovery and development of natural-product-based insecticidal agents, four series of novel cholesterol-based hydrazone derivatives were synthesized, and their insecticidal activity was tested against the pre-third-instar larvae of oriental armyworm, Mythimna separata (Walker) in vivo at 1mg/mL. All the derivatives showed the better insecticidal activity than their precursor cholesterol. Quantitative structure-activity relationship (QSAR) model demonstrated that six descriptors such as RDF085v, Mor06u, Mor11u, Dv, HATS0v and H-046, are likely to influence the insecticidal activity of these compounds. Among them, two important ones are the Mor06u and RDF085v. PMID:23891182

  16. Hepatoprotection of sesquiterpenoids: a quantitative structure-activity relationship (QSAR) approach.

    PubMed

    Vinholes, Juliana; Rudnitskaya, Alisa; Gonçalves, Pedro; Martel, Fátima; Coimbra, Manuel A; Rocha, Sílvia M

    2014-03-01

    The relative hepatoprotection effect of fifteen sesquiterpenoids, commonly found in plants and plant-derived foods and beverages was assessed. Endogenous lipid peroxidation (assay A) and induced lipid peroxidation (assay B) were evaluated in liver homogenates from Wistar rats by the thiobarbituric acid reactive species test. Sesquiterpenoids with different chemical structures were tested: trans,trans-farnesol, cis-nerolidol, (-)-α-bisabolol, trans-β-farnesene, germacrene D, α-humulene, β-caryophyllene, isocaryophyllene, (+)-valencene, guaiazulene, (-)-α-cedrene, (+)-aromadendrene, (-)-α-neoclovene, (-)-α-copaene, and (+)-cyclosativene. Ascorbic acid was used as a positive antioxidant control. With the exception of α-humulene, all the sesquiterpenoids under study (1mM) were effective in reducing the malonaldehyde levels in both endogenous and induced lipid peroxidation up to 35% and 70%, respectively. The 3D-QSAR models developed, relating the hepatoprotection activity with molecular properties, showed good fit (Radj(2) 0.819 and 0.972 for the assays A and B, respectively) with good prediction power (Q(2)>0.950 and SDEP<2%, for both models A and B). A network of effects associated with structural and chemical features of sesquiterpenoids such as shape, branching, symmetry, and presence of electronegative fragments, can modulate the hepatoprotective activity observed for these compounds. PMID:24176316

  17. Estimating the persistence of organic contaminants in indirect potable reuse systems using quantitative structure activity relationship (QSAR).

    PubMed

    Lim, Seung Joo; Fox, Peter

    2012-09-01

    Predictions from the quantitative structure activity relationship (QSAR) model EPI Suite were modified to estimate the persistence of organic contaminants in indirect potable reuse systems. The modified prediction included the effects of sorption, biodegradation, and oxidation that may occur during sub-surface transport. A retardation factor was used to simulate the mobility of adsorbed compounds during sub-surface transport to a recovery well. A set of compounds with measured persistent properties during sub-surface transport was used to validate the results of the modifications to the predictions of EPI Suite. A comparison of the predicted values and measured values was done and the residual sum of the squares showed the importance of including oxidation and sorption. Sorption was the most important factor to include in predicting the fates of organic chemicals in the sub-surface environment. PMID:22766422

  18. A quantitative structure activity relationships (QSAR) analysis of triarylmethane dye tracers

    NASA Astrophysics Data System (ADS)

    Mon, Jarai; Flury, Markus; Harsh, James B.

    2006-01-01

    Dyes are important hydrological tracers. Many different dyes have been proposed as optimal tracers, but none of these dyes can be considered an ideal water tracer. Some dyes are toxic and most sorb to subsurface materials. The objective of this study was to find the molecular structure of an optimal water tracer. We used QSAR to screen a large number of hypothetical molecules, belonging to the class of triarylmethane dyes, in regard to their sorption characteristics to a sandy soil. The QSAR model was based on experimental sorption data obtained from four triarylmethane dyes: C.I. Food Blue 2 (C.I. 42090; Brilliant Blue FCF), C.I. Food Green 3 (C.I. 42053; FD&C Green No. 3), C.I. Acid Blue 7 (C.I. 42080; ORCOacid Blue A 150%), and C.I. Acid Green 9 (C.I. 42100; ORCOacid Fast Green B). Sorption characteristics of the dyes to the sandy soil were expressed with the Langmuir isotherm. Our premise was that dye sorption can be reduced by attachment of sulfonic acid (SO 3) groups to the triarylmethane template. About 70 hypothetical dyes were created and QSAR were used to estimate sorption characteristics. The results indicated that both the position and the number of SO 3 groups affected dye sorption. Sorption decreased with increasing number of SO 3 groups attached to the molecule. Increasing the number of sulfonic acid groups also decreases the toxicity of the compounds. An optimal triarylmethane water tracer contains 4 to 6 SO 3 groups.

  19. A review of quantitative structure activity relationships (QSARs) for assessing the ecotoxicity of phthalate esters

    SciTech Connect

    Parkerton, T.F.

    1995-12-31

    Dialkyl phthalate esters represent an important class of high production volume, industrial chemicals spanning a wide range of chemical properties. Over the last two decades, numerous studies have been conducted to characterize the ecotoxicity of phthalate esters. The purpose of this presentation is to provide a synthesis of the available ecotoxicity literature using a QSAR paradigm. Results from this analysis provide several important insights. First, a mechanistic explanation is provided to account for the general lack of ecotoxicity observed for higher molecular weight phthalates possessing alkyl chains of six or more carbons. Second, studies that appear as outliers are identified due to either experimental artifacts (e.g., physical effects on daphnids due to testing at concentrations exceeding water solubility) or questionable experimental methods (e.g., toxicity tests based on nominal concentrations). Lastly, differences in ecotoxicity between species appear to be due, in part, to differences in test organisms biotransformation capacities. The utility of adopting a QSAR-based approach for risk assessment will be discussed.

  20. Utilization of quantitative structure-activity relationships (QSARs) in risk assessment: Alkylphenols

    SciTech Connect

    Beck, B.D.; Toole, A.P.; Callahan, B.G.; Siddhanti, S.K. )

    1991-12-01

    Alkylphenols are a class of environmentally pervasive compounds, found both in natural (e.g., crude oils) and in anthropogenic (e.g., wood tar, coal gasification waste) materials. Despite the frequent environmental occurrence of these chemicals, there is a limited toxicity database on alkylphenols. The authors have therefore developed a 'toxicity equivalence approach' for alkylphenols which is based on their ability to inhibit, in a specific manner, the enzyme cyclooxygenase. Enzyme-inhibiting ability for individual alkylphenols can be estimated based on the quantitative structure-activity relationship developed by Dewhirst (1980) and is a function of the free hydroxyl group, electron-donating ring substituents, and hydrophobic aromatic ring substituents. The authors evaluated the toxicological significance of cyclooxygenase inhibition by comparison of the inhibitory capacity of alkylphenols with the inhibitory capacity of acetylsalicylic acid, or aspirin, a compound whose low-level effects are due to cyclooxygenase inhibition. Since nearly complete absorption for alkylphenols and aspirin is predicted, based on estimates of hydrophobicity and fraction of charged molecules at gastrointestinal pHs, risks from alkylphenols can be expressed directly in terms of 'milligram aspirin equivalence,' without correction for absorption differences. They recommend this method for assessing risks of mixtures of alkylphenols, especially for those compounds with no chronic toxicity data.38 references.

  1. Identification of phototransformation products of thalidomide and mixture toxicity assessment: an experimental and quantitative structural activity relationships (QSAR) approach.

    PubMed

    Mahmoud, Waleed M M; Toolaram, Anju P; Menz, Jakob; Leder, Christoph; Schneider, Mandy; Kümmerer, Klaus

    2014-02-01

    The fate of thalidomide (TD) was investigated after irradiation with a medium-pressure Hg-lamp. The primary elimination of TD was monitored and structures of phototransformation products (PTPs) were assessed by LC-UV-FL-MS/MS. Environmentally relevant properties of TD and its PTPs as well as hydrolysis products (HTPs) were predicted using in silico QSAR models. Mutagenicity of TD and its PTPs was investigated in the Ames microplate format (MPF) aqua assay (Xenometrix, AG). Furthermore, a modified luminescent bacteria test (kinetic luminescent bacteria test (kinetic LBT)), using the luminescent bacteria species Vibrio fischeri, was applied for the initial screening of environmental toxicity. Additionally, toxicity of phthalimide, one of the identified PTPs, was investigated separately in the kinetic LBT. The UV irradiation eliminated TD itself without complete mineralization and led to the formation of several PTPs. TD and its PTPs did not exhibit mutagenic response in the Salmonella typhimurium strains TA 98, and TA 100 with and without metabolic activation. In contrast, QSAR analysis of PTPs and HTPs provided evidence for mutagenicity, genotoxicity and carcinogenicity using additional endpoints in silico software. QSAR analysis of different ecotoxicological endpoints, such as acute toxicity towards V. fischeri, provided positive alerts for several identified PTPs and HTPs. This was partially confirmed by the results of the kinetic LBT, in which a steady increase of acute and chronic toxicity during the UV-treatment procedure was observed for the photolytic mixtures at the highest tested concentration. Moreover, the number of PTPs within the reaction mixture that might be responsible for the toxification of TD during UV-treatment was successfully narrowed down by correlating the formation kinetics of PTPs with QSAR predictions and experimental toxicity data. Beyond that, further analysis of the commercially available PTP phthalimide indicated that transformation of

  2. MOLECULAR TOPOLOGY AND NARCOSIS - A QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP (QSAR) STUDY OF ALCOHOLS USING COMPLEMENTARY INFORMATION CONTENT (CIC)

    EPA Science Inventory

    A newly formulated information -theoretic topological index - complementary information content (CIC) - defined for the planar chemical graph of molecules is applied in the QSAR studies of congeneric series of alcohols. Results show that CIC can quantitatively predict the LC50 va...

  3. DEVELOPMENT OF QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIPS (QSARS) TO PREDICT TOXICITY FOR A VARIETY OF HUMAN AND ECOLOGICAL ENDPOINTS

    EPA Science Inventory

    In general, the accuracy of a predicted toxicity value increases with increase in similarity between the query chemical and the chemicals used to develop a QSAR model. A toxicity estimation methodology employing this finding has been developed. A hierarchical based clustering t...

  4. Synthesis and quantitative structure-activity relationship (QSAR) study of novel 4-acyloxypodophyllotoxin derivatives modified in the A and C rings as insecticidal agents.

    PubMed

    He, Shuzhen; Shao, Yonghua; Fan, Lingling; Che, Zhiping; Xu, Hui; Zhi, Xiaoyan; Wang, Juanjuan; Yao, Xiaojun; Qu, Huan

    2013-01-23

    In continuation of our program aimed at the discovery and development of natural-product-based insecticidal agents, we have synthesized three series of novel 4-acyloxy compounds derived from podophyllotoxin modified in the A and C rings, which is isolated as the main secondary metabolite from the roots and rhizomes of Podophyllum hexandrum . Their insecticidal activity was preliminarily evaluated against the pre-third-instar larvae of Mythimna separata in vivo. Compound 9g displayed the best promising insecticidal activity. It revealed that cleavage of the 6,7-methylenedioxy group of podophyllotoxin will lead to a less active compound and that the C-4 position of podophyllotoxin was the important modification location. A quantitative structure-activity relationship (QSAR) model was developed by genetic algorithm combined with multiple linear regression (GA-MLR). For this model, the squared correlation coefficient (R(2)) is 0.914, the leave-one-out cross-validation correlation coefficient (Q(2)(LOO)) is 0.881, and the root-mean-square error (RMSE) is 0.024. Five descriptors, BEHm2, Mor14v, Wap, G1v, and RDF020e, are likely to influence the biological activity of these compounds. Among them, two important ones are BEHm2 and Mor14v. This study will pave the way for further design, structural modification, and development of podophyllotoxin derivatives as insecticidal agents. PMID:23278333

  5. Drug interaction study of natural steroids from herbs specifically toward human UDP-glucuronosyltransferase (UGT) 1A4 and their quantitative structure activity relationship (QSAR) analysis for prediction.

    PubMed

    Xu, Min; Dong, Peipei; Tian, Xiangge; Wang, Chao; Huo, Xiaokui; Zhang, Baojing; Wu, Lijun; Deng, Sa; Ma, Xiaochi

    2016-08-01

    The wide application of herbal medicines and foods containing steroids has resulted in the high risk of herb-drug interactions (HDIs). The present study aims to evaluate the inhibition potential of 43 natural steroids from herb medicines toward human UDP- glucuronosyltransferases (UGTs). A remarkable structure-dependent inhibition toward UGT1A4 was observed in vitro. Some natural steroids such as gitogenin, tigogenin, and solasodine were found to be the novel selective inhibitors of UGT1A4, and did not inhibit the activities of major human CYP isoforms. To clarify the possibility of the in vivo interaction of common steroids and clinical drugs, the kinetic inhibition type and related kinetic parameters (Ki) were measured. The target compounds 2-6 and 15, competitively inhibited the UGT1A4-catalyzed trifluoperazine glucuronidation reaction, with Ki values of 0.6, 0.18, 1.1, 0.7, 0.8, and 12.3μM, respectively. And this inhibition of steroids towards UGT1A4 was also verified in human primary hepatocytes. Furthermore, a quantitative structure-activity relationship (QSAR) of steroids with inhibitory effects toward human UGT1A4 isoform was established using the computational methods. Our findings elucidate the potential for in vivo HDI effects of steroids in herbal medicine and foods, with the clinical dr ugs eliminated by UGT1A4, and reveal the vital pharamcophoric requirement of natural steroids for UGT1A4 inhibition activity. PMID:27208893

  6. Quantitative structure-activity relationship (QSAR) prediction of (eco)toxicity of short aliphatic protic ionic liquids.

    PubMed

    Peric, Brezana; Sierra, Jordi; Martí, Esther; Cruañas, Robert; Garau, Maria Antonia

    2015-05-01

    Ionic liquids (ILs) are considered as a group of very promising compounds due to their excellent properties (practical non-volatility, high thermal stability and very good and diverse solving capacity). The ILs have a good prospect of replacing traditional organic solvents in vast variety of applications. However, the complete information on their environmental impact is still not available. There is also an enormous number of possible combinations of anions and cations which can form ILs, the fact that requires a method allowing the prediction of toxicity of existing and potential ILs. In this study, a group contribution QSAR model has been used in order to predict the (eco)toxicity of protic and aprotic ILs for five tests (Microtox®, Pseudokirchneriella subcapitata and Lemna minor growth inhibition test, and Acetylcholinestherase inhibition and Cell viability assay with IPC-81 cells). The predicted and experimental toxicity are well correlated. A prediction of EC50 for these (eco)toxicity tests has also been made for eight representatives of the new family of short aliphatic protic ILs, whose toxicity has not been determined experimentally to date. The QSAR model applied in this study can allow the selection of potentially less toxic ILs amongst the existing ones (e.g. in the case of aprotic ILs), but it can also be very helpful in directing the synthesis efforts toward developing new "greener" ILs respectful with the environment (e.g. short aliphatic protic ILs). PMID:25728357

  7. MIA-QSAR: a simple 2D image-based approach for quantitative structure activity relationship analysis

    NASA Astrophysics Data System (ADS)

    Freitas, Matheus P.; Brown, Steven D.; Martins, José A.

    2005-03-01

    An accessible and quite simple QSAR method, based on 2D image analysis, is reported. A case study is carried out in order to compare this model with a previously reported sophisticated methodology. A well known set of ( S)- N-[(1-ethyl-2-pyrrolidinyl)methyl]-6-methoxybenzamides, compounds with affinity to the dopamine D 2 receptor subtype, was divided in 40 calibration compounds and 18 test compounds and the descriptors were generated from pixels of 2D structures of each compound, which can be drawn with aid of any appropriate program. Bilinear (conventional) PLS was utilized as the regression method and leave-one-out cross-validation was performed using the NIPALS algorithm. The good predicted Q2 value obtained for the series of test compounds (0.58), together with the similar prediction quality obtained to other data sets (nAChR ligands, HIV protease inhibitors, COX-2 inhibitors and anxiolytic agents), suggests that the model is robust and seems to be as applicable as more complex methods.

  8. Synthesis and quantitative structure-activity relationship (QSAR) study of novel N-arylsulfonyl-3-acylindole arylcarbonyl hydrazone derivatives as nematicidal agents.

    PubMed

    Che, Zhiping; Zhang, Shaoyong; Shao, Yonghua; Fan, Lingling; Xu, Hui; Yu, Xiang; Zhi, Xiaoyan; Yao, Xiaojun; Zhang, Rui

    2013-06-19

    In continuation of our program aimed at the discovery and development of natural-product-based pesticidal agents, 54 novel N-arylsulfonyl-3-acylindole arylcarbonyl hydrazone derivatives were prepared, and their structures were well characterized by ¹H NMR, ¹³C NMR, HRMS, ESI-MS, and mp. Their nematicidal activity was evaluated against that of the pine wood nematode, Bursaphelenchus xylophilus in vivo. Among all of the derivatives, especially V-12 and V-39 displayed the best promising nematicidal activity with LC₅₀ values of 1.0969 and 1.2632 mg/L, respectively. This suggested that introduction of R¹ and R² together as the electron-withdrawing substituents, R³ as the methyl group, and R⁴ as the phenyl with the electron-donating substituents could be taken into account for further preparation of these kinds of compounds as nematicidal agents. Six selected descriptors are a WHIM descriptor (E1m), two GETAWAY descriptors (R1m+ and R3m+), a Burden eigenvalues descriptor (BEHm8), and two edge-adjacency index descriptors (EEig05x and EEig13d). Quantitative structure-activity relationship (QSAR) studies demonstrated that the structural factors, such as molecular mass (a negative correlation with the bioactivity) and molecular polarity (a positive correlation with bioactivity), are likely to govern the nematicidal activities of these compounds. For this model, the correlation coefficient (R²(training set)), the leave-one-out cross-validation correlation coefficient (Q²(LOO)), and the 7-fold cross-validation correlation coefficient (Q²(7-fold)) were 0.791, 0.701, and 0.715, respectively. The external cross-validation correlation coefficient (Q²ext) and the root-mean-square error for the test set (RMSE(test set)) were 0.774 and 3.412, respectively. This study will pave the way for future design, structural modification, and development of indole derivatives as nematicidal agents. PMID:23738496

  9. Synthesis, antimycobacterial activity evaluation, and QSAR studies of chalcone derivatives.

    PubMed

    Sivakumar, P M; Seenivasan, S Prabu; Kumar, Vanaja; Doble, Mukesh

    2007-03-15

    In order to develop relatively small molecules as antimycobacterial agents, twenty-five chalcones were synthesized, their activity was evaluated, and quantitative structure-activity relationship (QSAR) was developed. The synthesis was based on the Claisen-Schimdt scheme and the resultant compounds were tested for antitubercular activity by luciferase reporter phage (LRP) assay. Compound C(24) was found to be the most active ( approximately 99%) in this series based on the percentage reduction in Relative Light Units at both 50 and 100 microg/ml levels, followed by compound C(21). Four compounds at the 50 microg/ml and eight compounds at the 100 microg/ml showed activity above 90% level. QSAR model was developed between activity and spatial, topological, and ADME descriptors for the 50 microg/ml data. The statistical measures such as r, r(2), q(2), and F values obtained for the training set were in acceptable range and hence this relationship was used for the test set. The predictive ability of the model is satisfactory (q(2)=0.56) and it can be used for designing similar group of compounds. PMID:17276682

  10. FISH ACUTE TOXICITY SYNDROMES AND THEIR USE IN THE QSAR (QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIP) APPROACH TO HAZARD ASSESSMENT

    EPA Science Inventory

    Implementation of the Toxic Substances Control Act of 1977 creates the need to reliably establish testing priorities because laboratory resources are limited and the number of industrial chemicals requiring evaluation is overwhelming. The use of quantitative structure activity re...

  11. Synthesis, antifeedant activity against Coleoptera and 3D QSAR study of alpha-asarone derivatives.

    PubMed

    Łozowicka, B; Kaczyński, P; Magdziarz, T; Dubis, A T

    2014-01-01

    For the first time, a set of 56 compounds representing structural derivatives of naturally occurring alpha-asarone as an antifeedants against stored product pests Sitophilus granarius L., Trogoderma granarium Ev., and Tribolium confusum Duv., were subjected to the 3D QSAR studies. Three-dimensional quantitative structure-activity relationships (3D-QSAR) for 56 compounds, including 15 newly synthesized, were performed using comparative molecular field analysis s-CoMFA and SOM-CoMSA techniques. QSAR was conducted based on a combination of biological activity (against Coleoptera larvae and beetles) and various geometrical, topological, quantum-mechanical, electronic, and chromatographic descriptors. The CoMSA formalism coupled with IVE (CoMSA-IVE) allowed us to obtain highly predictive models for Trogoderma granarium Ev. larvae. We have found that this novel method indicates a clear molecular basis for activity and lipophilicity. This investigation will facilitate optimization of the design of new potential antifeedants. PMID:24601760

  12. Docking and 3D-QSAR (quantitative structure activity relationship) studies of flavones, the potent inhibitors of p-glycoprotein targeting the nucleotide binding domain.

    PubMed

    Kothandan, Gugan; Gadhe, Changdev G; Madhavan, Thirumurthy; Choi, Cheol Hee; Cho, Seung Joo

    2011-09-01

    In order to explore the interactions between flavones and P-gp, in silico methodologies such as docking and 3D-QSAR were performed. CoMFA and CoMSIA analyses were done using ligand based and receptor guided alignment schemes. Validation statistics include leave-one-out cross-validated R(2) (q(2)), internal prediction parameter by progressive scrambling (Q(*2)), external prediction with test set. They show that models derived from this study are quite robust. Ligand based CoMFA (q(2) = 0.747, Q(*2) = 0.639, r(pred)(2)=0.802) and CoMSIA model (q(2) = 0.810, Q(*2) = 0.676, r(pred)(2)=0.785) were developed using atom by atom matching. Receptor guided CoMFA (q(2) = 0.712, Q(*2) = 0.497, r(pred)(2) = 0.841) and for CoMSIA (q(2) = 0.805, Q(*2) = 0.589, r(pred)(2) = 0.937) models were developed by docking of highly active flavone into the proposed NBD (nucleotide binding domain) of P-gp. The 3D-QSAR models generated here predicted that hydrophobic and steric parameters are important for activity toward P-gp. Our studies indicate the important amino acid in NBD crucial for binding in accordance with the previous results. This site forms a hydrophobic site. Since flavonoids have potential without toxicity, we propose to inspect this hydrophobic site including Asn1043 and Asp1049 should be considered for future inhibitor design. PMID:21723648

  13. Virulence Factor-activity Relationships: Workshop Summary

    EPA Science Inventory

    The concept or notion of virulence factor–activity relationships (VFAR) is an approach for identifying an analogous process to the use of qualitative structure–activity relationships (QSAR) for identifying new microbial contaminants. In QSAR, it is hypothesized that, for new chem...

  14. Adaptive Neuro-Fuzzy Inference System Applied QSAR with Quantum Chemical Descriptors for Predicting Radical Scavenging Activities of Carotenoids.

    PubMed

    Jhin, Changho; Hwang, Keum Taek

    2015-01-01

    One of the physiological characteristics of carotenoids is their radical scavenging activity. In this study, the relationship between radical scavenging activities and quantum chemical descriptors of carotenoids was determined. Adaptive neuro-fuzzy inference system (ANFIS) applied quantitative structure-activity relationship models (QSAR) were also developed for predicting and comparing radical scavenging activities of carotenoids. Semi-empirical PM6 and PM7 quantum chemical calculations were done by MOPAC. Ionisation energies of neutral and monovalent cationic carotenoids and the product of chemical potentials of neutral and monovalent cationic carotenoids were significantly correlated with the radical scavenging activities, and consequently these descriptors were used as independent variables for the QSAR study. The ANFIS applied QSAR models were developed with two triangular-shaped input membership functions made for each of the independent variables and optimised by a backpropagation method. High prediction efficiencies were achieved by the ANFIS applied QSAR. The R-square values of the developed QSAR models with the variables calculated by PM6 and PM7 methods were 0.921 and 0.902, respectively. The results of this study demonstrated reliabilities of the selected quantum chemical descriptors and the significance of QSAR models. PMID:26474167

  15. Adaptive Neuro-Fuzzy Inference System Applied QSAR with Quantum Chemical Descriptors for Predicting Radical Scavenging Activities of Carotenoids

    PubMed Central

    Jhin, Changho; Hwang, Keum Taek

    2015-01-01

    One of the physiological characteristics of carotenoids is their radical scavenging activity. In this study, the relationship between radical scavenging activities and quantum chemical descriptors of carotenoids was determined. Adaptive neuro-fuzzy inference system (ANFIS) applied quantitative structure-activity relationship models (QSAR) were also developed for predicting and comparing radical scavenging activities of carotenoids. Semi-empirical PM6 and PM7 quantum chemical calculations were done by MOPAC. Ionisation energies of neutral and monovalent cationic carotenoids and the product of chemical potentials of neutral and monovalent cationic carotenoids were significantly correlated with the radical scavenging activities, and consequently these descriptors were used as independent variables for the QSAR study. The ANFIS applied QSAR models were developed with two triangular-shaped input membership functions made for each of the independent variables and optimised by a backpropagation method. High prediction efficiencies were achieved by the ANFIS applied QSAR. The R-square values of the developed QSAR models with the variables calculated by PM6 and PM7 methods were 0.921 and 0.902, respectively. The results of this study demonstrated reliabilities of the selected quantum chemical descriptors and the significance of QSAR models. PMID:26474167

  16. Combinatorial QSAR Modeling of Rat Acute Toxicity by Oral Exposure

    EPA Science Inventory

    Quantitative Structure-Activity Relationship (QSAR) toxicity models have become popular tools for identifying potential toxic compounds and prioritizing candidates for animal toxicity tests. However, few QSAR studies have successfully modeled large, diverse mammalian toxicity end...

  17. Antibacterial Activity of Imidazolium-Based Ionic Liquids Investigated by QSAR Modeling and Experimental Studies.

    PubMed

    Hodyna, Diana; Kovalishyn, Vasyl; Rogalsky, Sergiy; Blagodatnyi, Volodymyr; Petko, Kirill; Metelytsia, Larisa

    2016-09-01

    Predictive QSAR models for the inhibitors of B. subtilis and Ps. aeruginosa among imidazolium-based ionic liquids were developed using literary data. The regression QSAR models were created through Artificial Neural Network and k-nearest neighbor procedures. The classification QSAR models were constructed using WEKA-RF (random forest) method. The predictive ability of the models was tested by fivefold cross-validation; giving q(2) = 0.77-0.92 for regression models and accuracy 83-88% for classification models. Twenty synthesized samples of 1,3-dialkylimidazolium ionic liquids with predictive value of activity level of antimicrobial potential were evaluated. For all asymmetric 1,3-dialkylimidazolium ionic liquids, only compounds containing at least one radical with alkyl chain length of 12 carbon atoms showed high antibacterial activity. However, the activity of symmetric 1,3-dialkylimidazolium salts was found to have opposite relationship with the length of aliphatic radical being maximum for compounds based on 1,3-dioctylimidazolium cation. The obtained experimental results suggested that the application of classification QSAR models is more accurate for the prediction of activity of new imidazolium-based ILs as potential antibacterials. PMID:27086199

  18. DFT-based QSAR models to predict the antimycobacterial activity of chalcones.

    PubMed

    Barua, Nilakshi; Sarmah, Pubalee; Hussain, Iftikar; Deka, Ramesh C; Buragohain, Alak K

    2012-04-01

    In this study, antimycobacterial activity of a set of synthesized chalcone derivatives against Mycobacterium tuberculosis H37Rv was investigated by quantitative structure-activity relationship (QSAR) analysis using density functional theory (DFT) and molecular mechanics (MM+)-based descriptors in both gas and solvent phases. The best molecular descriptors identified were hardness, E(HOMO) , MR(A-4) and MR(B-4') that contributed to the antimycobacterial activity of the chalcones as independent factors. The correlation of these four descriptors with their antimycobacterial activity increases with the inclusion of solvent medium, indicating their importance in studying biological activity. QSAR models revealed that in gas phase, lower values of E(HOMO) , MR(A-4) and MR(B-4') increase the antimycobacterial activity of the chalcone molecules. However, in solvent phase, lower values of E(HOMO) and MR(B-4') and higher values of MR(A-4) increase their activity. PMID:22151277

  19. The analysis of structure-anticancer and antiviral activity relationships for macrocyclic pyridinophanes and their analogues on the basis of 4D QSAR models (simplex representation of molecular structure).

    PubMed

    Kuz'min, Victor E; Artemenko, Anatoly G; Lozitsky, Victor P; Muratov, Eugene N; Fedtchouk, Alla S; Dyachenko, Natalia S; Nosach, Lidiya N; Gridina, Tatiyana L; Shitikova, Larisa I; Mudrik, Liubov M; Mescheriakov, Aleksey K; Chelombitko, Vladislav A; Zheltvay, Andrey I; Vanden Eynde, Jean-Jaques

    2002-01-01

    A new 4D-QSAR approach has been considered. For all investigated molecules the 3D structural models have been created and the set of conformers (fourth dimension) have been used. Each conformer is represented as a system of different simplexes (tetratomic fragments of fixed structure, chirality and symmetry). The investigation of influence of molecular structure of macrocyclic pyridinophanes, their analogues and certain other compounds on anticancer and antiviral (anti-influenza, antiherpes and antiadenovirus) activity has been carried out by means of the 4D-QSAR. Statistic characteristics for QSAR of PLS (partial least squares) models are satisfactory (R = 0.92-0.97; CVR = 0.63-0.83). Molecular fragments increasing and decreasing biological activity were defined. This information may be useful for design, and direct synthesis of novel anticancer and antiviral agents. PMID:12136936

  20. Quantitative Structure--Activity Relationship Modeling of Rat Acute Toxicity by Oral Exposure

    EPA Science Inventory

    Background: Few Quantitative Structure-Activity Relationship (QSAR) studies have successfully modeled large, diverse rodent toxicity endpoints. Objective: In this study, a combinatorial QSAR approach has been employed for the creation of robust and predictive models of acute toxi...

  1. Daphnia and fish toxicity of (benzo)triazoles: validated QSAR models, and interspecies quantitative activity-activity modelling.

    PubMed

    Cassani, Stefano; Kovarich, Simona; Papa, Ester; Roy, Partha Pratim; van der Wal, Leon; Gramatica, Paola

    2013-08-15

    Due to their chemical properties synthetic triazoles and benzo-triazoles ((B)TAZs) are mainly distributed to the water compartments in the environment, and because of their wide use the potential effects on aquatic organisms are cause of concern. Non testing approaches like those based on quantitative structure-activity relationships (QSARs) are valuable tools to maximize the information contained in existing experimental data and predict missing information while minimizing animal testing. In the present study, externally validated QSAR models for the prediction of acute (B)TAZs toxicity in Daphnia magna and Oncorhynchus mykiss have been developed according to the principles for the validation of QSARs and their acceptability for regulatory purposes, proposed by the Organization for Economic Co-operation and Development (OECD). These models are based on theoretical molecular descriptors, and are statistically robust, externally predictive and characterized by a verifiable structural applicability domain. They have been applied to predict acute toxicity for over 300 (B)TAZs without experimental data, many of which are in the pre-registration list of the REACH regulation. Additionally, a model based on quantitative activity-activity relationships (QAAR) has been developed, which allows for interspecies extrapolation from daphnids to fish. The importance of QSAR/QAAR, especially when dealing with specific chemical classes like (B)TAZs, for screening and prioritization of pollutants under REACH, has been highlighted. PMID:23702385

  2. QSAR study of substituted 2-pyridinyl guanidines as selective urokinase-type plasminogen activator (uPA) inhibitors.

    PubMed

    Karthikeyan, C; Moorthy, N S Hari Narayana; Trivedi, Piyush

    2009-02-01

    A quantitative structure-activity relationship analysis was conducted on two different series of pyridinylguanidines acting as inhibitors of urokinase-type plasminogen activator using QuaSAR descriptors of molecular modeling software MOE. Multiple linear regression analysis following a stepwise scheme was employed to generate QSARs that relate molecular descriptors to uPA inhibitory activity data of the title compounds. Among the several QSARs generated by MLR analysis, the best models were selected on the basis of their statistical significance and predictive potential. The interpretation of the selected QSAR models suggest that uPA inhibitory activity of compounds in series 1 is influenced by their molecular shape, molecular flexibility and halogen atoms in the molecule whereas the uPA inhibitory potency of compounds in series 2 is dependent on molecular lipophilicity, number of double bonds and spatial orientation of bulky substituents in the molecule. PMID:19012070

  3. Ligand Biological Activity Predictions Using Fingerprint-Based Artificial Neural Networks (FANN-QSAR)

    PubMed Central

    Myint, Kyaw Z.; Xie, Xiang-Qun

    2015-01-01

    This chapter focuses on the fingerprint-based artificial neural networks QSAR (FANN-QSAR) approach to predict biological activities of structurally diverse compounds. Three types of fingerprints, namely ECFP6, FP2, and MACCS, were used as inputs to train the FANN-QSAR models. The results were benchmarked against known 2D and 3D QSAR methods, and the derived models were used to predict cannabinoid (CB) ligand binding activities as a case study. In addition, the FANN-QSAR model was used as a virtual screening tool to search a large NCI compound database for lead cannabinoid compounds. We discovered several compounds with good CB2 binding affinities ranging from 6.70 nM to 3.75 μM. The studies proved that the FANN-QSAR method is a useful approach to predict bioactivities or properties of ligands and to find novel lead compounds for drug discovery research. PMID:25502380

  4. QSAR studies of macrocyclic diterpenes with P-glycoprotein inhibitory activity.

    PubMed

    Sousa, Inês J; Ferreira, Maria-José U; Molnár, Joseph; Fernandes, Miguel X

    2013-02-14

    Multidrug resistance (MDR) represents a major limitation for cancer chemotherapy. There are several mechanisms of MDR but the most important is associated with P-glycoprotein (P-gp) overexpression. The development of modulators of P-gp that are able to re-establish drug sensitivity of resistant cells has been considered a promising approach for overcoming MDR. Macrocyclic lathyrane and jatrophane-type diterpenes from Euphorbia species were found to be strong MDR reversing agents. In this study we applied quantitative structure-activity relationship (QSAR) methodology in order to identify the most relevant molecular features of macrocyclic diterpenes with P-gp inhibitory activity and to determine which structural modifications can be performed to improve their activity. Using experimental biological data at two concentrations (4 and 40 μg/ml), we developed a QSAR model for a set of 51 bioactive diterpenic compounds which includes lathyrane and jatrophane-type diterpenes and another model just for jatrophanes. The cross-validation correlation values for all diterpenes QSAR models developed for biological activities at compound concentrations of 4 and 40 μg/ml were 0.758 and 0.729, respectively. Regarding the prediction ability, we get R²(pred) values of 0.765 and 0.534 for biological activities at compound concentrations of 4 and 40 μg/ml, respectively. Applying the cross-validation test to jatrophanes QSAR models, we obtained 0.680 and 0.787 for biological activities at compound concentrations of 4 and 40 μg/ml concentrations, respectively. For the same concentrations, the obtained R²(pred) values for jatrophanes models were 0.541 and 0.534, respectively. The obtained models were statistically valid and showed high prediction ability. PMID:23228414

  5. QSAR-Driven Discovery of Novel Chemical Scaffolds Active against Schistosoma mansoni.

    PubMed

    Melo-Filho, Cleber C; Dantas, Rafael F; Braga, Rodolpho C; Neves, Bruno J; Senger, Mario R; Valente, Walter C G; Rezende-Neto, João M; Chaves, Willian T; Muratov, Eugene N; Paveley, Ross A; Furnham, Nicholas; Kamentsky, Lee; Carpenter, Anne E; Silva-Junior, Floriano P; Andrade, Carolina H

    2016-07-25

    Schistosomiasis is a neglected tropical disease that affects millions of people worldwide. Thioredoxin glutathione reductase of Schistosoma mansoni (SmTGR) is a validated drug target that plays a crucial role in the redox homeostasis of the parasite. We report the discovery of new chemical scaffolds against S. mansoni using a combi-QSAR approach followed by virtual screening of a commercial database and confirmation of top ranking compounds by in vitro experimental evaluation with automated imaging of schistosomula and adult worms. We constructed 2D and 3D quantitative structure-activity relationship (QSAR) models using a series of oxadiazoles-2-oxides reported in the literature as SmTGR inhibitors and combined the best models in a consensus QSAR model. This model was used for a virtual screening of Hit2Lead set of ChemBridge database and allowed the identification of ten new potential SmTGR inhibitors. Further experimental testing on both shistosomula and adult worms showed that 4-nitro-3,5-bis(1-nitro-1H-pyrazol-4-yl)-1H-pyrazole (LabMol-17) and 3-nitro-4-{[(4-nitro-1,2,5-oxadiazol-3-yl)oxy]methyl}-1,2,5-oxadiazole (LabMol-19), two compounds representing new chemical scaffolds, have high activity in both systems. These compounds will be the subjects for additional testing and, if necessary, modification to serve as new schistosomicidal agents. PMID:27253773

  6. Determinants of Activity at Human Toll-like Receptors 7 and 8: Quantitative Structure–Activity Relationship (QSAR) of Diverse Heterocyclic Scaffolds

    PubMed Central

    2015-01-01

    Toll-like receptor (TLR) 7 and 8 agonists are potential vaccine adjuvants, since they directly activate APCs and enhance Th1-driven immune responses. Previous SAR investigations in several scaffolds of small molecule TLR7/8 activators pointed to the strict dependence of the selectivity for TLR7 vis-à-vis TLR8 on the electronic configurations of the heterocyclic systems, which we sought to examine quantitatively with the goal of developing “heuristics” to define structural requisites governing activity at TLR7 and/or TLR8. We undertook a scaffold-hopping approach, entailing the syntheses and biological evaluations of 13 different chemotypes. Crystal structures of TLR8 in complex with the two most active compounds confirmed important binding interactions playing a key role in ligand occupancy and biological activity. Density functional theory based quantum chemical calculations on these compounds followed by linear discriminant analyses permitted the classification of inactive, TLR8-active, and TLR7/8 dual-active compounds, confirming the critical role of partial charges in determining biological activity. PMID:25192394

  7. Predicting activities without computing descriptors: graph machines for QSAR.

    PubMed

    Goulon, A; Picot, T; Duprat, A; Dreyfus, G

    2007-01-01

    We describe graph machines, an alternative approach to traditional machine-learning-based QSAR, which circumvents the problem of designing, computing and selecting molecular descriptors. In that approach, which is similar in spirit to recursive networks, molecules are considered as structured data, represented as graphs. For each example of the data set, a mathematical function (graph machine) is built, whose structure reflects the structure of the molecule under consideration; it is the combination of identical parameterised functions, called "node functions" (e.g. a feedforward neural network). The parameters of the node functions, shared both within and across the graph machines, are adjusted during training with the "shared weights" technique. Model selection is then performed by traditional cross-validation. Therefore, the designer's main task consists in finding the optimal complexity for the node function. The efficiency of this new approach has been demonstrated in many QSAR or QSPR tasks, as well as in modelling the activities of complex chemicals (e.g. the toxicity of a family of phenols or the anti-HIV activities of HEPT derivatives). It generally outperforms traditional techniques without requiring the selection and computation of descriptors. PMID:17365965

  8. QSAR study of anti-prion activity of 2-aminothiazoles

    PubMed Central

    Mandi, Prasit; Nantasenamat, Chanin; Srungboonmee, Kakanand; Isarankura-Na-Ayudhya, Chartchalerm; Prachayasittikul, Virapong

    2012-01-01

    2-aminothiazoles is a class of compounds capable of treating life-threatening prion diseases. QSAR studies on a set of forty-seven 2-aminothiazole derivatives possessing anti-prion activity were performed using multivariate analysis, which comprised of multiple linear regression (MLR), artificial neural network (ANN) and support vector machine (SVM). The results indicated that MLR afforded reasonable performance with a correlation coefficient (r) and root mean squared error (RMSE) of 0.9073 and 0.2977, respectively, as obtained from leave-one-out cross-validation (LOO-CV). More sophisticated learning methods such as SVM provided models with the highest accuracy with r and RMSE of 0.9471 and 0.2264, respectively, while ANN gave reasonable performance with r and RMSE of 0.9023 and 0.3043, respectively, as obtained LOO-CV calculations. Descriptor analysis from the regression coefficients of the MLR model suggested that compounds should be asymmetrical molecule with low propensity to form hydrogen bonds and high frequency of N content at topological distance 02 in order to provide good activities. Insights from QSAR studies is anticipated to be useful in the design of novel derivatives based on the 2-aminothiazole scaffold as potent therapeutic agents against prion diseases. PMID:27418919

  9. QSAR and Docking Studies on Capsazepine Derivatives for Immunomodulatory and Anti-Inflammatory Activity

    PubMed Central

    Shukla, Aparna; Sharma, Pooja; Prakash, Om; Singh, Monika; Kalani, Komal; Khan, Feroz; Bawankule, Dnyaneshwar Umrao; Luqman, Suaib; Srivastava, Santosh Kumar

    2014-01-01

    Capsazepine, an antagonist of capsaicin, is discovered by the structure and activity relationship. In previous studies it has been found that capsazepine has potency for immunomodulation and anti-inflammatory activity and emerging as a favourable target in quest for efficacious and safe anti-inflammatory drug. Thus, a 2D quantitative structural activity relationship (QSAR) model against target tumor necrosis factor-α (TNF-α) was developed using multiple linear regression method (MLR) with good internal prediction (r2 = 0.8779) and external prediction (r2pred = 0.5865) using Discovery Studio v3.5 (Accelrys, USA). The predicted activity was further validated by in vitro experiment. Capsazepine was tested in lipopolysaccharide (LPS) induced inflammation in peritoneal mouse macrophages. Anti-inflammatory profile of capsazepine was assessed by its potency to inhibit the production of inflammatory mediator TNF-α. The in vitro experiment indicated that capsazepine is an efficient anti-inflammatory agent. Since, the developed QSAR model showed significant correlations between chemical structure and anti-inflammatory activity, it was successfully applied in the screening of forty-four virtual derivatives of capsazepine, which finally afforded six potent derivatives, CPZ-29, CPZ-30, CPZ-33, CPZ-34, CPZ-35 and CPZ-36. To gain more insights into the molecular mechanism of action of capsazepine and its derivatives, molecular docking and in silico absorption, distribution, metabolism, excretion and toxicity (ADMET) studies were performed. The results of QSAR, molecular docking, in silico ADMET screening and in vitro experimental studies provide guideline and mechanistic scope for the identification of more potent anti-inflammatory & immunomodulatory drug. PMID:25003344

  10. Comparison of Different 2D and 3D-QSAR Methods on Activity Prediction of Histamine H3 Receptor Antagonists.

    PubMed

    Dastmalchi, Siavoush; Hamzeh-Mivehroud, Maryam; Asadpour-Zeynali, Karim

    2012-01-01

    Histamine H3 receptor subtype has been the target of several recent drug development programs. Quantitative structure-activity relationship (QSAR) methods are used to predict the pharmaceutically relevant properties of drug candidates whenever it is applicable. The aim of this study was to compare the predictive powers of three different QSAR techniques, namely, multiple linear regression (MLR), artificial neural network (ANN), and HASL as a 3D QSAR method, in predicting the receptor binding affinities of arylbenzofuran histamine H3 receptor antagonists. Genetic algorithm coupled partial least square as well as stepwise multiple regression methods were used to select a number of calculated molecular descriptors to be used in MLR and ANN-based QSAR studies. Using the leave-group-out cross-validation technique, the performances of the MLR and ANN methods were evaluated. The calculated values for the mean absolute percentage error (MAPE), ranging from 2.9 to 3.6, and standard deviation of error of prediction (SDEP), ranging from 0.31 to 0.36, for both MLR and ANN methods were statistically comparable, indicating that both methods perform equally well in predicting the binding affinities of the studied compounds toward the H3 receptors. On the other hand, the results from 3D-QSAR studies using HASL method were not as good as those obtained by 2D methods. It can be concluded that simple traditional approaches such as MLR method can be as reliable as those of more advanced and sophisticated methods like ANN and 3D-QSAR analyses. PMID:25317190

  11. QSAR for cholinesterase inhibition by organophosphorus esters and CNDO/2 calculations for organophosphorus ester hydrolysis. [quantitative structure-activity relationship, complete neglect of differential overlap

    NASA Technical Reports Server (NTRS)

    Johnson, H.; Kenley, R. A.; Rynard, C.; Golub, M. A.

    1985-01-01

    Quantitative structure-activity relationships were derived for acetyl- and butyrylcholinesterase inhibition by various organophosphorus esters. Bimolecular inhibition rate constants correlate well with hydrophobic substituent constants, and with the presence or absence of cationic groups on the inhibitor, but not with steric substituent constants. CNDO/2 calculations were performed on a separate set of organophosphorus esters, RR-primeP(O)X, where R and R-prime are alkyl and/or alkoxy groups and X is fluorine, chlorine or a phenoxy group. For each subset with the same X, the CNDO-derived net atomic charge at the central phosphorus atom in the ester correlates well with the alkaline hydrolysis rate constant. For the whole set of esters with different X, two equations were derived that relate either charge and leaving group steric bulk, or orbital energy and bond order to the hydrolysis rate constant.

  12. Molecular Fingerprint-based Artificial Neural Networks QSAR for Ligand Biological Activity Predictions

    PubMed Central

    Myint, Kyaw-Zeyar; Wang, Lirong; Tong, Qin; Xie, Xiang-Qun

    2012-01-01

    In this manuscript, we have reported a novel 2D fingerprint-based artificial neural network QSAR (FANN-QSAR) method in order to effectively predict biological activities of structurally diverse chemical ligands. Three different types of fingerprints, namely ECFP6, FP2 and MACCS, were used in FANN-QSAR algorithm development, and FANN-QSAR models were compared to known 3D and 2D QSAR methods using five data sets previously reported. In addition, the derived models were used to predict GPCR cannabinoid ligand binding affinities using our manually curated cannabinoid ligand database containing 1699 structurally diverse compounds with reported cannabinoid receptor subtype CB2 activities. To demonstrate its useful applications, the established FANN-QSAR algorithm was used as a virtual screening tool to search a large NCI compound database for lead cannabinoid compounds and we have discovered several compounds with good CB2 binding affinities ranging from 6.70 nM to 3.75 μM. To the best of our knowledge, this is the first report for a fingerprint-based neural network approach validated with a successful virtual screening application in identifying lead compounds. The studies proved that the FANN-QSAR method is a useful approach to predict bioactivities or properties of ligands and to find novel lead compounds for drug discovery research. PMID:22937990

  13. Antibacterial activity and QSAR of chalcones against biofilm-producing bacteria isolated from marine waters.

    PubMed

    Sivakumar, P M; Prabhawathi, V; Doble, M

    2010-04-01

    Biofouling in the marine environment is a major problem. In this study, three marine organisms, namely Bacillus flexus (LD1), Pseudomonas fluorescens (MD3) and Vibrio natriegens (MD6), were isolated from biofilms formed on polymer and metal surfaces immersed in ocean water. Phylogenetic analysis of these three organisms indicated that they were good model systems for studying marine biofouling. The in vitro antifouling activity of 47 synthesized chalcone derivatives was investigated by estimating the minimum inhibitory concentration against these organisms using a twofold dilution technique. Compounds C-5, C-16, C-24, C-33, C-34 and C-37 were found to be the most active. In the majority of the cases it was found that these active compounds had hydroxyl substitutions. A quantitative structure-activity relationship (QSAR) was developed after dividing the total data into training and test sets. The statistical measures r(2), [image omitted] (>0.6) q(2) (>0.5) and the F-ratio were found to be satisfactory. Spatial, structural and electronic descriptors were found to be predominantly affecting the antibiofouling activity of these compounds. Among the spatial descriptors, Jurs descriptors showed their contribution in all the three antibacterial QSARs. PMID:20544550

  14. Use of QSARs in international decision-making frameworks to predict health effects of chemical substances.

    PubMed Central

    Cronin, Mark T D; Jaworska, Joanna S; Walker, John D; Comber, Michael H I; Watts, Christopher D; Worth, Andrew P

    2003-01-01

    This article is a review of the use of quantitative (and qualitative) structure-activity relationships (QSARs and SARs) by regulatory agencies and authorities to predict acute toxicity, mutagenicity, carcinogenicity, and other health effects. A number of SAR and QSAR applications, by regulatory agencies and authorities, are reviewed. These include the use of simple QSAR analyses, as well as the use of multivariate QSARs, and a number of different expert system approaches. PMID:12896862

  15. QSAR Study of p56lck Protein Tyrosine Kinase Inhibitory Activity of Flavonoid Derivatives Using MLR and GA-PLS

    PubMed Central

    Fassihi, Afshin; Sabet, Razieh

    2008-01-01

    Quantitative relationships between molecular structure and p56lck protein tyrosine kinase inhibitory activity of 50 flavonoid derivatives are discovered by MLR and GA-PLS methods. Different QSAR models revealed that substituent electronic descriptors (SED) parameters have significant impact on protein tyrosine kinase inhibitory activity of the compounds. Between the two statistical methods employed, GA-PLS gave superior results. The resultant GA-PLS model had a high statistical quality (R2 = 0.74 and Q2 = 0.61) for predicting the activity of the inhibitors. The models proposed in the present work are more useful in describing QSAR of flavonoid derivatives as p56lck protein tyrosine kinase inhibitors than those provided previously. PMID:19325836

  16. A QSAR study of radical scavenging antioxidant activity of a series of flavonoids using DFT based quantum chemical descriptors--the importance of group frontier electron density.

    PubMed

    Sarkar, Ananda; Middya, Tapas Ranjan; Jana, Atish Dipnakar

    2012-06-01

    In a pursuit of electronic level understanding of the antioxidant activity of a series of flavonoids, quantitative structure-activity relationship (QSAR) studies have been carried out using density functional theory (DFT) based quantum chemical descriptors. The best QSAR model have been selected for which the computed square correlation coefficient r(2) = 0.937 and cross-validated squared correlation coefficient q(2) =0.916. The QSAR model indicates that hardness (η), group electrophilic frontier electron density (F(E)(A)) and group philicity (ω(B)(+)) of individual molecules are responsible for in vitro biological activity. To the best our knowledge, the group electrophilic frontier electron density (F(E)(A)) has been used for the first time to explain the radical scavenging activity (RSA) of flavonoids. The excellent correlation between the RSA and the above mentioned DFT based descriptors lead us to predict new antioxidants having very good antioxidant activity. PMID:22080306

  17. Multi-Target QSAR Approaches for Modeling Protein Inhibitors. Simultaneous Prediction of Activities Against Biomacromolecules Present in Gram-Negative Bacteria.

    PubMed

    Speck-Planche, Alejandro; Cordeiro, M N D S

    2015-01-01

    Drug discovery is aimed at finding therapeutic agents for the treatment of many diverse diseases and infections. However, this is a very slow an expensive process, and for this reason, in silico approaches are needed to rationalize the search for new molecular entities with desired biological profiles. Models focused on quantitative structure-activity relationships (QSAR) have constituted useful complementary tools in medicinal chemistry, allowing the virtual predictions of dissimilar pharmacological activities of compounds. In the last 10 years, multi-target (mt) QSAR models have been reported, representing great advances with respect to those models generated from classical approaches. Thus, mt- QSAR models can simultaneously predict activities against different biological targets (proteins, microorganisms, cell lines, etc.) by using large and heterogeneous datasets of chemicals. The present review is devoted to discuss the most promising mt-QSAR models, particularly those developed for the prediction of protein inhibitors. We also report the first multi-tasking QSAR (mtk-QSAR) model for simultaneous prediction of inhibitors against biomacromolecules (specifically proteins) present in Gram-negative bacteria. This model allowed us to consider both different proteins and multiple experimental conditions under which the inhibitory activities of the chemicals were determined. The mtk-QSAR model exhibited accuracies higher than 98% in both training and prediction sets, also displaying a very good performance in the classification of active and inactive cases that depended on the specific elements of the experimental conditions. The physicochemical interpretations of the molecular descriptors were also analyzed, providing important insights regarding the molecular patterns associated with the appearance/enhancement of the inhibitory potency. PMID:25961517

  18. Quantitative structure-activity relationship (QSAR) analysis of surfactants influencing attachment of a Mycobacterium sp. to cellulose acetate and aromatic polyamide reverse osmosis membranes.

    PubMed

    Campbell, P; Srinivasan, R; Knoell, T; Phipps, D; Ishida, K; Safarik, J; Cormack, T; Ridgway, H

    1999-09-01

    A series of 23 neutral, anionic, and zwitterionic surfactants were tested at a concentration of 0.1% wt/vol for their influence on attachment of a Mycobacterium sp. to cellulose acetate (CA) and polyamide (PA) reverse osmosis (RO) membranes. Four cell attachment bioassays were used: (1) semiconcurrent addition of surfactant and bacteria to RO coupons (standard assay); (2) surfactant pretreatment of RO membranes (membrane pretreatment assay); (3) surfactant treatment of adsorbed cells (detachment assay); and (4) surfactant pretreatment of mycobacteria (cell pretreatment assay). Seventeen surfactants inhibited attachment to PA membranes, whereas 15 inhibited attachment to CA in standard assays and, in 13 cases, the same surfactant inhibited attachment to both PA and CA. Despite greater cell attachment to PA than CA, surfactants were typically more effective in the former membrane system. More surfactants were effective in impairing cell attachment than in promoting detachment and a number enhanced attachment in membrane pretreatment assays, suggesting surface modification of RO membranes. Cell pretreatment inhibited attachment to CA membranes, suggesting the bacterial surface was also a target for detergent activity. Multivariate regression and cluster analyses indicated that critical micellar concentration (CMC) was positively correlated with Mycobacterium attachment in CA and PA standard assays. Surfactant dipole moment and octanol/water partitioning (LogP) also contributed to detergent activity in the PA system, whereas dipole moment, molecular topology (i.e., connectivity indices), and charge properties influenced activity in the CA system. Influential variables in membrane pretreatment assays included the LogP, topology indices, and charge properties, whereas CMC played a diminished role. Surfactant dipole moment was most influential in CA membrane detachment assays. Increasing system ionic strength by LiBr addition strengthened inhibition of cell attachment to

  19. Probing the origins of aromatase inhibitory activity of disubstituted coumarins via QSAR and molecular docking

    PubMed Central

    Worachartcheewan, Apilak; Suvannang, Naravut; Prachayasittikul, Supaluk; Prachayasittikul, Virapong; Nantasenamat, Chanin

    2014-01-01

    This study investigated the quantitative structure-activity relationship (QSAR) of imidazole derivatives of 4,7-disubstituted coumarins as inhibitors of aromatase, a potential therapeutic protein target for the treatment of breast cancer. Herein, a series of 3,7- and 4,7-disubstituted coumarin derivatives (1-34) with R1 and R2 substituents bearing aromatase inhibitory activity were modeled as a function of molecular and quantum chemical descriptors derived from low-energy conformer geometrically optimized at B3LYP/6-31G(d) level of theory. Insights on origins of aromatase inhibitory activity was afforded by the computed set of 7 descriptors comprising of F10[N-O], Inflammat-50, Psychotic-80, H-047, BELe1, B10[C-O] and MAXDP. Such significant descriptors were used for QSAR model construction and results indicated that model 4 afforded the best statistical performance. Good predictive performance were achieved as verified from the internal (comprising the training and the leave-one-out cross-validation (LOO-CV) sets) and external sets affording the following statistical parameters: R2Tr = 0.9576 and RMSETr = 0.0958 for the training set; Q2CV = 0.9239 and RMSECV = 0.1304 for the LOO-CV set as well as Q2Ext = 0.7268 and RMSEExt = 0.2927 for the external set. Significant descriptors showed correlation with functional substituents, particularly, R1 in governing high potency as aromatase inhibitor. Molecular docking calculations suggest that key residues interacting with the coumarins were predominantly lipophilic or non-polar while a few were polar and positively-charged. Findings illuminated herein serve as the impetus that can be used to rationally guide the design of new aromatase inhibitors. PMID:26417339

  20. QSAR analysis of antitumor activities of 3,4-ethylenedioxythiphene derivatives

    NASA Astrophysics Data System (ADS)

    Rastija, Vesna; Bajić, Miroslav; Stolić, Ivana; Krstulović, Luka; Jukić, Marijana; Glavaš-Obrovac, Ljubica

    2015-12-01

    QSAR analysis was performed for the antitumor activity of 27 derivatives of 3,4-ethylenedioxythiophene against six carcinoma cell lines. The best models were obtained with surface area (SAG) in combination with lipohilicity (log P) as descriptors. Results have shown that molecules with smaller solvent accessible surface area and higher lipophilicy should have higher biological activity against carcinoma cell.

  1. QSAR Modeling: Where have you been? Where are you going to?

    PubMed Central

    Cherkasov, Artem; Muratov, Eugene N.; Fourches, Denis; Varnek, Alexandre; Baskin, Igor I.; Cronin, Mark; Dearden, John; Gramatica, Paola; Martin, Yvonne C.; Todeschini, Roberto; Consonni, Viviana; Kuz'min, Victor E.; Cramer, Richard; Benigni, Romualdo; Yang, Chihae; Rathman, James; Terfloth, Lothar; Gasteiger, Johann; Richard, Ann; Tropsha, Alexander

    2014-01-01

    Quantitative Structure-Activity Relationship modeling is one of the major computational tools employed in medicinal chemistry. However, throughout its entire history it has drawn both praise and criticism concerning its reliability, limitations, successes, and failures. In this paper, we discuss: (i) the development and evolution of QSAR; (ii) the current trends, unsolved problems, and pressing challenges; and (iii) several novel and emerging applications of QSAR modeling. Throughout this discussion, we provide guidelines for QSAR development, validation, and application, which are summarized in best practices for building rigorously validated and externally predictive QSAR models. We hope that this Perspective will help communications between computational and experimental chemists towards collaborative development and use of QSAR models. We also believe that the guidelines presented here will help journal editors and reviewers apply more stringent scientific standards to manuscripts reporting new QSAR studies, as well as encourage the use of high quality, validated QSARs for regulatory decision making. PMID:24351051

  2. QSAR modeling: where have you been? Where are you going to?

    PubMed

    Cherkasov, Artem; Muratov, Eugene N; Fourches, Denis; Varnek, Alexandre; Baskin, Igor I; Cronin, Mark; Dearden, John; Gramatica, Paola; Martin, Yvonne C; Todeschini, Roberto; Consonni, Viviana; Kuz'min, Victor E; Cramer, Richard; Benigni, Romualdo; Yang, Chihae; Rathman, James; Terfloth, Lothar; Gasteiger, Johann; Richard, Ann; Tropsha, Alexander

    2014-06-26

    Quantitative structure-activity relationship modeling is one of the major computational tools employed in medicinal chemistry. However, throughout its entire history it has drawn both praise and criticism concerning its reliability, limitations, successes, and failures. In this paper, we discuss (i) the development and evolution of QSAR; (ii) the current trends, unsolved problems, and pressing challenges; and (iii) several novel and emerging applications of QSAR modeling. Throughout this discussion, we provide guidelines for QSAR development, validation, and application, which are summarized in best practices for building rigorously validated and externally predictive QSAR models. We hope that this Perspective will help communications between computational and experimental chemists toward collaborative development and use of QSAR models. We also believe that the guidelines presented here will help journal editors and reviewers apply more stringent scientific standards to manuscripts reporting new QSAR studies, as well as encourage the use of high quality, validated QSARs for regulatory decision making. PMID:24351051

  3. (Q)SAR: A Tool for the Toxicologist.

    PubMed

    Steinbach, Thomas; Gad-McDonald, Samantha; Kruhlak, Naomi; Powley, Mark; Greene, Nigel

    2015-01-01

    A continuing education (CE) course at the 2014 American College of Toxicology annual meeting covered the topic of (Quantitative) Structure-Activity Relationships [(Q)SAR]. The (Q)SAR methodologies use predictive computer modeling based on predefined rules to describe the relationship between chemical structure and a chemical's associated biological activity or statistical tools to find correlations between biologic activity and the molecular structure or properties of a compound. The (Q)SAR has applications in risk assessment, drug discovery, and regulatory decision making. Pressure within industry to reduce the cost of drug development and societal pressure for government regulatory agencies to produce more accurate and timely risk assessment of drugs and chemicals have necessitated the use of (Q)SAR. Producing a high-quality (Q)SAR model depends on many factors including the choice of statistical methods and descriptors, but first and foremost the quality of the data input into the model. Understanding how a (Q)SAR model is developed and applied is critical to the successful use of such a tool. The CE session covered the basic principles of (Q)SAR, practical applications of these computational models in toxicology, how regulatory agencies use and interpret (Q)SAR models, and potential pitfalls of using them. PMID:25979517

  4. The effect of various atomic partial charge schemes to elucidate consensus activity-correlating molecular regions: a test case of diverse QSAR models.

    PubMed

    Kumar, Sivakumar Prasanth; Jha, Prakash C; Jasrai, Yogesh T; Pandya, Himanshu A

    2016-03-01

    The estimation of atomic partial charges of the small molecules to calculate molecular interaction fields (MIFs) is an important process in field-based quantitative structure-activity relationship (QSAR). Several studies showed the influence of partial charge schemes that drastically affects the prediction accuracy of the QSAR model and focused on the selection of appropriate charge models that provide highest cross-validated correlation coefficient ([Formula: see text] or q(2)) to explain the variation in chemical structures against biological endpoints. This study shift this focus in a direction to understand the molecular regions deemed to explain SAR in various charge models and recognize a consensus picture of activity-correlating molecular regions. We selected eleven diverse dataset and developed MIF-based QSAR models using various charge schemes including Gasteiger-Marsili, Del Re, Merck Molecular Force Field, Hückel, Gasteiger-Hückel, and Pullman. The generalized resultant QSAR models were then compared with Open3DQSAR model to interpret the MIF descriptors decisively. We suggest the regions of activity contribution or optimization can be effectively determined by studying various charge-based models to understand SAR precisely. PMID:25997097

  5. Novel 3-Amino-6-chloro-7-(azol-2 or 5-yl)-1,1-dioxo-1,4,2-benzodithiazine Derivatives with Anticancer Activity: Synthesis and QSAR Study.

    PubMed

    Pogorzelska, Aneta; Sławiński, Jarosław; Brożewicz, Kamil; Ulenberg, Szymon; Bączek, Tomasz

    2015-01-01

    A series of new 3-amino-6-chloro-7-(azol-2 or 5-yl)-1,1-dioxo-1,4,2-benzodithiazine derivatives 5a-j have been synthesized and evaluated in vitro for their antiproliferative activity at the U.S. National Cancer Institute. The most active compound 5h showed significant cytotoxic effects against ovarian (OVCAR-3) and breast (MDA-MB-468) cancer (10% and 47% cancer cell death, respectively) as well as a good selectivity toward prostate (DU-145), colon (SW-620) and renal (TK-10) cancer cell lines. To obtain a deeper insight into the structure-activity relationships of the new compounds 5a-j QSAR studies have been applied. Theoretical calculations allowed the identification of molecular descriptors belonging to the RDF (RDF055p and RDF145m in the MOLT-4 and UO-31 QSAR models, respectively) and 3D-MorSE (Mor32m and Mor16e for MOLT-4 and UO-31 QSAR models) descriptor classes. Based on these data, QSAR models with good robustness and predictive ability have been obtained. PMID:26690109

  6. SEDIMENT-ASSOCIATED REACTIONS OF AROMATIC AMINES: QSAR DEVELOPMENT

    EPA Science Inventory

    Despite the common occurrence of the aromatic amine functional group in environmental contaminants, few quantitative structure-activity relationships (QSARs) have been developed to predict sorption kinetics for aromatic amines in natural soils and sediments. Towards the goal of d...

  7. QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIPS FOR CHEMICAL REDUCTIONS OF ORGANIC CONTAMINANTS

    EPA Science Inventory

    Sufficient kinetic data on abiotic reduction reactions involving organic contaminants are now available that quantitative structure-activity relationships (QSARs) for these reactions can be developed. Over 50 QSARs have been reported, most in just the last few years, and they ar...

  8. QSAR Methods.

    PubMed

    Gini, Giuseppina

    2016-01-01

    In this chapter, we introduce the basis of computational chemistry and discuss how computational methods have been extended to some biological properties and toxicology, in particular. Since about 20 years, chemical experimentation is more and more replaced by modeling and virtual experimentation, using a large core of mathematics, chemistry, physics, and algorithms. Then we see how animal experiments, aimed at providing a standardized result about a biological property, can be mimicked by new in silico methods. Our emphasis here is on toxicology and on predicting properties through chemical structures. Two main streams of such models are available: models that consider the whole molecular structure to predict a value, namely QSAR (Quantitative Structure Activity Relationships), and models that find relevant substructures to predict a class, namely SAR. The term in silico discovery is applied to chemical design, to computational toxicology, and to drug discovery. We discuss how the experimental practice in biological science is moving more and more toward modeling and simulation. Such virtual experiments confirm hypotheses, provide data for regulation, and help in designing new chemicals. PMID:27311459

  9. In Vitro Antioxidant Activity of Selected 4-Hydroxy-chromene-2-one Derivatives—SAR, QSAR and DFT Studies

    PubMed Central

    Mladenović, Milan; Mihailović, Mirjana; Bogojević, Desanka; Matić, Sanja; Nićiforović, Neda; Mihailović, Vladimir; Vuković, Nenad; Sukdolak, Slobodan; Solujić, Slavica

    2011-01-01

    The series of fifteen synthesized 4-hydroxycoumarin derivatives was subjected to antioxidant activity evaluation in vitro, through total antioxidant capacity, 1,1-diphenyl-2-picryl-hydrazyl (DPPH), hydroxyl radical, lipid peroxide scavenging and chelating activity. The highest activity was detected during the radicals scavenging, with 2b, 6b, 2c, and 4c noticed as the most active. The antioxidant activity was further quantified by the quantitative structure-activity relationships (QSAR) studies. For this purpose, the structures were optimized using Paramethric Method 6 (PM6) semi-empirical and Density Functional Theory (DFT) B3LYP methods. Bond dissociation enthalpies of coumarin 4-OH, Natural Bond Orbital (NBO) gained hybridization of the oxygen, acidity of the hydrogen atom and various molecular descriptors obtained, were correlated with biological activity, after which we designed 20 new antioxidant structures, using the most favorable structural motifs, with much improved predicted activity in vitro. PMID:21686153

  10. QSAR DataBank - an approach for the digital organization and archiving of QSAR model information

    PubMed Central

    2014-01-01

    Background Research efforts in the field of descriptive and predictive Quantitative Structure-Activity Relationships or Quantitative Structure–Property Relationships produce around one thousand scientific publications annually. All the materials and results are mainly communicated using printed media. The printed media in its present form have obvious limitations when they come to effectively representing mathematical models, including complex and non-linear, and large bodies of associated numerical chemical data. It is not supportive of secondary information extraction or reuse efforts while in silico studies poses additional requirements for accessibility, transparency and reproducibility of the research. This gap can and should be bridged by introducing domain-specific digital data exchange standards and tools. The current publication presents a formal specification of the quantitative structure-activity relationship data organization and archival format called the QSAR DataBank (QsarDB for shorter, or QDB for shortest). Results The article describes QsarDB data schema, which formalizes QSAR concepts (objects and relationships between them) and QsarDB data format, which formalizes their presentation for computer systems. The utility and benefits of QsarDB have been thoroughly tested by solving everyday QSAR and predictive modeling problems, with examples in the field of predictive toxicology, and can be applied for a wide variety of other endpoints. The work is accompanied with open source reference implementation and tools. Conclusions The proposed open data, open source, and open standards design is open to public and proprietary extensions on many levels. Selected use cases exemplify the benefits of the proposed QsarDB data format. General ideas for future development are discussed. PMID:24910716

  11. QSAR modeling of aromatase inhibitory activity of 1-substituted 1,2,3-triazole analogs of letrozole.

    PubMed

    Nantasenamat, Chanin; Worachartcheewan, Apilak; Prachayasittikul, Supaluk; Isarankura-Na-Ayudhya, Chartchalerm; Prachayasittikul, Virapong

    2013-11-01

    Aromatase is an estrogen biosynthesis enzyme belonging to the cytochrome P450 family that catalyzes the rate-limiting step of converting androgens to estrogens. As it is pertinent toward tumor cell growth promotion, aromatase is a lucrative therapeutic target for breast cancer. In the pursuit of robust aromatase inhibitors, a set of fifty-four 1-substituted mono- and bis-benzonitrile or phenyl analogs of 1,2,3-triazole letrozole were employed in quantitative structure-activity relationship (QSAR) study using multiple linear regression (MLR), artificial neural network (ANN) and support vector machine (SVM). Such QSAR models were developed using a set of descriptors providing coverage of the general characteristics of a molecule encompassing molecular size, flexibility, polarity, solubility, charge and electronic properties. Important physicochemical properties giving rise to good aromatase inhibition were obtained by means of exploring its chemical space as a function of the calculated molecular descriptors. The optimal subset of 3 descriptors (i.e. number of rings, ALogP and HOMO-LUMO) was further used for QSAR model construction. The predicted pIC₅₀ values were in strong correlation with their experimental values displaying correlation coefficient values in the range of 0.72-0.83 for the cross-validated set (QCV) while the external test set (Q(Ext)) afforded values in the range of 0.65-0.66. Insights gained from the present study are anticipated to provide pertinent information contributing to the origins of aromatase inhibitory activity and therefore aid in our on-going quest for aromatase inhibitors with robust properties. PMID:24012714

  12. Developing a QSAR model for hepatotoxicity screening of the active compounds in traditional Chinese medicines.

    PubMed

    Huang, Shan-Han; Tung, Chun-Wei; Fülöp, Ferenc; Li, Jih-Heng

    2015-04-01

    The perception that natural substances are deemed safe has made traditional Chinese medicine (TCM) popular in the treatment and prevention of disease globally. However, such an assumption is often misleading owing to a lack of scientific validation. To assess the safety of TCM, in silico screening provides major advantages over the classical laboratory approaches in terms of resource- and time-saving and full reproducibility. To screen the hepatotoxicity of the active compounds of TCMs, a quantitative structure-activity relationship (QSAR) model was firstly established by utilizing drugs from the Liver Toxicity Knowledge Base. These drugs were annotated with drug-induced liver injury information obtained from clinical trials and post-marketing surveillance. The performance of the model after nested 10-fold cross-validation was 79.1%, 91.2%, 53.8% for accuracy, sensitivity, and specificity, respectively. The external validation of 91 well-known ingredients of common herbal medicines yielded a high accuracy (87%). After screening the TCM Database@Taiwan, the world's largest TCM database, a total of 6853 (74.8%) ingredients were predicted to have hepatotoxic potential. The one-hundred chemical ingredients predicted to have the highest hepatotoxic potential by our model were further verified by published literatures. Our study indicated that this model can serve as a complementary tool to evaluate the safety of TCM. PMID:25660478

  13. QSAR Study on Thiazolidine-2,4-dione Derivatives for Antihyperglycemic Activity.

    PubMed

    Prashantha Kumar, B R; Nanjan, M J

    2008-09-01

    A set of seventy four molecules belonging to the class of thioglitazones were subjected to the QSAR analysis for their antihyperglycemic activity. All the molecules were subjected to energy minimization to get 3D structures, followed by conformational analysis to get the conformation of the molecule associated with the least energy and highest stability. Various physico-chemical parameters were then calculated using ALCHEMY 2000 software, namely, thermodynamic parameters, structure-dependant parameters, topological parameters and charge-dependant parameters. Multiple linear regression analysis was carried out on all the molecules. The final equation was developed by choosing optimal combination of descriptors after removing the outliers. Cross validation was performed by leave one out method to arrive at the final QSAR model for the chosen set of molecules to exhibit antihyperglycemic activity. PMID:21394250

  14. Synthesis and QSAR study of novel anti-inflammatory active mesalazine-metronidazole conjugates.

    PubMed

    Naumov, Roman N; Panda, Siva S; Girgis, Adel S; George, Riham F; Farhat, Michel; Katritzky, Alan R

    2015-06-01

    Novel, mesalazine, metronidazole conjugates 6a-e with amino acid linkers were synthesized utilizing benzotriazole chemistry. Biological data acquired for all the novel bis-conjugates showed (a) some bis-conjugates exhibit comparable anti-inflammatory activity with parent drugs and (b) the potent bis-conjugates show no visible stomach lesions. 3D-pharmacophore and 2D-QSAR modeling support the observed bio-properties. PMID:25937011

  15. 3D-QSAR Studies on a Series of Dihydroorotate Dehydrogenase Inhibitors: Analogues of the Active Metabolite of Leflunomide

    PubMed Central

    Li, Shun-Lai; He, Mao-Yu; Du, Hong-Guang

    2011-01-01

    The active metabolite of the novel immunosuppressive agent leflunomide has been shown to inhibit the enzyme dihydroorotate dehydrogenase (DHODH). This enzyme catalyzes the fourth step in de novo pyrimidine biosynthesis. Self-organizing molecular field analysis (SOMFA), a simple three-dimensional quantitative structure-activity relationship (3D-QSAR) method is used to study the correlation between the molecular properties and the biological activities of a series of analogues of the active metabolite. The statistical results, cross-validated rCV2 (0.664) and non cross-validated r2 (0.687), show a good predictive ability. The final SOMFA model provides a better understanding of DHODH inhibitor-enzyme interactions, and may be useful for further modification and improvement of inhibitors of this important enzyme. PMID:21686163

  16. History and successes of QSAR in environmental applications

    SciTech Connect

    Veith, G.D.

    1994-12-31

    The history of the development of relationships between chemical structure and chemical behavior for assessing the safety of chemicals is marked by the struggle with timidity that so many areas of science face. Despite a continuous stream of successes for estimating properties and biological activity of chemicals from their structure, the field of QSAR has been met forcibly by the skeptics. In failing to articulate the potential savings in term of costs land test animals, QSAR researchers have enabled the skeptics to prevent a strategic QSAR program from being formed in either the private or the public sectors. QSAR must generate systematic, reference databases for the intrinsic properties of chemicals. Partitioning is such an intrinsic property. QSAR has succeeded not only in calculating hydrophobicity descriptors that control partitioning, but also in using these descriptors in counties relationships for specific endpoints. QSAR has also developed databases for potency. So many structure-toxicity relationships have been published that the potency of over 75 percent of all chemicals produced worldwide can be estimated without further animal testing. Biochemical persistence, as evidenced in biodegradability and/or tissue metabolism, lags behind due, in part, to a shortage of systematic databases. Several interesting approaches will be discussed. Finally, intrinsic reactivity and the selectivity of chemicals among competing interactions must be modeled. Since chemical reactivity holds the key to identifying genotoxic chemicals and other highly toxic chemicals, reactivity models are urgently needed. Recent QSAR advances for some forms of reactivity will be discussed.

  17. Synthesis and QSAR study of novel 6-chloro-3-(2-Arylmethylene-1-methylhydrazino)-1,4,2-benzodithiazine 1,1-dioxide derivatives with anticancer activity.

    PubMed

    Sławiński, Jarosław; Żołnowska, Beata; Brzozowski, Zdzisław; Kawiak, Anna; Belka, Mariusz; Bączek, Tomasz

    2015-01-01

    A series of new 6-chloro-3-(2-arylmethylene-1-methylhydrazino)-1,4,2-benzodithiazine 1,1-dioxide derivatives were effectively synthesized from N-methyl-N-(6-chloro-1,1-dioxo-1,4,2-benzodithiazin-3-yl)hydrazines. The intermediate compounds as well as the products, were evaluated for their cytotoxic effects toward three human cancer cell lines. All compounds shown moderate or weak cytotoxic effects against the tested cancer cell lines, but selective cytotoxic effects were observed. Compound 16 exhibited the most potent cytotoxic activity against the HeLa cell line, with an IC50 value of 10 µM, while 14 was the most active against the MCF-7 and HCT-116 cell lines, affording IC50 values of 15 µM and 16 µM, respectively. The structure-activity relationship was evaluated based on QSAR methodology. The QSAR MCF-7 model indicated that natural charge on carbon atom C13 and energy of highest occupied molecular orbital (HOMO) are highly involved in cytotoxic activity against MCF-7 cell line. The cytotoxic activity of compounds against HCT-116 cell line is dependent on natural charge on carbon atom C13 and electrostatic charge on nitrogen atom N10. The obtained QSAR models could provide guidelines for further development of novel anticancer agents. PMID:25834988

  18. 2 D - QSAR studies on CYP26A1 inhibitory activity of 1-[benzofuran-2-yl-(4-alkyl/aryl-phenyl)-methyl]- 1 H-triazoles.

    PubMed

    Yadav, Madhu

    2011-01-01

    The Quantitative Structure Activity Relationship (QSAR) study is performed over a set of 15, 4-alkyl/aryl-substituted 1- [benzofuran-2-yl-phenylmethyl]-1 H-triazoles derivatives. This study is based on the application of physicochemical parameters in QSAR. The parameters include (MR (molar refractivity), MW (molecular weight), Pc (parachor), St (surface tension), D (density), Ir (index of refraction) and log P (partition coefficient). The parameters describing physiochemical properties are used as independent variables and the biological activity (IC(50)) is considered as dependent variable in multiple regression analysis. Different models were generated with high co-efficient of determination (R(2)). The 2D-QSAR study identified compounds capable of inhibiting the metabolic breakdown of the retinoid (trans-retinoic acid (ATRA)) involved in the activation of specific nuclear Retinoic acid receptors (RARs). This study identifies R115866 as a potential inhibitor of the cytochrome P450 (CYP) mediated metabolism with increased RA levels for retinoid actions. PMID:22347780

  19. Quantitative structure-activity relationships for cellular uptake of surface-modified nanoparticles.

    PubMed

    Liu, Rong; Rallo, Robert; Bilal, Muhammad; Cohen, Yoram

    2015-01-01

    Quantitative structure-activity relationships (QSARs) were developed, for cellular uptake of nanoparticles (NPs) of the same iron oxide core but with different surface-modifying organic molecules, based on linear and non-linear (epsilon support vector regression (ε-SVR)). A linear QSAR provided high prediction accuracy of R2=0.751 (coefficient of determination) using 11 descriptors selected from an initial pool of 184 descriptors calculated for the NP surfacemodifying molecules, while a ε-SVR based QSAR with only 6 descriptors improved prediction accuracy to R2=0.806. The linear and ε-SVR based QSARs both demonstrated good robustness and well spanned applicability domains. It is suggested that the approach of evaluating pertinent descriptors and their significance, via QSAR analysis, to cellular NP uptake could support planning and interpretation of toxicity studies as well as provide guidance for the tailor-design NPs with respect to targeted cellular uptake for various applications. PMID:25747434

  20. 3D-QSAR modelling dataset of bioflavonoids for predicting the potential modulatory effect on P-glycoprotein activity.

    PubMed

    Wongrattanakamon, Pathomwat; Lee, Vannajan Sanghiran; Nimmanpipug, Piyarat; Jiranusornkul, Supat

    2016-12-01

    The data is obtained from exploring the modulatory activities of bioflavonoids on P-glycoprotein function by ligand-based approaches. Multivariate Linear-QSAR models for predicting the induced/inhibitory activities of the flavonoids were created. Molecular descriptors were initially used as independent variables and a dependent variable was expressed as pFAR. The variables were then used in MLR analysis by stepwise regression calculation to build the linear QSAR data. The entire dataset consisted of 23 bioflavonoids was used as a training set. Regarding the obtained MLR QSAR model, R of 0.963, R (2)=0.927, [Formula: see text], SEE=0.197, F=33.849 and q (2)=0.927 were achieved. The true predictabilities of QSAR model were justified by evaluation with the external dataset (Table 4). The pFARs of representative flavonoids were predicted by MLR QSAR modelling. The data showed that internal and external validations may generate the same conclusion. PMID:27626051

  1. QSAR Studies of Copper Azamacrocycles and Thiosemicarbazones

    PubMed Central

    Wolohan, Peter; Yoo, Jeongsoo; Welch, Michael J.; Reichert, David E.

    2008-01-01

    Genetic algorithms (GA) were used to develop specific copper metal-ligand force field parameters for the MM3 force field, from a combination of crystallographic structures and ab initio calculations. These new parameters produced results in good agreement with experiment and previously reported copper metal-ligand parameters for the AMBER force field. The MM3 parameters were then used to develop several Quantitative Structure Activity Relationship (QSAR) models. A successful QSAR for predicting the lipophilicity (logPow) of several classes of Cu(II) chelating ligands, was built using a training set of thirty-two Cu(II) radiometal complexes and six simple molecular descriptors. The QSAR exhibited a correlation between the predicted and experimental logPow with a r2 = 0.95, q2 = 0.92. When applied to an external test set of eleven Cu(II) complexes the QSAR preformed with great accuracy; r2 = 0.93 and a q2 = 0.91 utilizing a leave-one-out cross-validation analysis. Additional QSAR models were developed to predict the biodistribution of a smaller set of Cu(II) bis(thiosemicarbazone) complexes. PMID:16107156

  2. Integration of QSAR and in vitro toxicology.

    PubMed Central

    Barratt, M D

    1998-01-01

    The principles of quantitative structure-activity relationships (QSAR) are based on the premise that the properties of a chemical are implicit in its molecular structure. Therefore, if a mechanistic hypothesis can be proposed linking a group of related chemicals with a particular toxic end point, the hypothesis can be used to define relevant parameters to establish a QSAR. Ways in which QSAR and in vitro toxicology can complement each other in development of alternatives to live animal experiments are described and illustrated by examples from acute toxicological end points. Integration of QSAR and in vitro methods is examined in the context of assessing mechanistic competence and improving the design of in vitro assays and the development of prediction models. The nature of biological variability is explored together with its implications for the selection of sets of chemicals for test development, optimization, and validation. Methods are described to support the use of data from in vivo tests that do not meet today's stringent requirements of acceptability. Integration of QSAR and in vitro methods into strategic approaches for the replacement, reduction, and refinement of the use of animals is described with examples. PMID:9599692

  3. QSAR study of antimicrobial activity of some 3-nitrocoumarins and related compounds.

    PubMed

    Debeljak, Zeljko; Skrbo, Armin; Jasprica, Ivona; Mornar, Ana; Plecko, Vanda; Banjanac, Mihajlo; Medić-Sarić, Marica

    2007-01-01

    A new class of antimicrobial agents, 3-nitrocoumarins and related compounds, has been chosen as a subject of the present study. In order to explore their activity and molecular properties that determine their antimicrobial effects, QSAR models have been proposed. Most of the 64 descriptors used for the development were extracted from semiempirical and density functional theory (DFT) founded calculations. For this study literature data containing results of microbiological activity screening of 33 coumarin derivatives against selected clinical isolates of C. albicans (CA) and S. aureus (SA) have been selected. Multivariate predictive models based on random forests (RF) and two hybrid classification approaches, genetic algorithms (GA) associated with either support vector machines (SVM) or k nearest neighbor (kNN), have been used for establishment of QSARs. An applied feature selection approach enabled two-dimensional linear separation of active and inactive compounds, which was a necessary tool for rational candidate design and descriptor relevance interpretation. Candidate molecules were checked by cross-validated models, and selected derivatives have been synthesized. Their antimicrobial activities were compared to antimicrobial activities of the representative derivatives from the original set in terms of minimal inhibitory concentration (MIC) against chosen SA and CA ATCC strains. High ranking of descriptors consistent with the degree of hydrolytic instability of selected compounds is common to models of antimicrobial activity against both microorganisms. However, descriptor ranking indicates different antimicrobial mechanisms of action of chosen coumarin derivatives against selected microbial species. PMID:17489552

  4. Synthesis, antimycobacterial, antiviral, antimicrobial activity and QSAR studies of N(2)-acyl isonicotinic acid hydrazide derivatives.

    PubMed

    Judge, Vikramjeet; Narasimhan, Balasubramanian; Ahuja, Munish; Sriram, Dharmarajan; Yogeeswari, Perumal; De Clercq, Erik; Pannecouque, Christophe; Balzarini, Jan

    2013-02-01

    A series of N(2)-acyl isonicotinic acid hydrazides (1-17) was synthesized and tested for its in vitro antimycobacterial activity against Mycobacterium tuberculosis and the results indicated that the compound, isonicotinic acid N'- tetradecanoyl-hydrazide (12) was more active than the reference compound isoniazid. The results of antimicrobial activity of the synthesized compounds against S. aureus, B. subtilis, E. coli, C. albicans and A. niger indicated that compounds with dichloro, hydroxyl, tri-iodo and N(2)-tetradecanoyl substituent were the most active ones. The antiviral activity studies depicted that none of the tested compounds were active against DNA or RNA viruses. The multi-target QSAR model was found to be effective in describing the antimicrobial activity of N(2)-acyl isonicotinic acid hydrazides. PMID:22762163

  5. QSARS FOR PREDICTING REDUCTIVE TRANSFORMATION RATE CONSTANTS OF HALOGENATED AROMATIC HYDROCARBONS IN ANOXIC SEDIMENT SYSTEMS

    EPA Science Inventory

    Quantitative structure-activity relationships (QSARs) are developed relating initial and final pseudo-first-order disappearance rate constants of 45 halogenated aromatic hydrocarbons in anoxic sediments to four readily available molecular descriptors: the carbon-halogen bond stre...

  6. Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARs.

    PubMed Central

    Eriksson, Lennart; Jaworska, Joanna; Worth, Andrew P; Cronin, Mark T D; McDowell, Robert M; Gramatica, Paola

    2003-01-01

    This article provides an overview of methods for reliability assessment of quantitative structure-activity relationship (QSAR) models in the context of regulatory acceptance of human health and environmental QSARs. Useful diagnostic tools and data analytical approaches are highlighted and exemplified. Particular emphasis is given to the question of how to define the applicability borders of a QSAR and how to estimate parameter and prediction uncertainty. The article ends with a discussion regarding QSAR acceptability criteria. This discussion contains a list of recommended acceptability criteria, and we give reference values for important QSAR performance statistics. Finally, we emphasize that rigorous and independent validation of QSARs is an essential step toward their regulatory acceptance and implementation. PMID:12896860

  7. ESTIMATION OF MICROBIAL REDUCTIVE TRANSFORMATION RATES FOR CHLORINATED BENZENES AND PHENOLS USING A QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP APPROACH

    EPA Science Inventory

    A set of literature data was used to derive several quantitative structure-activity relationships (QSARs) to predict the rate constants for the microbial reductive dehalogenation of chlorinated aromatics. Dechlorination rate constants for 25 chloroaromatics were corrected for th...

  8. Quantitative structure-activity relationships for fluoroelastomer/chlorofluorocarbon systems

    SciTech Connect

    Paciorek, K.J.L.; Masuda, S.R.; Nakahara, J.H. ); Snyder, C.E. Jr.; Warner, W.M. )

    1991-12-01

    This paper reports on swell, tensile, and modulus data that were determined for a fluoroelastomer after exposure to a series of chlorofluorocarbon model fluids. Quantitative structure-activity relationships (QSAR) were developed for the swell as a function of the number of carbons and chlorines and for tensile strength as a function of carbon number and chlorine positions in the chlorofluorocarbons.

  9. Energy-Based Pharmacophore and Three-Dimensional Quantitative Structure--Activity Relationship (3D-QSAR) Modeling Combined with Virtual Screening To Identify Novel Small-Molecule Inhibitors of Silent Mating-Type Information Regulation 2 Homologue 1 (SIRT1).

    PubMed

    Pulla, Venkat Koushik; Sriram, Dinavahi Saketh; Viswanadha, Srikant; Sriram, Dharmarajan; Yogeeswari, Perumal

    2016-01-25

    Silent mating-type information regulation 2 homologue 1 (SIRT1), being the homologous enzyme of silent information regulator-2 gene in yeast, has multifaceted functions. It deacetylates a wide range of histone and nonhistone proteins; hence, it has good therapeutic importance. SIRT1 was believed to be overexpressed in many cancers (prostate, colon) and inflammatory disorders (rheumatoid arthritis). Hence, designing inhibitors against SIRT1 could be considered valuable. Both structure-based and ligand-based drug design strategies were employed to design novel inhibitors utilizing high-throughput virtual screening of chemical databases. An energy-based pharmacophore was generated using the crystal structure of SIRT1 bound with a small molecule inhibitor and compared with a ligand-based pharmacophore model that showed four similar features. A three-dimensional quantitative structure-activity relationship (3D-QSAR) model was developed and validated to be employed in the virtual screening protocol. Among the designed compounds, Lead 17 emerged as a promising SIRT1 inhibitor with IC50 of 4.34 μM and, at nanomolar concentration (360 nM), attenuated the proliferation of prostate cancer cells (LnCAP). In addition, Lead 17 significantly reduced production of reactive oxygen species, thereby reducing pro inflammatory cytokines such as IL6 and TNF-α. Furthermore, the anti-inflammatory potential of the compound was ascertained using an animal paw inflammation model induced by carrageenan. Thus, the identified SIRT1 inhibitors could be considered as potent leads to treat both cancer and inflammation. PMID:26636371

  10. Synthesis, Evaluation of Anticancer Activity and QSAR Study of Heterocyclic Esters of Caffeic Acid

    PubMed Central

    Hajmohamad Ebrahim Ketabforoosh, Shima; Amini, Mohsen; Vosooghi, Mohsen; Shafiee, Abbas; Azizi, Ebrahim; Kobarfard, Farzad

    2013-01-01

    Caffeic acid phenethyl ester (CAPE) suppresses the growth of transformed cells such as human breast cancer cells, hepatocarcinoma , myeloid leukemia, colorectal cancer cells, fibrosarcoma, glioma and melanoma. A group of heterocyclic esters of caffeic acid was synthesized using Mitsunobu reaction and the esters were subjected to further structural modification by electrooxidation of the catechol ring of caffeic acid esters in the presence of sodium benzenesulfinate and sodium toluensulfinate as nucleophiles. Both heterocyclic esters of caffeic acid and their arylsulfonyl derivatives were evaluated for their cytotoxic activity against HeLa, SK-OV-3, and HT-29 cancer cell lines. HeLa cells showed the highest sensitivity to the compounds and heterocyclic esters with no substituent on catechol ring showed better activity compared to their substituted counterparts. QSAR studies reemphasized the importance of molecular shape of the compounds for their cytotoxic activity. PMID:24523750

  11. Molecular docking and 3D-QSAR studies on the glucocorticoid receptor antagonistic activity of hydroxylated polychlorinated biphenyls.

    PubMed

    Liu, S; Luo, Y; Fu, J; Zhou, J; Kyzas, G Z

    2016-02-01

    The glucocorticoid receptor (GR) antagonistic activities of hydroxylated polychlorinated biphenyls (HO-PCBs) were recently characterised. To further explore the interactions between HO-PCBs and the GR, and to elucidate structural characteristics that influence the GR antagonistic activity of HO-PCBs, molecular docking and three-dimensional quantitative structure-activity relationship (3D-QSAR) studies were performed. Comparative molecular similarity indices analysis (CoMSIA) was performed using both ligand- and receptor-based alignment schemes. Results generated from the receptor-based model were found to be more satisfactory, with q(2) of 0.632 and r(2) of 0.931 compared with those from the ligand-based model. Some internal validation strategies (e.g. cross-validation analysis, bootstrapping analysis and Y-randomisation) and an external validation method were used respectively to further assess the stability and predictive ability of the derived model. Graphical interpretation of the model provided some insights into the structural features that affected the GR antagonistic activity of HO-PCBs. Molecular docking studies revealed that some key residues were critical for ligand-receptor interactions by forming hydrogen bonds (Glu540) and hydrophobic interactions with ligands (Ile539, Val543 and Trp577). Although CoMSIA sometimes depends on the alignment of the molecules, the information provided is beneficial for predicting the GR antagonistic activities of HO-PCB homologues and is helpful for understanding the binding mechanisms of HO-PCBs to GR. PMID:26848875

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

    PubMed

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

    2006-11-01

    In the present paper, quantitative structure activity relationship (QSAR) approach was applied to understand the affinity and selectivity of a novel series of triaryl imidazole derivatives towards glucagon receptor. Statistically significant and highly predictive QSARs were derived for glucagon receptor inhibition by triaryl imidazoles using QuaSAR descriptors of molecular operating environment (MOE) employing computer-assisted multiple regression procedure. The generated QSAR models revealed that factors related to hydrophobicity, molecular shape and geometry predominantly influences glucagon receptor binding affinity of the triaryl imidazoles indicating the relevance of shape specific steric interactions between the molecule and the receptor. Further, QSAR models formulated for selective inhibition of glucagon receptor over p38 mitogen activated protein (MAP) kinase of the compounds in the series highlights that the same structural features, which influence the glucagon receptor affinity, also contribute to their selective inhibition. PMID:17077558

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

    PubMed

    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

  14. 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

  15. Ecotoxicity quantitative structure-activity relationships for alcohol ethoxylate mixtures based on substance-specific toxicity predictions.

    PubMed

    Boeije, G M; Cano, M L; Marshall, S J; Belanger, S E; Van Compernolle, R; Dorn, P B; Gümbel, H; Toy, R; Wind, T

    2006-05-01

    Traditionally, ecotoxicity quantitative structure-activity relationships (QSARs) for alcohol ethoxylate (AE) surfactants have been developed by assigning the measured ecotoxicity for commercial products to the average structures (alkyl chain length and ethoxylate chain length) of these materials. Acute Daphnia magna toxicity tests for binary mixtures indicate that mixtures are more toxic than the individual AE substances corresponding with their average structures (due to the nonlinear relation of toxicity with structure). Consequently, the ecotoxicity value (expressed as effects concentration) attributed to the average structures that are used to develop the existing QSARs is expected to be too low. A new QSAR technique for complex substances, which interprets the mixture toxicity with regard to the "ethoxymers" distribution (i.e., the individual AE components) rather than the average structure, was developed. This new technique was then applied to develop new AE ecotoxicity QSARs for invertebrates, fish, and mesocosms. Despite the higher complexity, the fit and accuracy of the new QSARs are at least as good as those for the existing QSARs based on the same data set. As expected from typical ethoxymer distributions of commercial AEs, the new QSAR generally predicts less toxicity than the QSARs based on average structure. PMID:16256196

  16. QSARS FOR PREDICTING BIOTIC AND ABIOTIC REDUCTIVE TRANSFORMATION RATE CONSTANTS OF HALOGENATED HYDROCARBONS IN ANOXIC SEDIMENT SYSTEMS

    EPA Science Inventory

    Quantitative structure-activity relationships (QSARs) are developed relating biotic and abiotic pseudo-first-order disappearance rate constants of halogenated hydrocarbons in anoxic sediments to a number of readily available molecular descriptors. ased upon knowledge of the under...

  17. Quantitative structure-activity relationship of antifungal activity of rosin derivatives.

    PubMed

    Wang, Hui; Nguyen, Thi Thanh Hien; Li, Shujun; Liang, Tao; Zhang, Yuanyuan; Li, Jian

    2015-01-15

    To develop new rosin-based wood preservatives with good antifungal activity, 24 rosin derivatives were synthesized, bioassay tested with Trametes versicolor and Gloeophyllum trabeum, and subjected to analysis of their quantitative structure-activity relationships (QSAR). A QSAR analysis using Ampac 9.2.1 and Codessa 2.7.16 software built two QSAR models of antifungal ratio for T. versicolor and G. trabeum with values of R(2)=0.9740 and 0.9692, respectively. Based on the models, tri-N-(3-hydroabietoxy-2-hydroxy) propyl-triethyl ammonium chloride was designed and the bioassay test result proved its better inhibitory effect against the two selected fungi as expected. PMID:25466709

  18. Variable selection for QSAR by artificial ant colony systems.

    PubMed

    Izrailev, S; Agrafiotis, D K

    2002-01-01

    Derivation of quantitative structure-activity relationships (QSAR) usually involves computational models that relate a set of input variables describing the structural properties of the molecules for which the activity has been measured to the output variable representing activity. Many of the input variables may be correlated, and it is therefore often desirable to select an optimal subset of the input variables that results in the most predictive model. In this paper we describe an optimization technique for variable selection based on artificial ant colony systems. The algorithm is inspired by the behavior of real ants, which are able to find the shortest path between a food source and their nest using deposits of pheromone as a communication agent. The underlying basic self-organizing principle is exploited for the construction of parsimonious QSAR models based on neural networks for several classical QSAR data sets. PMID:12184383

  19. QSAR modeling, synthesis and bioassay of diverse leukemia RPMI-8226 cell line active agents.

    PubMed

    Katritzky, Alan R; Girgis, Adel S; Slavov, Svetoslav; Tala, Srinivasa R; Stoyanova-Slavova, Iva

    2010-11-01

    A rigorous QSAR modeling procedure employing CODESSA PRO descriptors has been utilized for the prediction of more efficient anti-leukemia agents. Experimental data concerning the effect on leukemia RPMI-8226 cell line tumor growth of 34 compounds (treated at a dose of 10 μM) was related to their chemical structures by a 4-descriptor QSAR model. Four bis(oxy)bis-urea and bis(sulfanediyl)bis-urea derivatives (4a, 4b, 8, 11a) predicted as active by this model, together with 11b predicted to be of low activity, were synthesized and screened for anti-tumor activity utilizing 55 different tumor cell lines. Compounds 8 and 11a showed anti-tumor properties against most of the adopted cell lines with growth inhibition exceeding 50%. The highly promising preliminary anti-tumor properties of compounds 8 and 11a, were screened at serial dilutions (10(-4)-10(-8) μM) for determination of their GI(50) and TGI against the screened human tumor cell lines. Compound 11a (GI(50) = 1.55, TGI = 8.68 μM) is more effective than compound 8 (GI(50)=58.30, TGI = > 100 μM) against the target leukemia RPMI-8226 cell line. Compound 11a also exhibits highly pronounced anti-tumor properties against NCI-H226, NCI-H23 (non-small cell lung cancer), COLO 205 (colon cancer), SNB-75 (CNS cancer), OVCAR-3, SK-OV-3 (ovarian cancer), A498 (renal cancer) MDA-MB-231/ATCC and MDA-MB-468 (breast cancer) cell lines (GI(50) = 1.95, 1.61, 1.38, 1.56, 1.30, 1.98, 1.18, 1.85, 1.08, TGI = 8.35, 6.01, 2.67, 8.59, 4.01, 7.01, 5.62, 6.38, 5.63 μM, respectively). Thus 11a could be a suitable lead towards the design of broad spectrum anti-tumor active agents targeting various human tumor cell lines. PMID:20843586

  20. 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...

  1. QSAR Models at the US FDA/NCTR.

    PubMed

    Hong, Huixiao; Chen, Minjun; Ng, Hui Wen; Tong, Weida

    2016-01-01

    Quantitative structure-activity relationship (QSAR) has been used in the scientific research community for many decades and applied to drug discovery and development in the industry. QSAR technologies are advancing fast and attracting possible applications in regulatory science. To facilitate the development of reliable QSAR models, the FDA had invested a lot of efforts in constructing chemical databases with a variety of efficacy and safety endpoint data, as well as in the development of computational algorithms. In this chapter, we briefly describe some of the often used databases developed at the FDA such as EDKB (Endocrine Disruptor Knowledge Base), EADB (Estrogenic Activity Database), LTKB (Liver Toxicity Knowledge Base), and CERES (Chemical Evaluation and Risk Estimation System) and the technologies adopted by the agency such as Mold(2) program for calculation of a large and diverse set of molecular descriptors and decision forest algorithm for QSAR model development. We also summarize some QSAR models that have been developed for safety evaluation of the FDA-regulated products. PMID:27311476

  2. Quantitative Structure-Antifungal Activity Relationships for cinnamate derivatives.

    PubMed

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

    2015-12-01

    Quantitative Structure-Activity Relationships (QSAR) are established with the aim of analyzing the fungicidal activities of a set of 27 active cinnamate derivatives. The exploration of more than a thousand of constitutional, topological, geometrical and electronic molecular descriptors, which are calculated with Dragon software, leads to predictions of the growth inhibition on Pythium sp and Corticium rolfsii fungi species, in close agreement to the experimental values extracted from the literature. A set containing 21 new structurally related cinnamate compounds is prepared. The developed QSAR models are applied to predict the unknown fungicidal activity of this set, showing that cinnamates like 38, 28 and 42 are expected to be highly active for Pythium sp, while this is also predicted for 28 and 34 in C. rolfsii. PMID:26410195

  3. Predicting chemically-induced skin reactions. Part II: QSAR models of skin permeability and the relationships between skin permeability and skin sensitization

    SciTech Connect

    Alves, Vinicius M.; Muratov, Eugene; Fourches, Denis; Strickland, Judy; Kleinstreuer, Nicole; Tropsha, Alexander

    2015-04-15

    Skin permeability is widely considered to be mechanistically implicated in chemically-induced skin sensitization. Although many chemicals have been identified as skin sensitizers, there have been very few reports analyzing the relationships between molecular structure and skin permeability of sensitizers and non-sensitizers. The goals of this study were to: (i) compile, curate, and integrate the largest publicly available dataset of chemicals studied for their skin permeability; (ii) develop and rigorously validate QSAR models to predict skin permeability; and (iii) explore the complex relationships between skin sensitization and skin permeability. Based on the largest publicly available dataset compiled in this study, we found no overall correlation between skin permeability and skin sensitization. In addition, cross-species correlation coefficient between human and rodent permeability data was found to be as low as R{sup 2} = 0.44. Human skin permeability models based on the random forest method have been developed and validated using OECD-compliant QSAR modeling workflow. Their external accuracy was high (Q{sup 2}{sub ext} = 0.73 for 63% of external compounds inside the applicability domain). The extended analysis using both experimentally-measured and QSAR-imputed data still confirmed the absence of any overall concordance between skin permeability and skin sensitization. This observation suggests that chemical modifications that affect skin permeability should not be presumed a priori to modulate the sensitization potential of chemicals. The models reported herein as well as those developed in the companion paper on skin sensitization suggest that it may be possible to rationally design compounds with the desired high skin permeability but low sensitization potential. - Highlights: • It was compiled the largest publicly-available skin permeability dataset. • Predictive QSAR models were developed for skin permeability. • No concordance between skin

  4. Use of QSARs in international decision-making frameworks to predict ecologic effects and environmental fate of chemical substances.

    PubMed Central

    Cronin, Mark T D; Walker, John D; Jaworska, Joanna S; Comber, Michael H I; Watts, Christopher D; Worth, Andrew P

    2003-01-01

    This article is a review of the use, by regulatory agencies and authorities, of quantitative structure-activity relationships (QSARs) to predict ecologic effects and environmental fate of chemicals. For many years, the U.S. Environmental Protection Agency has been the most prominent regulatory agency using QSARs to predict the ecologic effects and environmental fate of chemicals. However, as increasing numbers of standard QSAR methods are developed and validated to predict ecologic effects and environmental fate of chemicals, it is anticipated that more regulatory agencies and authorities will find them to be acceptable alternatives to chemical testing. PMID:12896861

  5. 5-N-Substituted-2-(substituted benzenesulphonyl) glutamines as antitumor agents. Part II: synthesis, biological activity and QSAR study.

    PubMed

    Samanta, Soma; Srikanth, K; Banerjee, Suchandra; Debnath, Bikash; Gayen, Shovanlal; Jha, Tarun

    2004-03-15

    Cancer is a major killer disease throughout human history. Thus, cancer becomes a major point of interest in life science. It was proved that cancer is a nitrogen trap and tumor cells are avid glutamine consumers. The non-essential amino acid glutamine, which is a glutamic acid derivative, supplies its amide nitrogen to tumor cells in the biosynthesis of purine and pyrimidine bases of nucleic acids as well as takes part in protein synthesis. Based on these and in continuation of our composite programme of development of new potential anticancer agents through rational drug design, 17 new 5-N-Substituted-2-(substituted benzenesulphonyl) glutamines were selected for synthesis. These compounds as well as 36 earlier synthesized glutamine analogues were screened for antitumor activity using percentage inhibition of tumor cell count as the activity parameter. QSAR study was performed with 53 compounds in order to design leads with increased effectiveness for antitumor activity using both physicochemical and topological parameters. QSAR study showed that steric effect on the aromatic ring is conducive to the activity. n-butyl substitution on aliphatic side chain and atom no 12 is important for antitumor activity of glutamine analogues. PMID:15018914

  6. Residual-QSAR. Implications for genotoxic carcinogenesis

    PubMed Central

    2011-01-01

    Introduction Both main types of carcinogenesis, genotoxic and epigenetic, were examined in the context of non-congenericity and similarity, respectively, for the structure of ligand molecules, emphasizing the role of quantitative structure-activity relationship ((Q)SAR) studies in accordance with OECD (Organization for Economic and Cooperation Development) regulations. The main purpose of this report involves electrophilic theory and the need for meaningful physicochemical parameters to describe genotoxicity by a general mechanism. Residual-QSAR Method The double or looping multiple linear correlation was examined by comparing the direct and residual structural information against the observed activity. A self-consistent equation of observed-computed activity was assumed to give maximum correlation efficiency for those situations in which the direct correlations gave non-significant statistical information. Alternatively, it was also suited to describe slow and apparently non-noticeable cancer phenomenology, with special application to non-congeneric molecules involved in genotoxic carcinogenesis. Application and Discussions The QSAR principles were systematically applied to a given pool of molecules with genotoxic activity in rats to elucidate their carcinogenic mechanisms. Once defined, the endpoint associated with ligand-DNA interaction was used to select variables that retained the main Hansch physicochemical parameters of hydrophobicity, polarizability and stericity, computed by the custom PM3 semiempirical quantum method. The trial and test sets of working molecules were established by implementing the normal Gaussian principle of activities that applies when the applicability domain is not restrained to the congeneric compounds, as in the present study. The application of the residual, self-consistent QSAR method and the factor (or average) method yielded results characterized by extremely high and low correlations, respectively, with the latter resembling

  7. Atomic softness-based QSAR study of testosterone

    NASA Astrophysics Data System (ADS)

    Srivastava, H. K.; Pasha, F. A.; Singh, P. P.

    Ionization potential of an atom in a molecule, electron affinity of an atom in a molecule, and quantum chemical descriptor atomic softness values En‡-based quantitative structure-activity relationship (QSAR) study of testosterone derivatives have been done with the help of PM3 calculations on WinMOPAC 7.21 software. The 3D modeling and geometry optimization of all the compounds have been done with the help of PCMODEL software. The biological activities of testosterone derivatives have been taken from literature. The predicted values of biological activity with the help of multiple linear regression (MLR) analysis is very close to observed biological activity. The cross-validation coefficient and correlation coefficient also indicate that the QSAR model is valuable. Regression analysis shows a very good relationship with biological activity and En‡ values. With the help of these values, prediction of the biological activity of any unknown compound is possible.

  8. Comparison of MLR, PLS and GA-MLR in QSAR analysis.

    PubMed

    Saxena, A K; Prathipati, P

    2003-01-01

    The use of the internet has evolved in quantitative structure-activity relationship (QSAR) over the past decade with the development of web based activities like the availability of numerous public domain software tools for descriptor calculation and chemometric toolboxes. The importance of chemometrics in QSAR has accelerated in recent years for processing the enormous amount of information in form of predictive mathematical models for large datasets of molecules. With the availability of huge numbers of physicochemical and structural parameters, variable selection became crucial in deriving interpretable and predictive QSAR models. Among several approaches to address this problem, the principle component regression (PCR) and partial least squares (PLS) analyses provide highly predictive QSAR models but being more abstract, they are difficult to understand and interpret. Genetic algorithm (GA) is a stochastic method well suited to the problem of variable selection and to solve optimization problems. Consequently the hybrid approach (GA-MLR) combining GA with multiple linear regression (MLR) may be useful in derivation of highly predictive and interpretable QSAR models. In view of the above, a comparative study of stepwise-MLR, PLS and GA-MLR in deriving QSAR models for datasets of alpha1-adrenoreceptor antagonists and beta3-adrenoreceptor agonists has been carried out using the public domain software Dragon for computing descriptors and free Matlab codes for data modeling. PMID:14758986

  9. STRUCTURE-ACTIVITY RELATIONSHIPS FOR SCREENING ORGANIC CHEMICALS FOR POTENTIAL ECOTOXICITY EFFECTS

    EPA Science Inventory

    The paper presents structure-activity relationships (QSAR) for estimating the bioconcentration factor and acute toxicity of some classes of industrial chemicals using only the n-octanol/water partition coefficient (Log P) which is derived from chemical structure. The bioconcentra...

  10. Quantitative structure-activity relationships and the prediction of MHC supermotifs.

    PubMed

    Doytchinova, Irini A; Guan, Pingping; Flower, Darren R

    2004-12-01

    The underlying assumption in quantitative structure-activity relationship (QSAR) methodology is that related chemical structures exhibit related biological activities. We review here two QSAR methods in terms of their applicability for human MHC supermotif definition. Supermotifs are motifs that characterise binding to more than one allele. Supermotif definition is the initial in silico step of epitope-based vaccine design. The first QSAR method we review here--the additive method--is based on the assumption that the binding affinity of a peptide depends on contributions from both amino acids and the interactions between them. The second method is a 3D-QSAR method: comparative molecular similarity indices analysis (CoMSIA). Both methods were applied to 771 peptides binding to 9 HLA alleles. Five of the alleles (A*0201, A*0202, A*0203, A*0206 and A*6802) belong to the HLA-A2 superfamily and the other four (A*0301, A*1101, A*3101 and A*6801) to the HLA-A3 superfamily. For each superfamily, supermotifs defined by the two QSAR methods agree closely and are supported by many experimental data. PMID:15542370

  11. Nonlinear QSAR modeling for predicting cytotoxicity of ionic liquids in leukemia rat cell line: an aid to green chemicals designing.

    PubMed

    Gupta, Shikha; Basant, Nikita; Singh, Kunwar P

    2015-08-01

    Safety assessment and designing of safer ionic liquids (ILs) are among the priorities of the chemists and toxicologists today. Computational approaches have been considered as appropriate methods for prior safety assessment of chemicals and tools to aid in structural designing. The present study is an attempt to investigate the chemical attributes of a wide variety of ILs towards their cytotoxicity in leukemia rat cell line IPC-81 through the development of nonlinear quantitative structure-activity relationship (QSAR) models in the light of the OECD principles for QSAR development. Here, the cascade correlation network (CCN), probabilistic neural network (PNN), and generalized regression neural networks (GRNN) QSAR models were established for the discrimination of ILs in four categories of cytotoxicity and their end-point prediction using few simple descriptors. The diversity and nonlinearity of the considered dataset were evaluated through computing the Euclidean distance and Brock-Dechert-Scheinkman statistics. The constructed QSAR models were validated with external test data. The predictive power of these models was established through a variety of stringent parameters recommended in QSAR literature. The classification QSARs rendered the accuracy of >86%, and the regression models yielded correlation (R(2)) of >0.90 in test data. The developed QSAR models exhibited high statistical confidence and identified the structural elements of the ILs responsible for their cytotoxicity and, hence, could be useful tools in structural designing of safer and green ILs. PMID:25913312

  12. Predicting chemically-induced skin reactions. Part II: QSAR models of skin permeability and the relationships between skin permeability and skin sensitization

    PubMed Central

    Alves, Vinicius M.; Muratov, Eugene; Fourches, Denis; Strickland, Judy; Kleinstreuer, Nicole; Andrade, Carolina H.; Tropsha, Alexander

    2015-01-01

    Skin permeability is widely considered to be mechanistically implicated in chemically-induced skin sensitization. Although many chemicals have been identified as skin sensitizers, there have been very few reports analyzing the relationships between molecular structure and skin permeability of sensitizers and non-sensitizers. The goals of this study were to: (i) compile, curate, and integrate the largest publicly available dataset of chemicals studied for their skin permeability; (ii) develop and rigorously validate QSAR models to predict skin permeability; and (iii) explore the complex relationships between skin sensitization and skin permeability. Based on the largest publicly available dataset compiled in this study, we found no overall correlation between skin permeability and skin sensitization. In addition, cross-species correlation coefficient between human and rodent permeability data was found to be as low as R2=0.44. Human skin permeability models based on the random forest method have been developed and validated using OECD-compliant QSAR modeling workflow. Their external accuracy was high (Q2ext = 0.73 for 63% of external compounds inside the applicability domain). The extended analysis using both experimentally-measured and QSAR-imputed data still confirmed the absence of any overall concordance between skin permeability and skin sensitization. This observation suggests that chemical modifications that affect skin permeability should not be presumed a priori to modulate the sensitization potential of chemicals. The models reported herein as well as those developed in the companion paper on skin sensitization suggest that it may be possible to rationally design compounds with the desired high skin permeability but low sensitization potential. PMID:25560673

  13. Predicting chemically-induced skin reactions. Part II: QSAR models of skin permeability and the relationships between skin permeability and skin sensitization.

    PubMed

    Alves, Vinicius M; Muratov, Eugene; Fourches, Denis; Strickland, Judy; Kleinstreuer, Nicole; Andrade, Carolina H; Tropsha, Alexander

    2015-04-15

    Skin permeability is widely considered to be mechanistically implicated in chemically-induced skin sensitization. Although many chemicals have been identified as skin sensitizers, there have been very few reports analyzing the relationships between molecular structure and skin permeability of sensitizers and non-sensitizers. The goals of this study were to: (i) compile, curate, and integrate the largest publicly available dataset of chemicals studied for their skin permeability; (ii) develop and rigorously validate QSAR models to predict skin permeability; and (iii) explore the complex relationships between skin sensitization and skin permeability. Based on the largest publicly available dataset compiled in this study, we found no overall correlation between skin permeability and skin sensitization. In addition, cross-species correlation coefficient between human and rodent permeability data was found to be as low as R(2)=0.44. Human skin permeability models based on the random forest method have been developed and validated using OECD-compliant QSAR modeling workflow. Their external accuracy was high (Q(2)ext=0.73 for 63% of external compounds inside the applicability domain). The extended analysis using both experimentally-measured and QSAR-imputed data still confirmed the absence of any overall concordance between skin permeability and skin sensitization. This observation suggests that chemical modifications that affect skin permeability should not be presumed a priori to modulate the sensitization potential of chemicals. The models reported herein as well as those developed in the companion paper on skin sensitization suggest that it may be possible to rationally design compounds with the desired high skin permeability but low sensitization potential. PMID:25560673

  14. SAR/QSAR methods in public health practice

    SciTech Connect

    Demchuk, Eugene Ruiz, Patricia; Chou, Selene; Fowler, Bruce A.

    2011-07-15

    Methods of (Quantitative) Structure-Activity Relationship ((Q)SAR) modeling play an important and active role in ATSDR programs in support of the Agency mission to protect human populations from exposure to environmental contaminants. They are used for cross-chemical extrapolation to complement the traditional toxicological approach when chemical-specific information is unavailable. SAR and QSAR methods are used to investigate adverse health effects and exposure levels, bioavailability, and pharmacokinetic properties of hazardous chemical compounds. They are applied as a part of an integrated systematic approach in the development of Health Guidance Values (HGVs), such as ATSDR Minimal Risk Levels, which are used to protect populations exposed to toxic chemicals at hazardous waste sites. (Q)SAR analyses are incorporated into ATSDR documents (such as the toxicological profiles and chemical-specific health consultations) to support environmental health assessments, prioritization of environmental chemical hazards, and to improve study design, when filling the priority data needs (PDNs) as mandated by Congress, in instances when experimental information is insufficient. These cases are illustrated by several examples, which explain how ATSDR applies (Q)SAR methods in public health practice.

  15. Sulfonamide derivatives containing dihydropyrazole moieties selectively and potently inhibit MMP-2/MMP-9: Design, synthesis, inhibitory activity and 3D-QSAR analysis.

    PubMed

    Yan, Xiao-Qiang; Wang, Zhong-Chang; Li, Zhen; Wang, Peng-Fei; Qiu, Han-Yue; Chen, Long-Wang; Lu, Xiao-Yuan; Lv, Peng-Cheng; Zhu, Hai-Liang

    2015-10-15

    New series of sulfonamide derivatives containing a dihydropyrazole moieties inhibitors of MMP-2/MMP-9 were discovered using structure-based drug design. Synthesis, antitumor activity, structure-activity relationship and optimization of physicochemical properties were described. In vitro the bioassay results revealed that most target compounds showed potent inhibitory activity in the enzymatic and cellular assays. Among the compounds, compound 3i exhibited the most potent inhibitory activity with IC50 values of 0.21 μM inhibiting MMP-2 and 1.87 μM inhibiting MMP-9, comparable to the control positive compound CMT-1 (1.26 μM, 2.52 μM). Docking simulation was performed to position compound 3i into the MMP-2 active site to determine the probable binding pose. Docking simulation was further performed to position compound 3i into the MMP-2 active site to determine the probable binding model the 3D-QSAR models were built for reasonable design of MMP-2/MMP-9 inhibitors at present and in future. PMID:26346367

  16. Synthesis, bioassay, and QSAR study of bronchodilatory active 4H-pyrano[3,2-c]pyridine-3-carbonitriles.

    PubMed

    Girgis, Adel S; Saleh, Dalia O; George, Riham F; Srour, Aladdin M; Pillai, Girinath G; Panda, Chandramukhi S; Katritzky, Alan R

    2015-01-01

    A statistically significant QSAR model describing the bioactivity of bronchodilatory active 4H-pyrano[3,2-c]pyridine-3-carbonitriles (N = 41, n = 8, R(2) = 0.824, R(2)cv = 0.724, F = 18.749, s(2) = 0.0018) was obtained employing CODESSA-Pro software. The bronchodilatory active 4H-pyrano[3,2-c]pyridine-3-carbonitriles 17-57 were synthesized through a facile approach via reaction of 1-alkyl-4-piperidones 1-4 with ylidenemalononitriles 5-16 in methanol in the presence of sodium. The bronchodilation properties of 17-57 were investigated in vitro using isolated guinea pig tracheal rings pre-contracted with histamine (standard method) and compared with theophylline (standard reference). Most of the compounds synthesized exhibit promising bronchodilation properties especially, compounds 25 and 28. PMID:25462283

  17. Acute toxicity and QSAR of chlorophenols on Daphnia magna

    SciTech Connect

    Devillers, J.; Chambon, P.

    1986-10-01

    Chlorophenols which are released into natural waters from various industrial processes and from agricultural uses have been recognized as a group of chemical substances potentially hazardous to the aquatic environment. Therefore it is important to estimate their toxic impact on biota. Thus, the scope of this research was to obtain acute toxicity data for seventeen chlorophenols towards Daphnia magna and to explore the possibilities of deriving QSAR's (quantitative structure-activity relationship) from the above values.

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

    USGS Publications Warehouse

    Hickey, James P.

    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.

  19. Partitioning and lipophilicity in quantitative structure-activity relationships.

    PubMed Central

    Dearden, J C

    1985-01-01

    The history of the relationship of biological activity to partition coefficient and related properties is briefly reviewed. The dominance of partition coefficient in quantitation of structure-activity relationships is emphasized, although the importance of other factors is also demonstrated. Various mathematical models of in vivo transport and binding are discussed; most of these involve partitioning as the primary mechanism of transport. The models describe observed quantitative structure-activity relationships (QSARs) well on the whole, confirming that partitioning is of key importance in in vivo behavior of a xenobiotic. The partition coefficient is shown to correlate with numerous other parameters representing bulk, such as molecular weight, volume and surface area, parachor and calculated indices such as molecular connectivity; this is especially so for apolar molecules, because for polar molecules lipophilicity factors into both bulk and polar or hydrogen bonding components. The relationship of partition coefficient to chromatographic parameters is discussed, and it is shown that such parameters, which are often readily obtainable experimentally, can successfully supplant partition coefficient in QSARs. The relationship of aqueous solubility with partition coefficient is examined in detail. Correlations are observed, even with solid compounds, and these can be used to predict solubility. The additive/constitutive nature of partition coefficient is discussed extensively, as are the available schemes for the calculation of partition coefficient. Finally the use of partition coefficient to provide structural information is considered. It is shown that partition coefficient can be a valuable structural tool, especially if the enthalpy and entropy of partitioning are available. PMID:3905374

  20. Causation or only correlation? Application of causal inference graphs for evaluating causality in nano-QSAR models

    NASA Astrophysics Data System (ADS)

    Sizochenko, Natalia; Gajewicz, Agnieszka; Leszczynski, Jerzy; Puzyn, Tomasz

    2016-03-01

    In this paper, we suggest that causal inference methods could be efficiently used in Quantitative Structure-Activity Relationships (QSAR) modeling as additional validation criteria within quality evaluation of the model. Verification of the relationships between descriptors and toxicity or other activity in the QSAR model has a vital role in understanding the mechanisms of action. The well-known phrase ``correlation does not imply causation'' reflects insight statistically correlated with the endpoint descriptor may not cause the emergence of this endpoint. Hence, paradigmatic shifts must be undertaken when moving from traditional statistical correlation analysis to causal analysis of multivariate data. Methods of causal discovery have been applied for broader physical insight into mechanisms of action and interpretation of the developed nano-QSAR models. Previously developed nano-QSAR models for toxicity of 17 nano-sized metal oxides towards E. coli bacteria have been validated by means of the causality criteria. Using the descriptors confirmed by the causal technique, we have developed new models consistent with the straightforward causal-reasoning account. It was proven that causal inference methods are able to provide a more robust mechanistic interpretation of the developed nano-QSAR models.In this paper, we suggest that causal inference methods could be efficiently used in Quantitative Structure-Activity Relationships (QSAR) modeling as additional validation criteria within quality evaluation of the model. Verification of the relationships between descriptors and toxicity or other activity in the QSAR model has a vital role in understanding the mechanisms of action. The well-known phrase ``correlation does not imply causation'' reflects insight statistically correlated with the endpoint descriptor may not cause the emergence of this endpoint. Hence, paradigmatic shifts must be undertaken when moving from traditional statistical correlation analysis to causal

  1. Does Rational Selection of Training and Test Sets Improve the Outcome of QSAR Modeling?

    EPA Science Inventory

    Prior to using a quantitative structure activity relationship (QSAR) model for external predictions, its predictive power should be established and validated. In the absence of a true external dataset, the best way to validate the predictive ability of a model is to perform its s...

  2. The antibacterial activity of some sulfonamides and sulfonyl hydrazones, and 2D-QSAR study of a series of sulfonyl hydrazones

    NASA Astrophysics Data System (ADS)

    Aslan, H. Güzin; Özcan, Servet; Karacan, Nurcan

    2012-12-01

    Benzenesulfonicacid-1-methylhydrazide (1) and its four aromatic sulfonyl hydrazone derivatives (1a-1d), N-(3-amino-2-hydroxypropyl)benzene sulfonamide (2) and N-(2-hydroxyethyl)benzenesulfonamide (3) were synthesized and their structures were determined by IR, 1H NMR, 13C NMR, and LCMS techniques. Antibacterial activities of new synthesized compounds were evaluated against various bacteria strains by microdilution and disk diffusion methods. The experimental results show that presence of OH group on sulfonamides reduces the antimicrobial activity, and antimicrobial activities of the sulfonyl hydrazones (1a-1d) are smaller than that of the parent sulfonamide (1), except Candida albicans. In addition, 2D-QSAR analysis was performed on 28 aromatic sulfonyl hydrazones as antimicrobial agents against Escherichia coli and Staphylococcus aureus. In the QSAR models, the most important descriptor is total point-charge component of the molecular dipole for E. coli, and partial negative surface area (PNSA-1) for S. aureus.

  3. 3D QSAR and docking study of gliptin derivatives as DPP-IV inhibitors.

    PubMed

    Agrawal, Ritesh; Jain, Pratima; Dikshit, Subodh Narayan; Bahare, Radhe Shyam

    2013-05-01

    The article describes the development of a robust pharmacophore model and the investigation of structure activity relationship analysis of 46 xanthine derivatives reported for DPP-IV inhibition using PHASE module of Schrodinger software. The present works also encompasses molecular interaction of 46 xanthine ligand through maestro 8.5 software. The QSAR study comprises AHHR.7 pharmacophore hypothesis, which elaborates the three points, e.g. one hydrogen bond acceptor (A), two hydrophobic rings (H) and one aromatic ring (R). The discrete geometries as pharmacophoric feature were developed and the generated pharmacophore model was used to derive a predictive atom-based 3D QSAR model for the studied data set. The obtained 3D QSAR model has an excellent correlation coefficient value (r(2)= 0.9995) along with good statistical significance which is indicated by high Fisher ratio (F= 8537.4). The model also exhibits good predictive power confirmed by the high value of cross validated correlation coefficient (q(2) = 0.6919). The QSAR model suggests that hydrophobic character is crucial for the DPP-IV inhibitory activity exhibited by these compounds and inclusion of hydrophobic substituents will enhance the DPP-IV inhibition. In addition to the hydrophobic character, electron withdrawing groups positively contribute to the DPP-IV inhibition potency. The findings of the QSAR study provide a set of guidelines for designing compounds with better DPP-IV inhibitory potency. PMID:23305140

  4. The signature molecular descriptor. 3. Inverse-quantitative structure-activity relationship of ICAM-1 inhibitory peptides.

    PubMed

    Churchwell, Carla J; Rintoul, Mark D; Martin, Shawn; Visco, Donald P; Kotu, Archana; Larson, Richard S; Sillerud, Laurel O; Brown, David C; Faulon, Jean-Loup

    2004-03-01

    We present a methodology for solving the inverse-quantitative structure-activity relationship (QSAR) problem using the molecular descriptor called signature. This methodology is detailed in four parts. First, we create a QSAR equation that correlates the occurrence of a signature to the activity values using a stepwise multilinear regression technique. Second, we construct constraint equations, specifically the graphicality and consistency equations, which facilitate the reconstruction of the solution compounds directly from the signatures. Third, we solve the set of constraint equations, which are both linear and Diophantine in nature. Last, we reconstruct and enumerate the solution molecules and calculate their activity values from the QSAR equation. We apply this inverse-QSAR method to a small set of LFA-1/ICAM-1 peptide inhibitors to assist in the search and design of more-potent inhibitory compounds. Many novel inhibitors were predicted, a number of which are predicted to be more potent than the strongest inhibitor in the training set. Two of the more potent inhibitors were synthesized and tested in-vivo, confirming them to be the strongest inhibiting peptides to date. Some of these compounds can be recycled to train a new QSAR and develop a more focused library of lead compounds. PMID:15177078

  5. AZOrange - High performance open source machine learning for QSAR modeling in a graphical programming environment

    PubMed Central

    2011-01-01

    Background Machine learning has a vast range of applications. In particular, advanced machine learning methods are routinely and increasingly used in quantitative structure activity relationship (QSAR) modeling. QSAR data sets often encompass tens of thousands of compounds and the size of proprietary, as well as public data sets, is rapidly growing. Hence, there is a demand for computationally efficient machine learning algorithms, easily available to researchers without extensive machine learning knowledge. In granting the scientific principles of transparency and reproducibility, Open Source solutions are increasingly acknowledged by regulatory authorities. Thus, an Open Source state-of-the-art high performance machine learning platform, interfacing multiple, customized machine learning algorithms for both graphical programming and scripting, to be used for large scale development of QSAR models of regulatory quality, is of great value to the QSAR community. Results This paper describes the implementation of the Open Source machine learning package AZOrange. AZOrange is specially developed to support batch generation of QSAR models in providing the full work flow of QSAR modeling, from descriptor calculation to automated model building, validation and selection. The automated work flow relies upon the customization of the machine learning algorithms and a generalized, automated model hyper-parameter selection process. Several high performance machine learning algorithms are interfaced for efficient data set specific selection of the statistical method, promoting model accuracy. Using the high performance machine learning algorithms of AZOrange does not require programming knowledge as flexible applications can be created, not only at a scripting level, but also in a graphical programming environment. Conclusions AZOrange is a step towards meeting the needs for an Open Source high performance machine learning platform, supporting the efficient development of

  6. Quantitative structure-activity relationship investigation of the role of hydrophobicity in regulating mutagenicity in the Ames test: 2. Mutagenicity of aromatic and heteroaromatic nitro compounds in Salmonella typhimurium TA100

    SciTech Connect

    Debnath, A.K.; Hansch, C. ); Shusterman, A.J. ); Lopez de Compadre, R.L. )

    1992-01-01

    A quantitative structure-activity relationship (QSAR) has been derived for the mutagenic activity of 117 aromatic and heteroaromatic nitro compounds acting on Salmonella typhimurium TA100. Relative mutagenic activity is bilinearly dependent on hydrophobicity, with an optimal log P of 5.44, and is linearly dependent on the energy of the lowest unoccupied molecular orbital of the nitro compound. The dependence of mutagenic activity on hydrophobicity and electronic effects is very similar for TA98 and TA100. Mutagenic activity in TA100 does not depend on the size of the aromatic ring system, as it does in TA98. The effect of the choice of assay organism, TA98 versus TA100, on nitroarene QSAR is seen to be similar to the effect previously found for aminoarenes. Lateral verification of QSARs is presented as a tool for establishing the significance of a new QSAR.

  7. Three dimensional quantitative structure-activity relationships of sulfonamides binding monoclonal antibody by comparative molecular field analysis

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The three-dimensional quantitative structure-activity relationship (3D-QSAR) model of sulfonamide analogs, binding a monoclonal antibody (MabSMR) produced against sulfamerazine was carried out by comparative molecular field analysis (CoMFA). The affinities of MabSMR, expressed as Log10IC50, for 17 ...

  8. Design, synthesis, crystal structure, insecticidal activity, molecular docking, and QSAR studies of novel N3-substituted imidacloprid derivatives.

    PubMed

    Wang, Mei-Juan; Zhao, Xiao-Bo; Wu, Dan; Liu, Ying-Qian; Zhang, Yan; Nan, Xiang; Liu, Huanxiang; Yu, Hai-Tao; Hu, Guan-Fang; Yan, Li-Ting

    2014-06-18

    Three novel series of N3-substituted imidacloprid derivatives were designed and synthesized, and their structures were identified on the basis of satisfactory analytical and spectral ((1)H NMR, (13)C NMR, MS, elemental analysis, and X-ray) data. Preliminary bioassays indicated that all of the derivatives exhibited significant insecticidal activities against Aphis craccivora, with LC50 values ranging from 0.00895 to 0.49947 mmol/L, and the insecticidal activities of some of them were comparable to those of the control imidacloprid. Some key structural features related to their insecticidal activities were identified, and the binding modes between target compounds and nAChR model were also further explored by molecular docking. By comparing the interaction features of imidacloprid and compound 26 with highest insecticidal activity, the origin of the high insecticidal activity of compound 26 was identified. On the basis of the conformations generated by molecular docking, a satisfactory 2D-QSAR model with six selected descriptors was built using genetic algorithm-multiple linear regression (GA-MLR) method. The analysis of the built model showed the molecular size, shape, and the ability to form hydrogen bond were important for insecticidal potency. The information obtained in the study will be very helpful for the design of new derivatives with high insecticidal activities. PMID:24834971

  9. Combined 3D-QSAR, molecular docking and molecular dynamics study on thyroid hormone activity of hydroxylated polybrominated diphenyl ethers to thyroid receptors β

    SciTech Connect

    Li, Xiaolin; Ye, Li; Wang, Xiaoxiang; Wang, Xinzhou; Liu, Hongling; Zhu, Yongliang; Yu, Hongxia

    2012-12-15

    Several recent reports suggested that hydroxylated polybrominated diphenyl ethers (HO-PBDEs) may disturb thyroid hormone homeostasis. To illuminate the structural features for thyroid hormone activity of HO-PBDEs and the binding mode between HO-PBDEs and thyroid hormone receptor (TR), the hormone activity of a series of HO-PBDEs to thyroid receptors β was studied based on the combination of 3D-QSAR, molecular docking, and molecular dynamics (MD) methods. The ligand- and receptor-based 3D-QSAR models were obtained using Comparative Molecular Similarity Index Analysis (CoMSIA) method. The optimum CoMSIA model with region focusing yielded satisfactory statistical results: leave-one-out cross-validation correlation coefficient (q{sup 2}) was 0.571 and non-cross-validation correlation coefficient (r{sup 2}) was 0.951. Furthermore, the results of internal validation such as bootstrapping, leave-many-out cross-validation, and progressive scrambling as well as external validation indicated the rationality and good predictive ability of the best model. In addition, molecular docking elucidated the conformations of compounds and key amino acid residues at the docking pocket, MD simulation further determined the binding process and validated the rationality of docking results. -- Highlights: ► The thyroid hormone activities of HO-PBDEs were studied by 3D-QSAR. ► The binding modes between HO-PBDEs and TRβ were explored. ► 3D-QSAR, molecular docking, and molecular dynamics (MD) methods were performed.

  10. Design, synthesis, α-glucosidase inhibitory activity, molecular docking and QSAR studies of benzimidazole derivatives

    NASA Astrophysics Data System (ADS)

    Dinparast, Leila; Valizadeh, Hassan; Bahadori, Mir Babak; Soltani, Somaieh; Asghari, Behvar; Rashidi, Mohammad-Reza

    2016-06-01

    In this study the green, one-pot, solvent-free and selective synthesis of benzimidazole derivatives is reported. The reactions were catalyzed by ZnO/MgO containing ZnO nanoparticles as a highly effective, non-toxic and environmentally friendly catalyst. The structure of synthesized benzimidazoles was characterized using spectroscopic technics (FT-IR, 1HNMR, 13CNMR). Synthesized compounds were evaluated for their α-glucosidase inhibitory potential. Compounds 3c, 3e, 3l and 4n were potent inhibitors with IC50 values ranging from 60.7 to 168.4 μM. In silico studies were performed to explore the binding modes and interactions between enzyme and synthesized benzimidazoles. Developed linear QSAR model based on density and molecular weight could predict bioactivity of newly synthesized compounds well. Molecular docking studies revealed the availability of some hydrophobic interactions. In addition, the bioactivity of most potent compounds had good correlation with estimated free energy of binding (ΔGbinding) which was calculated according to docked best conformations.

  11. Design and prediction of new acetylcholinesterase inhibitor via quantitative structure activity relationship of huprines derivatives.

    PubMed

    Zhang, Shuqun; Hou, Bo; Yang, Huaiyu; Zuo, Zhili

    2016-05-01

    Acetylcholinesterase (AChE) is an important enzyme in the pathogenesis of Alzheimer's disease (AD). Comparative quantitative structure-activity relationship (QSAR) analyses on some huprines inhibitors against AChE were carried out using comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), and hologram QSAR (HQSAR) methods. Three highly predictive QSAR models were constructed successfully based on the training set. The CoMFA, CoMSIA, and HQSAR models have values of r (2) = 0.988, q (2) = 0.757, ONC = 6; r (2) = 0.966, q (2) = 0.645, ONC = 5; and r (2) = 0.957, q (2) = 0.736, ONC = 6. The predictabilities were validated using an external test sets, and the predictive r (2) values obtained by the three models were 0.984, 0.973, and 0.783, respectively. The analysis was performed by combining the CoMFA and CoMSIA field distributions with the active sites of the AChE to further understand the vital interactions between huprines and the protease. On the basis of the QSAR study, 14 new potent molecules have been designed and six of them are predicted to be more active than the best active compound 24 described in the literature. The final QSAR models could be helpful in design and development of novel active AChE inhibitors. PMID:26832327

  12. QSAR study and the hydrolysis activity prediction of three alkaline lipases from different lipase-producing microorganisms

    PubMed Central

    2012-01-01

    The hydrolysis activities of three alkaline lipases, L-A1, L-A2 and L-A3 secreted by different lipase-producing microorganisms isolated from the Bay of Bohai, P. R. China were characterized with 16 kinds of esters. It was found that all the lipases have the ability to catalyze the hydrolysis of the glycerides, methyl esters, ethyl esters, especially for triglycerides, which shows that they have broad substrate spectra, and this property is very important for them to be used in detergent industry. Three QSAR models were built for L-A1, L-A2 and L-A3 respectively with GFA using Discovery studio 2.1. The models equations 1, 2 and 3 can explain 95.80%, 97.45% and 97.09% of the variances (R2adj) respectively while they could predict 95.44%, 89.61% and 93.41% of the variances (R2cv) respectively. With these models the hydrolysis activities of these lipases to mixed esters were predicted and the result showed that the predicted values are in good agreement with the measured values, which indicates that this method can be used as a simple tool to predict the lipase activities for single or mixed esters. PMID:23016923

  13. QSAR study and the hydrolysis activity prediction of three alkaline lipases from different lipase-producing microorganisms.

    PubMed

    Wang, Haikuan; Wang, Xiaojie; Li, Xiaolu; Zhang, Yehong; Dai, Yujie; Guo, Changlu; Zheng, Heng

    2012-01-01

    The hydrolysis activities of three alkaline lipases, L-A1, L-A2 and L-A3 secreted by different lipase-producing microorganisms isolated from the Bay of Bohai, P. R. China were characterized with 16 kinds of esters. It was found that all the lipases have the ability to catalyze the hydrolysis of the glycerides, methyl esters, ethyl esters, especially for triglycerides, which shows that they have broad substrate spectra, and this property is very important for them to be used in detergent industry. Three QSAR models were built for L-A1, L-A2 and L-A3 respectively with GFA using Discovery studio 2.1. The models equations 1, 2 and 3 can explain 95.80%, 97.45% and 97.09% of the variances (R(2)(adj)) respectively while they could predict 95.44%, 89.61% and 93.41% of the variances (R(2)(cv)) respectively. With these models the hydrolysis activities of these lipases to mixed esters were predicted and the result showed that the predicted values are in good agreement with the measured values, which indicates that this method can be used as a simple tool to predict the lipase activities for single or mixed esters. PMID:23016923

  14. Alert-QSAR. Implications for Electrophilic Theory of Chemical Carcinogenesis

    PubMed Central

    Putz, Mihai V.; Ionaşcu, Cosmin; Putz, Ana-Maria; Ostafe, Vasile

    2011-01-01

    Given the modeling and predictive abilities of quantitative structure activity relationships (QSARs) for genotoxic carcinogens or mutagens that directly affect DNA, the present research investigates structural alert (SA) intermediate-predicted correlations ASA of electrophilic molecular structures with observed carcinogenic potencies in rats (observed activity, A = Log[1/TD50], i.e., ASA=f(X1SA,X2SA,…)). The present method includes calculation of the recently developed residual correlation of the structural alert models, i.e., ARASA=f(A−ASA,X1SA,X2SA,…). We propose a specific electrophilic ligand-receptor mechanism that combines electronegativity with chemical hardness-associated frontier principles, equality of ligand-reagent electronegativities and ligand maximum chemical hardness for highly diverse toxic molecules against specific receptors in rats. The observed carcinogenic activity is influenced by the induced SA-mutagenic intermediate effect, alongside Hansch indices such as hydrophobicity (LogP), polarizability (POL) and total energy (Etot), which account for molecular membrane diffusion, ionic deformation, and stericity, respectively. A possible QSAR mechanistic interpretation of mutagenicity as the first step in genotoxic carcinogenesis development is discussed using the structural alert chemoinformation and in full accordance with the Organization for Economic Co-operation and Development QSAR guidance principles. PMID:21954348

  15. QSAR-Assisted Design of an Environmental Catalyst for Enhanced Estrogen Remediation

    PubMed Central

    Colosi, Lisa M.; Huang, Qingguo; Weber, Walter J.

    2010-01-01

    A quantitative structure-activity relationship (QSAR) was used to streamline redesign of a model environmental catalyst, horseradish peroxidase (HRP), for enhanced reactivity towards a target pollutant, steroid hormone 17β-estradiol. This QSAR, embodying relationship between reaction rate and intermolecular binding distance, was used in silico to screen for mutations improving enzyme reactivity. Eight mutations mediating significant reductions in binding distances were expressed in Saccharomyces cerevisiae, and resulting recombinant HRP strains were analyzed to determine Michaelis-Menten parameters during reaction with the target substrate. Enzyme turnover rate, ln(kCAT), exhibited inverse relationship with model-predicted binding distances (R2 = 0.81), consistent with the QSAR. Additional analysis of native substrate degradation by selected mutants yielded unexpected increases in ln(kCAT) that were also inversely correlated (R2 = 1.00) with model-predicted binding distances. This suggests that the mechanism of improvement comprises a nonspecific “opening up” of the active site such that it better accommodates environmental estrogens of any size. The novel QSAR-assisted approach described herein offers specific advantages compared to conventional design strategies, most notably targeting an entire class of pollutants at one time and a flexible hybridization of benefits associated with rational design and directed evolution. Thus, this approach is a promising tool for improving enzyme-mediated environmental remediation. PMID:20797763

  16. Study of differences in the VEGFR2 inhibitory activities between semaxanib and SU5205 using 3D-QSAR, docking, and molecular dynamics simulations.

    PubMed

    Muñoz, Camila; Adasme, Francisco; Alzate-Morales, Jans H; Vergara-Jaque, Ariela; Kniess, Torsten; Caballero, Julio

    2012-02-01

    Semaxanib (SU5416) and 3-[4'-fluorobenzylidene]indolin-2-one (SU5205) are structurally similar drugs that are able to inhibit vascular endothelial growth factor receptor-2 (VEGFR2), but the former is 87 times more effective than the latter. Previously, SU5205 was used as a radiolabelled inhibitor (as surrogate for SU5416) and a radiotracer for positron emission tomography (PET) imaging, but the compound exhibited poor stability and only a moderate IC(50) toward VEGFR2. In the current work, the relationship between the structure and activity of these drugs as VEGFR2 inhibitors was studied using 3D-QSAR, docking and molecular dynamics (MD) simulations. First, comparative molecular field analysis (CoMFA) was performed using 48 2-indolinone derivatives and their VEGFR2 inhibitory activities. The best CoMFA model was carried out over a training set including 40 compounds, and it included steric and electrostatic fields. In addition, this model gave satisfactory cross-validation results and adequately predicted 8 compounds contained in the test set. The plots of the CoMFA fields could explain the structural differences between semaxanib and SU5205. Docking and molecular dynamics simulations showed that both molecules have the same orientation and dynamics inside the VEGFR2 active site. However, the hydrophobic pocket of VEGFR2 was more exposed to the solvent media when it was complexed with SU5205. An energetic analysis, including Embrace and MM-GBSA calculations, revealed that the potency of ligand binding is governed by van der Waals contacts. PMID:22070999

  17. Rational design, synthesis and 2D-QSAR study of novel vasorelaxant active benzofuran-pyridine hybrids.

    PubMed

    Srour, Aladdin M; Abd El-Karim, Somaia S; Saleh, Dalia O; El-Eraky, Wafaa I; Nofal, Zeinab M

    2016-05-15

    Reaction of 3-aryl-1-(benzofuran-2-yl)-2-propen-1-ones 3a-c with malononitrile in the presence of sufficient amount of sodium alkoxide in the corresponding alcohol proceeds in a regioselective manner to afford 2-alkoxy-4-aryl-6-(benzofuran-2-yl)-3-pyridinecarbonitriles 4-37, which also obtained by treating ylidenemalononitriles 6a-q with 2-acetylbenzofuran 1 in the presence of sufficient amount of sodium alkoxide in the corresponding alcohol. The new chemical entities showed significant vasodilation properties using isolated thoracic aortic rings of rats pre-contracted with norepinephrine hydrochloride standard technique. Compounds 11, 16, 21, 24 and 30 exhibited remarkable activity compared with amiodarone hydrochloride the reference standard used in the present study. CODESSA-Pro software was employing to obtain a statistically significant QSAR model describing the bioactivity of the newly synthesized analogs (N=31, n=5, R(2)=0.846, R(2)cvOO=0.765, R(2)cvMO=0.778, F=27.540. s(2)=0.002). PMID:27048942

  18. Ecological Structure Activity Relationships

    EPA Science Inventory

    Ecological Structure Activity Relationships, v1.00a, February 2009
    ECOSAR (Ecological Structure Activity Relationships) is a personal computer software program that is used to estimate the toxicity of chemicals used in industry and discharged into water. The program predicts...

  19. 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. PMID:25204823

  20. INFLUENCE OF MATRIX FORMULATION ON DERMAL PERCUTANEOUS ABSORPTION OF TRIAZOLE FUNGICIDES USING QSAR AND PBPK / PD MODELS

    EPA Science Inventory

    The objective of this work is to use the Exposure Related Dose Estimating Model (ERDEM) and quantitative structure-activity relationship (QSAR) models to develop an assessment tool for human exposure assessment to triazole fungicides. A dermal exposure route is used for the physi...

  1. Quantitative structure-activity relationship correlation between molecular structure and the Rayleigh enantiomeric enrichment factor.

    PubMed

    Jammer, S; Rizkov, D; Gelman, F; Lev, O

    2015-08-01

    It was recently demonstrated that under environmentally relevant conditions the Rayleigh equation is valid to describe the enantiomeric enrichment - conversion relationship, yielding a proportional constant called the enantiomeric enrichment factor, εER. In the present study we demonstrate a quantitative structure-activity relationship model (QSAR) that describes well the dependence of εER on molecular structure. The enantiomeric enrichment factor can be predicted by the linear Hansch model, which correlates biological activity with physicochemical properties. Enantioselective hydrolysis of sixteen derivatives of 2-(phenoxy)propionate (PPMs) have been analyzed during enzymatic degradation by lipases from Pseudomonas fluorescens (PFL), Pseudomonas cepacia (PCL), and Candida rugosa (CRL). In all cases the QSAR relationships were significant with R(2) values of 0.90-0.93, and showed high predictive abilities with internal and external validations providing QLOO(2) values of 0.85-0.87 and QExt(2) values of 0.8-0.91. Moreover, it is demonstrated that this model enables differentiation between enzymes with different binding site shapes. The enantioselectivity of PFL and PCL was dictated by electronic properties, whereas the enantioselectivity of CRL was determined by lipophilicity and steric factors. The predictive ability of the QSAR model demonstrated in the present study may serve as a helpful tool in environmental studies, assisting in source tracking of unstudied chiral compounds belonging to a well-studied homologous series. PMID:26153539

  2. Active site characterization and structure based 3D-QSAR studies on non-redox type 5-lipoxygenase inhibitors.

    PubMed

    Ul-Haq, Zaheer; Khan, Naveed; Zafar, Syed Kashif; Moin, Syed Tarique

    2016-06-10

    Structure-based 3D-QSAR study was performed on a class of 5-benzylidene-2-phenylthiazolinones non-redox type 5-LOX inhibitors. In this study, binding pocket of 5-Lipoxygenase (pdb id 3o8y) was identified by manual docking using 15-LOX (pdb id 2p0m) as a reference structure. Additionally, most of the binding site residues were found conserved in both structures. These non-redox inhibitors were then docked into the binding site of 5-LOX. To generate reliable CoMFA and CoMSIA models, atom fit data base alignment method using docked conformation of the most active compound was employed. The q(2)cv and r(2)ncv values for CoMFA model were found to be 0.549 and 0.702, respectively. The q(2)cv and r(2)ncv values for the selected CoMSIA model comprised four descriptors steric, electrostatic, hydrophobic and hydrogen bond donor fields were found to be 0.535 and 0.951, respectively. Obtained results showed that our generated model was statistically reliable. Furthermore, an external test set validates the reliability of the predicted model by calculating r(2)pred i.e.0.787 and 0.571 for CoMFA and CoMSIA model, respectively. 3D contour maps generated from CoMFA and CoMSIA models were utilized to determine the key structural features of ligands responsible for biological activities. The applied protocol will be helpful to design more potent and selective inhibitors of 5-LOX. PMID:27044904

  3. Quantitative structure-activity relationships for organophosphates binding to acetylcholinesterase.

    PubMed

    Ruark, Christopher D; Hack, C Eric; Robinson, Peter J; Anderson, Paul E; Gearhart, Jeffery M

    2013-02-01

    Organophosphates are a group of pesticides and chemical warfare nerve agents that inhibit acetylcholinesterase, the enzyme responsible for hydrolysis of the excitatory neurotransmitter acetylcholine. Numerous structural variants exist for this chemical class, and data regarding their toxicity can be difficult to obtain in a timely fashion. At the same time, their use as pesticides and military weapons is widespread, which presents a major concern and challenge in evaluating human toxicity. To address this concern, a quantitative structure-activity relationship (QSAR) was developed to predict pentavalent organophosphate oxon human acetylcholinesterase bimolecular rate constants. A database of 278 three-dimensional structures and their bimolecular rates was developed from 15 peer-reviewed publications. A database of simplified molecular input line entry notations and their respective acetylcholinesterase bimolecular rate constants are listed in Supplementary Material, Table I. The database was quite diverse, spanning 7 log units of activity. In order to describe their structure, 675 molecular descriptors were calculated using AMPAC 8.0 and CODESSA 2.7.10. Orthogonal projection to latent structures regression, bootstrap leave-random-many-out cross-validation and y-randomization were used to develop an externally validated consensus QSAR model. The domain of applicability was assessed by the William's plot. Six external compounds were outside the warning leverage indicating potential model extrapolation. A number of compounds had residuals >2 or <-2, indicating potential outliers or activity cliffs. The results show that the HOMO-LUMO energy gap contributed most significantly to the binding affinity. A mean training R (2) of 0.80, a mean test set R (2) of 0.76 and a consensus external test set R (2) of 0.66 were achieved using the QSAR. The training and external test set RMSE values were found to be 0.76 and 0.88. The results suggest that this QSAR model can be used in

  4. On the number of EINECS compounds that can be covered by (Q)SAR models for acute toxicity.

    PubMed

    Zvinavashe, Elton; Murk, Albertinka J; Rietjens, Ivonne M C M

    2009-01-10

    The new EU legislation for managing chemicals called REACH aims to fill in gaps in toxicity information that exist for the chemicals listed on the European Inventory of Existing Chemical Substances (EINECS). REACH advocates the use of alternatives to animal experimentation including, amongst others, (quantitative) structure-activity relationship models [(Q)SARs] to help fill in the toxicity data gaps. The aim of the present study was to provide a science-based estimate of the number of EINECS compounds that can be covered by (Q)SAR models for acute toxicity. Using the ECOSAR software, 54% of the 100196 EINECS chemicals were classified into 49 classes that can be potentially covered by (Q)SAR models. The largest proportion of the classified compounds (40% of the EINECS list) falls into the classes of non-polar and polar narcotics. Compounds that were not classified include, for example, fish oils, botanical and animal extracts, and crude oil distillates. With rapid improvements in analytical tools, the number of EINECS compounds for which toxicity evaluations may be based on (Q)SAR approaches may be extended by further developing the method recently developed for the safety assessment of natural flavor complexes used as ingredients in food. This method is based on identification of the individual components in a mixture, and judgment of the safety of these identified individual compounds using toxicity information on structurally similar congeners in the respective classes. Such (Q)SAR approaches may be applied to an additional 2938 EINECS compounds, representing botanical and animal extracts, leading to a total estimate of 57% of the EINECS compounds for which (Q)SAR-based approaches may assist in their safety assessment. It is concluded that, despite the fact that individual (Q)SARs may often each cover only a limited number, i.e. less than 1%, of the EINECS compounds, the potential for applying (Q)SAR approaches for safety assessment of EINECS compounds may prove

  5. Using particle swarms for the development of QSAR models based on K-nearest neighbor and kernel regression.

    PubMed

    Cedeño, Walter; Agrafiotis, Dimitris K

    2003-01-01

    We describe the application of particle swarms for the development of quantitative structure-activity relationship (QSAR) models based on k-nearest neighbor and kernel regression. Particle swarms is a population-based stochastic search method based on the principles of social interaction. Each individual explores the feature space guided by its previous success and that of its neighbors. Success is measured using leave-one-out (LOO) cross validation on the resulting model as determined by k-nearest neighbor kernel regression. The technique is shown to compare favorably to simulated annealing using three classical data sets from the QSAR literature. PMID:13677491

  6. Flow network QSAR for the prediction of physicochemical properties by mapping an electrical resistance network onto a chemical reaction poset.

    PubMed

    Ivanciuc, Ovidiu; Ivanciuc, Teodora; Klein, Douglas J

    2013-06-01

    Usual quantitative structure-activity relationship (QSAR) models are computed from unstructured input data, by using a vector of molecular descriptors for each chemical in the dataset. Another alternative is to consider the structural relationships between the chemical structures, such as molecular similarity, presence of certain substructures, or chemical transformations between compounds. We defined a class of network-QSAR models based on molecular networks induced by a sequence of substitution reactions on a chemical structure that generates a partially ordered set (or poset) oriented graph that may be used to predict various molecular properties with quantitative superstructure-activity relationships (QSSAR). The network-QSAR interpolation models defined on poset graphs, namely average poset, cluster expansion, and spline poset, were tested with success for the prediction of several physicochemical properties for diverse chemicals. We introduce the flow network QSAR, a new poset regression model in which the dataset of chemicals, represented as a reaction poset, is transformed into an oriented network of electrical resistances in which the current flow results in a potential at each node. The molecular property considered in the QSSAR model is represented as the electrical potential, and the value of this potential at a particular node is determined by the electrical resistances assigned to each edge and by a system of batteries. Each node with a known value for the molecular property is attached to a battery that sets the potential on that node to the value of the respective molecular property, and no external battery is attached to nodes from the prediction set, representing chemicals for which the values of the molecular property are not known or are intended to be predicted. The flow network QSAR algorithm determines the values of the molecular property for the prediction set of molecules by applying Ohm's law and Kirchhoff's current law to the poset

  7. Structure-activity relationship investigations of leishmanicidal N-benzylcytisine derivatives.

    PubMed

    Turabekova, Malakhat A; Vinogradova, Valentina I; Werbovetz, Karl A; Capers, Jeffrey; Rasulev, Bakhtiyor F; Levkovich, Mikhail G; Rakhimov, Shukhrat B; Abdullaev, Nasrulla D

    2011-07-01

    In vitro leishmanicidal activity of 16 N-benzylcytisine derivatives has been evaluated using Leishmania donovani axenic amastigotes. In general, halogen (bromo-, chloro-) derivatives appeared to be more toxic against parasites than their parent compounds. Quantum-chemical calculations helped to recognize certain patterns in the structure of frontier orbitals related to bioactivity of compounds. Thus, the presence of halogen atom is shown to have a significant effect on both distribution and the energy of LUMOs thereby on potent activity that was also confirmed by Quantitative-Structure Activity Relationship (QSAR) analysis. Experimentally and theoretically observed structure-cytotoxicity relationships are described. PMID:21457471

  8. eCounterscreening: using QSAR predictions to prioritize testing for off-target activities and setting the balance between benefit and risk.

    PubMed

    Sheridan, Robert P; McMasters, Daniel R; Voigt, Johannes H; Wildey, Mary Jo

    2015-02-23

    During drug development, compounds are tested against counterscreens, a panel of off-target activities that would be undesirable for a drug to have. Testing every compound against every counterscreen is generally too costly in terms of time and money, and we need to find a rational way of prioritizing counterscreen testing. Here we present the eCounterscreening paradigm, wherein predictions from QSAR models for counterscreen activity are used to generate a recommendation as to whether a specific compound in a specific project should be tested against a specific counterscreen. The rules behind the recommendations, which can be summarized in a risk-benefit plot specific for a counterscreen/project combination, are based on a previously assembled database of prospective QSAR predictions. The recommendations require two user-defined cutoffs: the level of activity in a specific counterscreen that is considered undesirable and the level of risk the chemist is willing to accept that an undesired counterscreen activity will go undetected. We demonstrate in a simulated prospective experiment that eCounterscreening can be used to postpone a large fraction of counterscreen testing and still have an acceptably low risk of undetected counterscreen activity. PMID:25551659

  9. 3D QSAR of aminophenyl benzamide derivatives as histone deacetylase inhibitors.

    PubMed

    Mahipal; Tanwar, Om Prakash; Karthikeyan, C; Moorthy, N S Hari Narayana; Trivedi, Piyush

    2010-09-01

    The article describes the development of a robust pharmacophore model and the investigation of structure activity relationship analysis of 48 aminophenyl benzamide derivatives reported for Histone Deacetylase (HDAC) inhibition using PHASE module of Schrodinger software. A five point pharmacophore model consisting of two aromatic rings (R), two hydrogen bond donors (D) and one hydrogen bond acceptor (A) with discrete geometries as pharmacophoric features was developed and the generated pharmacophore model was used to derive a predictive atom-based 3D QSAR model for the studied dataset. The obtained 3D QSAR model has an excellent correlation coefficient value (r(2)=0.99) along with good statistical significance as shown by high Fisher ratio (F=631.80). The model also exhibits good predictive power confirmed by the high value of cross validated correlation coefficient (q(2) = 0.85). The QSAR model suggests that hydrophobic character is crucial for the HDAC inhibitory activity exhibited by these compounds and inclusion of hydrophobic substituents will enhance the HDAC inhibition. In addition to the hydrophobic character, hydrogen bond donating groups positively contributes to the HDAC inhibition whereas electron withdrawing groups has a negative influence in HDAC inhibitory potency. The findings of the QSAR study provide a set of guidelines for designing compounds with better HDAC inhibitory potency. PMID:20977417

  10. QSAR and molecular docking studies on oxindole derivatives as VEGFR-2 tyrosine kinase inhibitors.

    PubMed

    Kang, Cong-Min; Liu, Dong-Qing; Zhao, Xu-Hao; Dai, Ying-Jie; Cheng, Jia-Gao; Lv, Ying-Tao

    2016-01-01

    The three-dimensional quantitative structure-activity relationships (3D-QSAR) were established for 30 oxindole derivatives as vascular endothelial growth factor receptor-2 (VEGFR-2) tyrosine kinase inhibitors by using comparative molecular field analysis (CoMFA) and comparative similarity indices analysis comparative molecular similarity indices analysis (CoMSIA) techniques. With the CoMFA model, the cross-validated value (q(2)) was 0.777, the non-cross-validated value (R(2)) was 0.987, and the external cross-validated value ([Formula: see text]) was 0.72. And with the CoMSIA model, the corresponding q(2), R(2) and [Formula: see text] values were 0.710, 0.988 and 0.78, respectively. Docking studies were employed to bind the inhibitors into the active site to determine the probable binding conformation. The binding mode obtained by molecular docking was in good agreement with the 3D-QSAR results. Based on the QSAR models and the docking binding mode, a set of new VEGFR-2 tyrosine kinase inhibitors were designed, which showed excellent predicting inhibiting potencies. The result revealed that both QSAR models have good predictive capability to guide the design and structural modification of homologic compounds. It is also helpful for further research and development of new VEGFR-2 tyrosine kinase inhibitors. PMID:26416217

  11. In Silico Study of In Vitro GPCR Assays by QSAR Modeling.

    PubMed

    Mansouri, Kamel; Judson, Richard S

    2016-01-01

    The US EPA's ToxCast program is screening thousands of chemicals of environmental interest in hundreds of in vitro high-throughput screening (HTS) assays. One goal is to prioritize chemicals for more detailed analyses based on activity in assays that target molecular initiating events (MIEs) of adverse outcome pathways (AOPs). However, the chemical space of interest for environmental exposure is much wider than ToxCast's chemical library. In silico methods such as quantitative structure-activity relationships (QSARs) are proven and cost-effective approaches to predict biological activity for untested chemicals. However, empirical data is needed to build and validate QSARs. ToxCast has developed datasets for about 2000 chemicals ideal for training and testing QSAR models. The overall goal of the present work was to develop QSAR models to fill the data gaps in larger environmental chemical lists. The specific aim of the current work was to build QSAR models for 18 G-protein-coupled receptor (GPCR) assays, part of the aminergic family. Two QSAR modeling strategies were adopted: classification models were developed to separate chemicals into active/non-active classes, and then regression models were built to predict the potency values of the bioassays for the active chemicals. Multiple software programs were used to calculate constitutional, topological, and substructural molecular descriptors from two-dimensional (2D) chemical structures. Model-fitting methods included PLSDA (partial least square discriminant analysis), SVMs (support vector machines), kNNs (k-nearest neighbors), and PLSs (partial least squares). Genetic algorithms (GAs) were applied as a variable selection technique to select the most predictive molecular descriptors for each assay. N-fold cross-validation (CV) coupled with multi-criteria decision-making fitting criteria was used to evaluate the models. Finally, the models were applied to make predictions within the established chemical space limits

  12. Development of quantitative structure activity relationships for the binding affinity of methoxypyridinium cations for human acetylcholinesterase.

    PubMed

    Morrill, Jason A; Topczewski, Joseph J; Lodge, Alexander M; Yasapala, Nilanthi; Quinn, Daniel M

    2015-11-01

    Among the most toxic substances known are the organophosphorus (OP) compounds used as pesticides and chemical warfare agents. Owing to their high toxicity there is a number of efforts underway to develop effective therapies for OP agent exposure. To date all therapies in use treat inhibited acetylcholinesterase (AChE), but are ineffective for the treatment of inhibited AChE, which has undergone a subsequent hydrolysis process, referred to as aging. Toward developing a therapy for treating victims of OP intoxication in the aged state we have developed Quantitative Structure-Activity Relationships (QSARs) based on the AM1 semiempirical quantum mechanical method using the program, CODESSA (COmprehensive Descriptors for Structural and Statistical Analysis). Using this methodology we obtained a multiple correlation QSAR equation which gave R(2)=0.9359 for a random training set of 38 ligands and R(2)=0.9236 for prediction on a random test set of 9 ligands. PMID:26454505

  13. A quantitative structure-activity relationship model for radical scavenging activity of flavonoids.

    PubMed

    Om, A; Kim, J H

    2008-03-01

    A quantitative structure-activity relationship (QSAR) study has been carried out for a training set of 29 flavonoids to correlate and predict the 1,1-diphenyl-2-picrylhydrazyl radical scavenging activity (RSA) values obtained from published data. Genetic algorithm and multiple linear regression were employed to select the descriptors and to generate the best prediction model that relates the structural features to the RSA activities using (1) three-dimensional (3D) Dragon (TALETE srl, Milan, Italy) descriptors and (2) semi-empirical descriptor calculations. The predictivity of the models was estimated by cross-validation with the leave-one-out method. The result showed that a significant improvement of the statistical indices was obtained by deleting outliers. Based on the data for the compounds used in this study, our results suggest a QSAR model of RSA that is based on the following descriptors: 3D-Morse, WHIM, and GETAWAY. Therefore, satisfactory relationships between RSA and the semi-empirical descriptors were found, demonstrating that the energy of the highest occupied molecular orbital, total energy, and energy of heat of formation contributed more significantly than all other descriptors. PMID:18361735

  14. Modeling Liver-Related Adverse Effects of Drugs Using kNN QSAR Method

    PubMed Central

    Rodgers, Amie D.; Zhu, Hao; Fourches, Dennis; Rusyn, Ivan; Tropsha, Alexander

    2010-01-01

    Adverse effects of drugs (AEDs) continue to be a major cause of drug withdrawals both in development and post-marketing. While liver-related AEDs are a major concern for drug safety, there are few in silico models for predicting human liver toxicity for drug candidates. We have applied the Quantitative Structure Activity Relationship (QSAR) approach to model liver AEDs. In this study, we aimed to construct a QSAR model capable of binary classification (active vs. inactive) of drugs for liver AEDs based on chemical structure. To build QSAR models, we have employed an FDA spontaneous reporting database of human liver AEDs (elevations in activity of serum liver enzymes), which contains data on approximately 500 approved drugs. Approximately 200 compounds with wide clinical data coverage, structural similarity and balanced (40/60) active/inactive ratio were selected for modeling and divided into multiple training/test and external validation sets. QSAR models were developed using the k nearest neighbor method and validated using external datasets. Models with high sensitivity (>73%) and specificity (>94%) for prediction of liver AEDs in external validation sets were developed. To test applicability of the models, three chemical databases (World Drug Index, Prestwick Chemical Library, and Biowisdom Liver Intelligence Module) were screened in silico and the validity of predictions was determined, where possible, by comparing model-based classification with assertions in publicly available literature. Validated QSAR models of liver AEDs based on the data from the FDA spontaneous reporting system can be employed as sensitive and specific predictors of AEDs in pre-clinical screening of drug candidates for potential hepatotoxicity in humans. PMID:20192250

  15. Neural network-based QSAR and insecticide discovery: spinetoram.

    PubMed

    Sparks, Thomas C; Crouse, Gary D; Dripps, James E; Anzeveno, Peter; Martynow, Jacek; Deamicis, Carl V; Gifford, James

    2008-01-01

    Improvements in the efficacy and spectrum of the spinosyns, novel fermentation derived insecticide, has long been a goal within Dow AgroSciences. As large and complex fermentation products identifying specific modifications to the spinosyns likely to result in improved activity was a difficult process, since most modifications decreased the activity. A variety of approaches were investigated to identify new synthetic directions for the spinosyn chemistry including several explorations of the quantitative structure activity relationships (QSAR) of spinosyns, which initially were unsuccessful. However, application of artificial neural networks (ANN) to the spinosyn QSAR problem identified new directions for improved activity in the chemistry, which subsequent synthesis and testing confirmed. The ANN-based analogs coupled with other information on substitution effects resulting from spinosyn structure activity relationships lead to the discovery of spinetoram (XDE-175). Launched in late 2007, spinetoram provides both improved efficacy and an expanded spectrum while maintaining the exceptional environmental and toxicological profile already established for the spinosyn chemistry. PMID:18344004

  16. QSAR as a random event: modeling of nanoparticles uptake in PaCa2 cancer cells.

    PubMed

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

    2013-06-01

    Quantitative structure-property/activity relationships (QSPRs/QSARs) are a tool to predict various endpoints for various substances. The "classic" QSPR/QSAR analysis is based on the representation of the molecular structure by the molecular graph. However, simplified molecular input-line entry system (SMILES) gradually becomes most popular representation of the molecular structure in the databases available on the Internet. Under such circumstances, the development of molecular descriptors calculated directly from SMILES becomes attractive alternative to "classic" descriptors. The CORAL software (http://www.insilico.eu/coral) is provider of SMILES-based optimal molecular descriptors which are aimed to correlate with various endpoints. We analyzed data set on nanoparticles uptake in PaCa2 pancreatic cancer cells. The data set includes 109 nanoparticles with the same core but different surface modifiers (small organic molecules). The concept of a QSAR as a random event is suggested in opposition to "classic" QSARs which are based on the only one distribution of available data into the training and the validation sets. In other words, five random splits into the "visible" training set and the "invisible" validation set were examined. The SMILES-based optimal descriptors (obtained by the Monte Carlo technique) for these splits are calculated with the CORAL software. The statistical quality of all these models is good. PMID:23566368

  17. QSARs for estimating intrinsic hepatic clearance of organic chemicals in humans.

    PubMed

    Pirovano, Alessandra; Brandmaier, Stefan; Huijbregts, Mark A J; Ragas, Ad M J; Veltman, Karin; Hendriks, A Jan

    2016-03-01

    Quantitative structure-activity relationships (QSARs) were developed to predict the in vitro clearance (CLINT) of xenobiotics metabolised in human hepatocytes (118 compounds) and microsomes (115 compounds). Clearance values were gathered from the scientific literature and multiple linear models were built and validated selecting at most 6 predictors from a pool of over 2000 potential molecular descriptors. For the hepatocytes QSAR, the explained variance (Radj(2)) was 67% and the predictive ability (Rext(2)) was 62%. For the microsomes QSAR, Radj(2) was 50% and Rext(2) 30%. For both liver assays, the most important descriptor relates to electronic properties of the compound. Functional groups of fragments were useful to identify specific compounds that have a deviating reaction rate compared to the others, such as polychlorobiphenyls (PCBs) and organic amides which were poorly metabolised by hepatocytes and microsomes, respectively. For hepatocytes, clearance was predominantly determined by electronic characteristics, while size and shape characteristics were less important and partitioning properties were absent. This may suggest that uptake across the membrane and enzyme binding are not rate-limiting steps. Particularly for hepatocytes the QSAR statistics are encouraging, allowing application of the outcomes in in vitro to in vivo extrapolation. PMID:26874337

  18. Predictive QSAR modeling of phosphodiesterase 4 inhibitors.

    PubMed

    Kovalishyn, Vasyl; Tanchuk, Vsevolod; Charochkina, Larisa; Semenuta, Ivan; Prokopenko, Volodymyr

    2012-02-01

    A series of diverse organic compounds, phosphodiesterase type 4 (PDE-4) inhibitors, have been modeled using a QSAR-based approach. 48 QSAR models were compared by following the same procedure with different combinations of descriptors and machine learning methods. QSAR methodologies used random forests and associative neural networks. The predictive ability of the models was tested through leave-one-out cross-validation, giving a Q² = 0.66-0.78 for regression models and total accuracies Ac=0.85-0.91 for classification models. Predictions for the external evaluation sets obtained accuracies in the range of 0.82-0.88 (for active/inactive classifications) and Q² = 0.62-0.76 for regressions. The method showed itself to be a potential tool for estimation of IC₅₀ of new drug-like candidates at early stages of drug development. PMID:22023934

  19. CURRENT PRACTICES IN QSAR DEVELOPMENT AND APPLICATIONS

    EPA Science Inventory

    Current Practices in QSAR Development and Applications

    Although it is commonly assumed that the structure and properties of a single chemical determines its activity in a particular biological system, it is only through study of how biological activity varies with changes...

  20. Applying quantitative structure-activity relationship approaches to nanotoxicology: current status and future potential.

    PubMed

    Winkler, David A; Mombelli, Enrico; Pietroiusti, Antonio; Tran, Lang; Worth, Andrew; Fadeel, Bengt; McCall, Maxine J

    2013-11-01

    The potential (eco)toxicological hazard posed by engineered nanoparticles is a major scientific and societal concern since several industrial sectors (e.g. electronics, biomedicine, and cosmetics) are exploiting the innovative properties of nanostructures resulting in their large-scale production. Many consumer products contain nanomaterials and, given their complex life-cycle, it is essential to anticipate their (eco)toxicological properties in a fast and inexpensive way in order to mitigate adverse effects on human health and the environment. In this context, the application of the structure-toxicity paradigm to nanomaterials represents a promising approach. Indeed, according to this paradigm, it is possible to predict toxicological effects induced by chemicals on the basis of their structural similarity with chemicals for which toxicological endpoints have been previously measured. These structure-toxicity relationships can be quantitative or qualitative in nature and they can predict toxicological effects directly from the physicochemical properties of the entities (e.g. nanoparticles) of interest. Therefore, this approach can aid in prioritizing resources in toxicological investigations while reducing the ethical and monetary costs that are related to animal testing. The purpose of this review is to provide a summary of recent key advances in the field of QSAR modelling of nanomaterial toxicity, to identify the major gaps in research required to accelerate the use of quantitative structure-activity relationship (QSAR) methods, and to provide a roadmap for future research needed to achieve QSAR models useful for regulatory purposes. PMID:23165187

  1. Structure-activity relationship of genotoxic polycyclic aromatic nitro compounds: Further evidence for the importance of hydrophobicity and molecular orbital energies in genetic toxicity

    SciTech Connect

    Debnath, A.K.; Hansch, C. )

    1992-01-01

    A quantitative structure-activity relationship (QSAR) has been formulated for 15 polycyclic aromatic nitro compounds acting on E. coli PQ37. Upon damage of DNA by these substances [beta]-galactosidase is induced and can be easily assayed colorimetrically, hence, this is a short-term test for mutagenicity. The QSAR (log SOSIP = 1.07 log P - 1.57 e[sub LUMO] - 6.41) is strikingly similar to that found earlier with nitroaromatics acting in the Ames test (TA100) and differs significantly for that found using TA98 organisms. The QSAR brings out in a unique manner the underlying similarity in the two test systems. 24 refs., 2 tabs.

  2. Structure-based approach to pharmacophore identification, in silico screening, and three-dimensional quantitative structure-activity relationship studies for inhibitors of Trypanosoma cruzi dihydrofolate reductase function

    SciTech Connect

    Schormann, N.; Senkovich, O.; Walker, K.; Wright, D.L.; Anderson, A.C.; Rosowsky, A.; Ananthan, S.; Shinkre, B.; Velu, S.; Chattopadhyay, D.

    2009-07-10

    We have employed a structure-based three-dimensional quantitative structure-activity relationship (3D-QSAR) approach to predict the biochemical activity for inhibitors of T. cruzi dihydrofolate reductase-thymidylate synthase (DHFR-TS). Crystal structures of complexes of the enzyme with eight different inhibitors of the DHFR activity together with the structure in the substrate-free state (DHFR domain) were used to validate and refine docking poses of ligands that constitute likely active conformations. Structural information from these complexes formed the basis for the structure-based alignment used as input for the QSAR study. Contrary to indirect ligand-based approaches the strategy described here employs a direct receptor-based approach. The goal is to generate a library of selective lead inhibitors for further development as antiparasitic agents. 3D-QSAR models were obtained for T. cruzi DHFR-TS (30 inhibitors in learning set) and human DHFR (36 inhibitors in learning set) that show a very good agreement between experimental and predicted enzyme inhibition data. For crossvalidation of the QSAR model(s), we have used the 10% leave-one-out method. The derived 3D-QSAR models were tested against a few selected compounds (a small test set of six inhibitors for each enzyme) with known activity, which were not part of the learning set, and the quality of prediction of the initial 3D-QSAR models demonstrated that such studies are feasible. Further refinement of the models through integration of additional activity data and optimization of reliable docking poses is expected to lead to an improved predictive ability.

  3. QSAR of 2-(4-methylsulphonylphenyl)pyrimidine derivatives as cyclooxygenase-2 inhibitors: simple structural fragments as potential modulators of activity.

    PubMed

    Sharma, B K; Singh, P; Pilania, P; Shekhawat, M; Prabhakar, Y S

    2012-04-01

    The cyclooxygenase-2 (COX-2) inhibitory activity of 2-(4-methylsulphonylphenyl)pyrimidine derivatives has been quantitatively analyzed in terms of Dragon descriptors. The derived QSAR models have provided rationales to explain the activity of titled derivatives. The descriptors (Me, Mp, GATS1p and GATS5p) identified in CP-MLR analysis have highlighted the role of atomic properties, such as Sanderson electronegativity and polarizability, to explain the inhibitory activity. Additionally, prevalence of aromatic ether functionality (descriptor nRORPh) and certain structural fragments (number of Me groups, C-001; number of H attached to heteroatom, H-050 and number of H attached to α-C, H-051) in a molecular structure are helpful to rationalize the COX-2 inhibitory activity of pyrimidine derivatives. The partial least square (PLS) analysis has also confirmed the dominance of information content of CP-MLR-identified descriptors for modelling the activity. PMID:21679051

  4. A Quantitative Structure Activity Relationship for acute oral toxicity of pesticides on rats: Validation, domain of application and prediction.

    PubMed

    Hamadache, Mabrouk; Benkortbi, Othmane; Hanini, Salah; Amrane, Abdeltif; Khaouane, Latifa; Si Moussa, Cherif

    2016-02-13

    Quantitative Structure Activity Relationship (QSAR) models are expected to play an important role in the risk assessment of chemicals on humans and the environment. In this study, we developed a validated QSAR model to predict acute oral toxicity of 329 pesticides to rats because a few QSAR models have been devoted to predict the Lethal Dose 50 (LD50) of pesticides on rats. This QSAR model is based on 17 molecular descriptors, and is robust, externally predictive and characterized by a good applicability domain. The best results were obtained with a 17/9/1 Artificial Neural Network model trained with the Quasi Newton back propagation (BFGS) algorithm. The prediction accuracy for the external validation set was estimated by the Q(2)ext and the root mean square error (RMS) which are equal to 0.948 and 0.201, respectively. 98.6% of external validation set is correctly predicted and the present model proved to be superior to models previously published. Accordingly, the model developed in this study provides excellent predictions and can be used to predict the acute oral toxicity of pesticides, particularly for those that have not been tested as well as new pesticides. PMID:26513561

  5. Assessment of the Potential Biological Activity of Low Molecular Weight Metabolites of Freshwater Macrophytes with QSAR

    PubMed Central

    Fedorova, Elena V.; Krylova, Julia V.

    2016-01-01

    The paper focuses on the assessment of the spectrum of biological activities (antineoplastic, anti-inflammatory, antifungal, and antibacterial) with PASS (Prediction of Activity Spectra for Substances) for the major components of three macrophytes widespread in the Holarctic species of freshwater, emergent macrophyte with floating leaves, Nuphar lutea (L.) Sm., and two species of submergent macrophyte groups, Ceratophyllum demersum L. and Potamogeton obtusifolius (Mert. et Koch), for the discovery of their ecological and pharmacological potential. The predicted probability of anti-inflammatory or antineoplastic activities above 0.8 was observed for twenty compounds. The same compounds were also characterized by high probability of antifungal and antibacterial activity. Six metabolites, namely, hexanal, pentadecanal, tetradecanoic acid, dibutyl phthalate, hexadecanoic acid, and manool, were a part of the major components of all three studied plants, indicating their high ecological significance and a certain universalism in their use by various species of water plants for the implementation of ecological and biochemical functions. This report underlines the role of identified compounds not only as important components in regulation of biochemical and metabolic pathways and processes in aquatic ecological systems, but also as potential pharmacological agents in the fight against different diseases. PMID:27200207

  6. Assessment of the Potential Biological Activity of Low Molecular Weight Metabolites of Freshwater Macrophytes with QSAR.

    PubMed

    Kurashov, Evgeny A; Fedorova, Elena V; Krylova, Julia V; Mitrukova, Galina G

    2016-01-01

    The paper focuses on the assessment of the spectrum of biological activities (antineoplastic, anti-inflammatory, antifungal, and antibacterial) with PASS (Prediction of Activity Spectra for Substances) for the major components of three macrophytes widespread in the Holarctic species of freshwater, emergent macrophyte with floating leaves, Nuphar lutea (L.) Sm., and two species of submergent macrophyte groups, Ceratophyllum demersum L. and Potamogeton obtusifolius (Mert. et Koch), for the discovery of their ecological and pharmacological potential. The predicted probability of anti-inflammatory or antineoplastic activities above 0.8 was observed for twenty compounds. The same compounds were also characterized by high probability of antifungal and antibacterial activity. Six metabolites, namely, hexanal, pentadecanal, tetradecanoic acid, dibutyl phthalate, hexadecanoic acid, and manool, were a part of the major components of all three studied plants, indicating their high ecological significance and a certain universalism in their use by various species of water plants for the implementation of ecological and biochemical functions. This report underlines the role of identified compounds not only as important components in regulation of biochemical and metabolic pathways and processes in aquatic ecological systems, but also as potential pharmacological agents in the fight against different diseases. PMID:27200207

  7. Synthesis, Biological Evaluation and QSAR Studies of Newer Isoxazole Derivatives.

    PubMed

    Asirvatham, Sahaya; Mahajan, Supriya

    2015-01-01

    A series of newer 3-(4'-methoxyphenyl)-5-substituted phenylisoxazoles derivatives have been synthesized by reacting hydroxylamine hydrochloride with chalcones. The chalcones were formed by reacting different aromatic aldehydes with 4-methoxyacetophenone in presence of aqueos potassium hydroxide (KOH). The purity of all the synthesized compounds was checked by recording their melting points and the retention Factors (Rf) values from thin layer chromatography. The structures of the compounds were characterized by recording their infrared (IR) spectra and confirmed by recording their nuclear magnetic resonance ((1)H NMR) spectra. The acute toxicity study was carried out on all the synthesized compounds and they were screened for their antiinflammatory activity by carrageenan induced rat paw edema method. Anti-inflammatory studies showed statistically significant activity when compared to the control, indomethacin. The two most potent compounds giving good anti-inflammatory activity were further evaluated for their antiulcer activity. The compounds were subjected to quantitative structure activity relationships (QSAR) studies. A close correlation between the observed and the predicted anti-inflammatory activity (Log % inhibition) for the compounds indicated the development of the best QSAR model. The synthesized compounds were found to be non-ulcerogenic as compared to the standard, aspirin. PMID:26265199

  8. Structural and Physico-Chemical Interpretation (SPCI) of QSAR Models and Its Comparison with Matched Molecular Pair Analysis.

    PubMed

    Polishchuk, Pavel; Tinkov, Oleg; Khristova, Tatiana; Ognichenko, Ludmila; Kosinskaya, Anna; Varnek, Alexandre; Kuz'min, Victor

    2016-08-22

    This paper describes the Structural and Physico-Chemical Interpretation (SPCI) approach, which is an extension of a recently reported method for interpretation of quantitative structure-activity relationship (QSAR) models. This approach can efficiently be used to reveal structural motifs and the major physicochemical factors affecting the investigated properties. Its efficacy was demonstrated both on the classical Free-Wilson data set and on several data sets with different end points (permeability of the blood-brain barrier, fibrinogen receptor antagonists, acute oral toxicity). Structure-activity patterns extracted from QSAR models with SPCI were in good correspondence with experimentally observed relationships and molecular docking, regardless of the machine learning method used. Comparison of SPCI with the matched molecular pair (MMP) method clearly shows an advantage of our approach over MMP, especially for small or structurally diverse data sets. The developed approach has been implemented in the SPCI software tool with a graphical user interface, which is publicly available at http://qsar4u.com/pages/sirms_qsar.php . PMID:27419846

  9. Benefits of statistical molecular design, covariance analysis, and reference models in QSAR: a case study on acetylcholinesterase.

    PubMed

    Andersson, C David; Hillgren, J Mikael; Lindgren, Cecilia; Qian, Weixing; Akfur, Christine; Berg, Lotta; Ekström, Fredrik; Linusson, Anna

    2015-03-01

    Scientific disciplines such as medicinal- and environmental chemistry, pharmacology, and toxicology deal with the questions related to the effects small organic compounds exhort on biological targets and the compounds' physicochemical properties responsible for these effects. A common strategy in this endeavor is to establish structure-activity relationships (SARs). The aim of this work was to illustrate benefits of performing a statistical molecular design (SMD) and proper statistical analysis of the molecules' properties before SAR and quantitative structure-activity relationship (QSAR) analysis. Our SMD followed by synthesis yielded a set of inhibitors of the enzyme acetylcholinesterase (AChE) that had very few inherent dependencies between the substructures in the molecules. If such dependencies exist, they cause severe errors in SAR interpretation and predictions by QSAR-models, and leave a set of molecules less suitable for future decision-making. In our study, SAR- and QSAR models could show which molecular sub-structures and physicochemical features that were advantageous for the AChE inhibition. Finally, the QSAR model was used for the prediction of the inhibition of AChE by an external prediction set of molecules. The accuracy of these predictions was asserted by statistical significance tests and by comparisons to simple but relevant reference models. PMID:25351962

  10. Multiple receptor conformation docking, dock pose clustering and 3D QSAR studies on human poly(ADP-ribose) polymerase-1 (PARP-1) inhibitors.

    PubMed

    Fatima, Sabiha; Jatavath, Mohan Babu; Bathini, Raju; Sivan, Sree Kanth; Manga, Vijjulatha

    2014-10-01

    Poly(ADP-ribose) polymerase-1 (PARP-1) functions as a DNA damage sensor and signaling molecule. It plays a vital role in the repair of DNA strand breaks induced by radiation and chemotherapeutic drugs; inhibitors of this enzyme have the potential to improve cancer chemotherapy or radiotherapy. Three-dimensional quantitative structure activity relationship (3D QSAR) models were developed using comparative molecular field analysis, comparative molecular similarity indices analysis and docking studies. A set of 88 molecules were docked into the active site of six X-ray crystal structures of poly(ADP-ribose)polymerase-1 (PARP-1), by a procedure called multiple receptor conformation docking (MRCD), in order to improve the 3D QSAR models through the analysis of binding conformations. The docked poses were clustered to obtain the best receptor binding conformation. These dock poses from clustering were used for 3D QSAR analysis. Based on MRCD and QSAR information, some key features have been identified that explain the observed variance in the activity. Two receptor-based QSAR models were generated; these models showed good internal and external statistical reliability that is evident from the [Formula: see text], [Formula: see text] and [Formula: see text]. The identified key features enabled us to design new PARP-1 inhibitors. PMID:25046176

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

    PubMed Central

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

    2009-01-01

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

  12. Investigation of antigen-antibody interactions of sulfonamides with a monoclonal antibody in a fluorescence polarization immunoassay using 3D-QSAR models

    Technology Transfer Automated Retrieval System (TEKTRAN)

    A three-dimensional quantitative structure-activity relationship (3D-QSAR) model of sulfonamide analogs binding a monoclonal antibody (MAbSMR) produced against sulfamerazine was carried out by Distance Comparison (DISCOtech), comparative molecular field analysis (CoMFA), and comparative molecular si...

  13. Quantitative structure-activity relationships for weak acid respiratory uncouplers to Vibrio fisheri

    SciTech Connect

    Schultz, T.W.; Cronin, M.T.D.

    1997-02-01

    Acute toxicity values of 16 organic compounds thought to elicit their response via the weak acid respiratory uncoupling mechanism of toxic action were secured from the literature. Regression analysis of toxicities revealed that a measured 5-min V. fisheri potency value can be used as a surrogate for the 30-min value. Regression analysis of toxicity versus hydrophobicity, measured as the 1-octanol/water partition coefficient (log K{sub ow}), was used to formulate a quantitative structure-activity relationship (QSAR). The equation log pT{sub 30}{sup {minus}1} = 0.489(log K{sub ow}) + 0.126 was found to be a highly predictive model. This V. fisheri QSAR is statistically similar to QSARs generated from weak acid uncoupler potency data for Pimephales promelas survivability and Tetrahymena pyriformis population growth impairment. This work, therefore, suggests that the weak acid respiratory uncoupling mechanism of toxic action is present in V. fisheri, and as such is not restricted to mitochondria-containing organisms.

  14. Acute toxicity estimation by calculation--Tubifex assay and quantitative structure-activity relationships.

    PubMed

    Tichý, Milon; Rucki, Marian; Hanzlíková, Iveta; Roth, Zdenek

    2008-11-01

    A quantitative structure-activity relationship (QSAR) model dependent on log P(n - octanol/water), or log P(OW), was developed with acute toxicity index EC50, the median effective concentration measured as inhibition of movement of the oligochaeta Tubifex tubifex with 3 min exposure, EC50(Tt) (mol/L): log EC50(Tt) = -0.809 (+/-0.035) log P(OW) - 0.495 (+/-0.060), n=82, r=0.931, r2=0.867, residual standard deviation of the estimate 0.315. A learning series for the QSAR model with the oligochaete contained alkanols, alkenols, and alkynols; saturated and unsaturated aldehydes; aniline and chlorinated anilines; phenol and chlorinated phenols; and esters. Three cross-validation procedures proved the robustness and stability of QSAR models with respect to the chemical structure of compounds tested within a series of compounds used in the learning series. Predictive ability was described by q2 .801 (cross-validated r2; predicted variation estimated with cross-validation) in LSO (leave-a structurally series-out) cross-validation. PMID:18522479

  15. Evaluation of OASIS QSAR Models Using ToxCast™ in Vitro Estrogen and Androgen Receptor Binding Data and Application in an Integrated Endocrine Screening Approach

    PubMed Central

    Bhhatarai, Barun; Wilson, Daniel M.; Price, Paul S.; Marty, Sue; Parks, Amanda K.; Carney, Edward

    2016-01-01

    Background: Integrative testing strategies (ITSs) for potential endocrine activity can use tiered in silico and in vitro models. Each component of an ITS should be thoroughly assessed. Objectives: We used the data from three in vitro ToxCast™ binding assays to assess OASIS, a quantitative structure-activity relationship (QSAR) platform covering both estrogen receptor (ER) and androgen receptor (AR) binding. For stronger binders (described here as AC50 < 1 μM), we also examined the relationship of QSAR predictions of ER or AR binding to the results from 18 ER and 10 AR transactivation assays, 72 ER-binding reference compounds, and the in vivo uterotrophic assay. Methods: NovaScreen binding assay data for ER (human, bovine, and mouse) and AR (human, chimpanzee, and rat) were used to assess the sensitivity, specificity, concordance, and applicability domain of two OASIS QSAR models. The binding strength relative to the QSAR-predicted binding strength was examined for the ER data. The relationship of QSAR predictions of binding to transactivation- and pathway-based assays, as well as to in vivo uterotrophic responses, was examined. Results: The QSAR models had both high sensitivity (> 75%) and specificity (> 86%) for ER as well as both high sensitivity (92–100%) and specificity (70–81%) for AR. For compounds within the domains of the ER and AR QSAR models that bound with AC50 < 1 μM, the QSAR models accurately predicted the binding for the parent compounds. The parent compounds were active in all transactivation assays where metabolism was incorporated and, except for those compounds known to require metabolism to manifest activity, all assay platforms where metabolism was not incorporated. Compounds in-domain and predicted to bind by the ER QSAR model that were positive in ToxCast™ ER binding at AC50 < 1 μM were active in the uterotrophic assay. Conclusions: We used the extensive ToxCast™ HTS binding data set to show that OASIS ER and AR QSAR models had

  16. Neuronal nicotinic acetylcholine receptor agonists: pharmacophores, evolutionary QSAR and 3D-QSAR models.

    PubMed

    Nicolotti, Orazio; Altomare, Cosimo; Pellegrini-Calace, Marialuisa; Carotti, Angelo

    2004-01-01

    Neuronal nicotinic acetylcholine ion channel receptors (nAChRs) exist as several subtypes and are involved in a variety of functions and disorders of the central nervous system (CNS), such as Alzheimer's and Parkinson's diseases. The lack of reliable information on the 3D structure of nAChRs prompted us to focus efforts on pharmacophore and structure-affinity relationships (SAFIRs). The use of DISCO (DIStance COmparison) and Catalyst/HipHop led to the formulation of a pharmacophore that is made of three geometrically unrelated features: (i) an ammonium head involved in coulombic and/or H-bond interactions, (ii) a lone pair of a pyridine nitrogen or a carbonyl oxygen, as H-bond acceptor site, and (iii) a hydrophobic molecular region generally constituted by aliphatic cycles. The quantitative SAFIR (QSAFIR) study was carried out on about three hundred nicotinoid agonists, and coherent results were obtained from classical Hansch-type approach, 3D QSAFIRs, based on Comparative Molecular Field Analysis (CoMFA), and trade-off models generated by Multi-objective Genetic QSAR (MoQSAR), a novel evolutionary software that makes use of Genetic Programming (GP) and multi-objective optimization (MO). Within each congeneric series, Hansch-type equations revealed detrimental steric effects as the major factors modulating the receptor affinity, whereas CoMFA allowed us to merge progressively single-class models in a more global one, whose robustness was supported by crossvalidation, high prediction statistics and satisfactory predictions of the affinity data of a true external ligand set (r(2)(pred) = 0.796). Next, MoQSAR was used to analyze a data set of 58 highly active nicotinoids characterized by 56 descriptors, that are log P, MR and 54 low inter-correlated WHIM (Weighted Holistic Invariant Molecular) indices. Equivalent QSAFIR models, that represent different compromises between structural model complexity, fitting and internal model complexity, were found. Our attention was

  17. Discovery of potent adenosine A2a antagonists as potential anti-Parkinson disease agents. Non-linear QSAR analyses integrated with pharmacophore modeling.

    PubMed

    Khanfar, Mohammad A; Al-Qtaishat, Saja; Habash, Maha; Taha, Mutasem O

    2016-07-25

    Adenosine A2A receptor antagonists are of great interest in the treatment for Parkinson's disease. In this study, we combined extensive pharmacophore modeling and quantitative structure-activity relationship (QSAR) analysis to explore the structural requirements for potent Adenosine A2A antagonists. Genetic function algorithm (GFA) joined with k nearest neighbor (kNN) analyses were applied to build predictive QSAR models. Successful pharmacophores were complemented with exclusion spheres to improve their receiver operating characteristic curve (ROC) profiles. Best QSAR models and their associated pharmacophore hypotheses were validated by identification of several novel Adenosine A2A antagonist leads retrieved from the National Cancer Institute (NCI) structural database. The most potent hit illustrated IC50 value of 545.7 nM. PMID:27216633

  18. New QSAR prediction models derived from GPCR CB2-antagonistic triaryl bis-sulfone analogues by a combined molecular morphological and pharmacophoric approach.

    PubMed

    Chen, J-Z; Myint, K-Z; Xie, X-Q

    2011-01-01

    In order to build quantitative structure-activity relationship (QSAR) models for virtual screening of novel cannabinoid CB2 ligands and hit ranking selections, a new QSAR algorithm has been developed for the cannabinoid ligands, triaryl bis-sulfones, using a combined molecular morphological and pharmacophoric search approach. Both pharmacophore features and shape complementarity were considered using a number of molecular descriptors, including Surflex-Sim similarity and Unity Query fit, in addition to other molecular properties such as molecular weight, ClogP, molecular volume, molecular area, molecular polar volume, molecular polar surface area and dipole moment. Subsequently, partial least squares regression analyses were carried out to derive QSAR models linking bioactivity and the descriptors mentioned, using a training set of 25 triaryl bis-sulfones. Good prediction capability was confirmed for the best QSAR model by evaluation against a test set of a further 20 triaryl bis-sulfones. The pharmacophore and molecular shape-based QSAR scoring function now established can be used to predict the biological properties of virtual hits or untested compounds obtained from ligand-based virtual screenings. PMID:21714749

  19. QSAR studies of benzofuran/benzothiophene biphenyl derivatives as inhibitors of PTPase-1B

    PubMed Central

    Kaushik, D.; Kumar, R.; Saxena, A. K.

    2010-01-01

    Objectives: Insulin resistance is associated with a defect in protein tyrosine phosphorylation in the insulin signal transduction cascade. The PTPase enzyme dephosphorylates the active form of the insulin receptor and thus attenuates its tyrosine kinase activity, therefore, the need for a potent PTPase inhibitor exists, with the intention of which the QSAR was performed. Materials and Methods: Quantitative structure-activity relationship (QSAR) has been established on a series of 106 compounds considering 27 variables, for novel biphenyl analogs, using the SYSTAT (Version 7.0) software, for their protein tyrosine phosphatase (PTPase-1B) inhibitor activity, in order to understand the essential structural requirement for binding with the receptor. Results: Among several regression models, one per series was selected on the basis of a high correlation coefficient (r, 0.86), least standard deviation (s, 0.234), and a high value of significance for the maximum number of subjects (n, 101). Conclusions: The influence of the different physicochemical parameters of the substituents in various positions has been discussed by generating the best QSAR model using multiple regression analysis, and the information thus obtained from the present study can be used to design and predict more potent molecules as PTPase-1B inhibitors, prior to their synthesis. PMID:21814427

  20. Toxicity in relation to mode of action for the nematode Caenorhabditis elegans: Acute-to-chronic ratios and quantitative structure-activity relationships.

    PubMed

    Ristau, Kai; Akgül, Yeliz; Bartel, Anna Sophie; Fremming, Jana; Müller, Marie-Theres; Reiher, Luise; Stapela, Frederike; Splett, Jan-Paul; Spann, Nicole

    2015-10-01

    Acute-to-chronic ratios (ACRs) and quantitative structure-activity relationships (QSARs) are of particular interest in chemical risk assessment. Previous studies focusing on the relationship between the size or variation of ACRs to substance classes and QSAR models were often based on data for standard test organisms, such as daphnids and fish. In the present study, acute and chronic toxicity tests were performed with the nematode Caenorhabditis elegans for a total of 11 chemicals covering 3 substance classes (nonpolar narcotics: 1-propanol, ethanol, methanol, 2-butoxyethanol; metals: copper, cadmium, zinc; and carbamates: methomyl, oxamyl, aldicarb, dioxacarb). The ACRs were variable, especially for the carbamates and metals, although there was a trend toward small and less variable ACRs for nonpolar narcotic substances. The octanol-water partition coefficient was a good predictor for explaining acute and chronic toxicity of nonpolar narcotic substances to C. elegans, but not for carbamates. Metal toxicity could be related to the covalent index χm2r. Overall, the results support earlier results from ACR and QSAR studies with standard freshwater test animals. As such C. elegans as a representative of small soil/sediment invertebrates would probably be protected by risk assessment strategies already in use. To increase the predictive power of ACRs and QSARs, further research should be expanded to other species and compounds and should also consider the target sites and toxicokinetics of chemicals. PMID:25994998

  1. The rm2 metrics and regression through origin approach: reliable and useful validation tools for predictive QSAR models (Commentary on 'Is regression through origin useful in external validation of QSAR models?').

    PubMed

    Roy, Kunal; Kar, Supratik

    2014-10-01

    Quantitative structure-activity relationship (QSAR) is an in silico technique which can be used in drug discovery, environmental fate modeling, property and toxicity prediction of chemical entities and regulatory toxicology. The predictive potential of a QSAR model is judged from various validation metrics in order to evaluate how well it is capable to predict endpoint values of new untested compounds. The rm2 group of metrics is one of the stringent validation metrics currently used by the QSAR fraternity in different reports. We scrutinized a recently published paper which raised an issue that the constructed criteria based on regression through origin (RTO) are not optimal and there is a significant difference in the rm2 metrics values computed from different statistical software packages. According to our point of view, the conclusion drawn in this paper appears to be misleading. Any inconsistency in the software algorithms has nothing to do with the calculation of rm2 metrics, as such computation is not limited by the use of any specific software, rather it depends only on fundamental mathematical formulae that are well established. However, it is a concern to the QSAR users that Excel and SPSS can return different results for the metrics using the RTO method. Thus, a proper validation of the software tool is required before its use for computation of any validation metric. PMID:24881556

  2. Multivariate SAR and QSAR of cucurbitacin derivatives as cytotoxic compounds in a human lung adenocarcinoma cell line.

    PubMed

    Lang, Karen L; Silva, Izabella T; Machado, Vanessa R; Zimmermann, Lara A; Caro, Miguel S B; Simões, Cláudia M O; Schenkel, Eloir P; Durán, Fernando J; Bernardes, Lílian S C; de Melo, Eduardo B

    2014-03-01

    This article describes structure-activity relationship (SAR/QSAR) studies on the cytotoxic activity in a human lung adenocarcinoma cell line (A549) of 43 cucurbitacin derivatives. Modeling was performed using the methods partial least squares with discriminant analysis (PLS-DA) and PLS. For both studies, the variables were selected using the ordered predictor selection (OPS) algorithm. The SAR study demonstrated that the presence or absence of cytotoxic activity of the cucurbitacins could be described using information derived from their chemical structures. The QSAR study displayed suitable internal and external predictivity, and the selected descriptors indicated that the observed activity might be related to electrophilic attack on cellular structures or genetic material. This study provides improves the understanding of the cytotoxic activity of cucurbitacins and could be used to propose new cytotoxic agents. PMID:24378396

  3. Studying the explanatory capacity of artificial neural networks for understanding environmental chemical quantitative structure-activity relationship models.

    PubMed

    Yang, Lei; Wang, Peng; Jiang, Yilin; Chen, Jian

    2005-01-01

    Although artificial neural networks (ANNs) have been shown to exhibit superior predictive power in the study of quantitative structure-activity relationships (QSARs), they have also been labeled a "black box" because they provide little explanatory insight into the relative influence of the independent variables in the predictive process so that little information on how and why compounds work can be obtained. Here, we have turned our interests to their explanatory capacities; therefore, a method was proposed for assessing the relative importance of variables indicating molecular structure, on the basis of axon connection weights and partial derivatives of the ANN output with respect to its input, which can identify variables that significantly contribute to network predictions, and providing a variable selection method for ANNs. We show that, by extending this approach to ANNs, the "black box" mechanics of ANNs can be greatly illuminated, thereby making it very useful in understanding environmental chemical QSAR models. PMID:16309287

  4. Theoretical Investigations and Density Functional Theory Based Quantitative Structure–Activity Relationships Model for Novel Cytotoxic Platinum(IV) Complexes

    PubMed Central

    2012-01-01

    Octahedral platinum(IV) complexes are promising candidates in the fight against cancer. In order to rationalize the further development of this class of compounds, detailed studies on their mechanisms of action, toxicity, and resistance must be provided and structure–activity relationships must be drawn. Herein, we report on theoretical and QSAR investigations of a series of 53 novel bis-, tris-, and tetrakis(carboxylato)platinum(IV) complexes, synthesized and tested for cytotoxicity in our laboratories. The hybrid DFT functional wb97x was used for optimization of the structure geometry and calculation of the descriptors. Reliable and robust QSAR models with good explanatory and predictive properties were obtained for both the cisplatin sensitive cell line CH1 and the intrinsically cisplatin resistant cell line SW480, with a set of four descriptors. PMID:23214999

  5. Prediction of acute mammalian toxicity using QSAR methods: a case study of sulfur mustard and its breakdown products.

    PubMed

    Ruiz, Patricia; Begluitti, Gino; Tincher, Terry; Wheeler, John; Mumtaz, Moiz

    2012-01-01

    Predicting toxicity quantitatively, using Quantitative Structure Activity Relationships (QSAR), has matured over recent years to the point that the predictions can be used to help identify missing comparison values in a substance's database. In this manuscript we investigate using the lethal dose that kills fifty percent of a test population (LD₅₀) for determining relative toxicity of a number of substances. In general, the smaller the LD₅₀ value, the more toxic the chemical, and the larger the LD₅₀ value, the lower the toxicity. When systemic toxicity and other specific toxicity data are unavailable for the chemical(s) of interest, during emergency responses, LD₅₀ values may be employed to determine the relative toxicity of a series of chemicals. In the present study, a group of chemical warfare agents and their breakdown products have been evaluated using four available rat oral QSAR LD₅₀ models. The QSAR analysis shows that the breakdown products of Sulfur Mustard (HD) are predicted to be less toxic than the parent compound as well as other known breakdown products that have known toxicities. The QSAR estimated break down products LD₅₀ values ranged from 299 mg/kg to 5,764 mg/kg. This evaluation allows for the ranking and toxicity estimation of compounds for which little toxicity information existed; thus leading to better risk decision making in the field. PMID:22842643

  6. A DFT-based toxicity QSAR study of aromatic hydrocarbons to Vibrio fischeri: Consideration of aqueous freely dissolved concentration.

    PubMed

    Wang, Ying; Yang, Xianhai; Wang, Juying; Cong, Yi; Mu, Jingli; Jin, Fei

    2016-05-01

    In the present study, quantitative structure-activity relationship (QSAR) techniques based on toxicity mechanism and density functional theory (DFT) descriptors were adopted to develop predictive models for the toxicity of alkylated and parent aromatic hydrocarbons to Vibrio fischeri. The acute toxicity data of 17 aromatic hydrocarbons from both literature and our experimental results were used to construct QSAR models by partial least squares (PLS) analysis. With consideration of the toxicity process, the partition of aromatic hydrocarbons between water phase and lipid phase and their interaction with the target biomolecule, the optimal QSAR model was obtained by introducing aqueous freely dissolved concentration. The high statistical values of R(2) (0.956) and Q(CUM)(2) (0.942) indicated that the model has good goodness-of-fit, robustness and internal predictive power. The average molecular polarizability (α) and several selected thermodynamic parameters reflecting the intermolecular interactions played important roles in the partition of aromatic hydrocarbons between the water phase and biomembrane. Energy of the highest occupied molecular orbital (E(HOMO)) was the most influential descriptor which dominated the toxicity of aromatic hydrocarbons through the electron-transfer reaction with biomolecules. The results demonstrated that the adoption of freely dissolved concentration instead of nominal concentration was a beneficial attempt for toxicity QSAR modeling of hydrophobic organic chemicals. PMID:26812082

  7. Theoretical studies on QSAR and mechanism of 2-indolinone derivatives as tubulin inhibitors

    NASA Astrophysics Data System (ADS)

    Liao, Si Yan; Qian, Li; Miao, Ti Fang; Lu, Hai Liang; Zheng, Kang Cheng

    The theoretical studies on three-dimensional quantitative structure activity relationship (3D-QSAR) and action mechanism of a series of 2-indolinone derivatives as tubulin inhibitors against human breast cancer cell line MDA-MB-231 have been carried out. The established 3D-QSAR model from the comparative molecular field analysis (CoMFA) shows not only significant statistical quality but also predictive ability, with high correlation coefficient (R2 = 0.986) and cross-validation coefficient (q2 = 0.683). In particular, the appropriate binding orientations and conformations of these 2-indolinone derivatives interacting with tubulin are located by docking study, and it is very interesting to find that the plot of the energy scores of these compounds in DOCK versus the corresponding experimental pIC50 values exhibits a considerable linear correlation. Therefore, the inhibition mechanism that 2-indolinone derivatives were regarded as tubulin inhibitors can be theoretically confirmed. Based on such an inhibition mechanism along with 3D-QSAR results, some important factors improving the activities of these compounds were discussed in detail. These factors can be summarized as follows: the H atom adopted as substituent R1, the substituent R2 with higher electropositivity and smaller bulk, the substituents R4-R6 (on the phenyl ring) with higher electropositivity and larger bulk, and so on. These results can offer useful theoretical references for understanding the action mechanism, designing more potent inhibitors, and predicting their activities prior to synthesis.

  8. Assessment of hydroxylated metabolites of polychlorinated biphenyls as potential xenoestrogens: a QSAR comparative analysis∗.

    PubMed

    Ruiz, P; Myshkin, E; Quigley, P; Faroon, O; Wheeler, J S; Mumtaz, M M; Brennan, R J

    2013-01-01

    Alternative methods, including quantitative structure-activity relationships (QSAR), are being used increasingly when appropriate data for toxicity evaluation of chemicals are not available. Approximately 40 mono-hydroxylated polychlorinated biphenyls (OH-PCBs) have been identified in humans. They represent a health and environmental concern because some of them have been shown to have agonist or antagonist interactions with human hormone receptors. This could lead to modulation of steroid hormone receptor pathways and endocrine system disruption. We performed QSAR analyses using available estrogenic activity (human estrogen receptor ER alpha) data for 71 OH-PCBs. The modelling was performed using multiple molecular descriptors including electronic, molecular, constitutional, topological, and geometrical endpoints. Multiple linear regressions and recursive partitioning were used to best fit descriptors. The results show that the position of the hydroxyl substitution, polarizability, and meta adjacent un-substituted carbon pairs at the phenolic ring contribute towards greater estrogenic activity for these chemicals. These comparative QSAR models may be used for predictive toxicity, and identification of health consequences of PCB metabolites that lack empirical data. Such information will help prioritize such molecules for additional testing, guide future basic laboratory research studies, and help the health/risk assessment community understand the complex nature of chemical mixtures. PMID:23557136

  9. Synthesis, activity, and QSAR studies of tryptamine derivatives on third-instar larvae of Aedes aegypti Linn.

    PubMed

    Oliveira, Rafael R B; Brito, Thaysnara B; Nepel, Angelita; Costa, Emmanoel V; Barison, Andersson; Nunes, Rogéria S; Santos, Roseli L C; Cavalcanti, Sócrates C H

    2014-01-01

    Special attention has been given to the mosquito Aedes aegypti Linn. (Diptera: Culicidae) owing to numerous dengue epidemic outbreaks worldwide. Failure to control vector spreading is accounted for unorganized urban growth and resistance to larvicides and insecticides. Therefore, researchers are currently searching for new and more efficient larvicides and insecticides to aid dengue control measures. Triptamine is known to affect insect behavior, development, and physiology. Expression of this compound in plants has reduced the growth rate of herbivore insects. In view of these facts, it was of our interest to synthesize triptamine amide derivatives as potential larvicides against Ae. aegypti, establishing a Structure-Activity Relationship. Eleven amide derivatives of triptamine were synthesized, characterized, and evaluated for their larvicidal activity against third-instar Ae. aegypti larvae. N-(2-(1H-indol-3-yl)ethyl)-2,2,2-trichloroacetamide exhibited the highest overall larvicidal potency, while N-(2-(1H-Indol-3-yl)ethyl) acetamide displayed the lowest larvicidal potency. A regression equation correlating the larvicidal activity with Log P was obtained. We have found a clear relationship between the larvicidal activity of non-chlorinated compounds and Log P. Analysis of the relationship between Log P and larvicidal activity against Ae. aegypti may be useful in the evaluation of potential larvicidal compounds. PMID:24295020

  10. Predicting chemical ocular toxicity using a combinatorial QSAR approach.

    PubMed

    Solimeo, Renee; Zhang, Jun; Kim, Marlene; Sedykh, Alexander; Zhu, Hao

    2012-12-17

    Regulatory agencies require testing of chemicals and products to protect workers and consumers from potential eye injury hazards. Animal screening, such as the rabbit Draize test, for potential environmental toxicants is time-consuming and costly. Therefore, virtual screening using computational models to tag potential ocular toxicants is attractive to toxicologists and policy makers. We have developed quantitative structure-activity relationship (QSAR) models for a set of small molecules with animal ocular toxicity data compiled by the National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods. The data set was initially curated by removing duplicates, mixtures, and inorganics. The remaining 75 compounds were used to develop QSAR models. We applied both k nearest neighbor and random forest statistical approaches in combination with Dragon and Molecular Operating Environment descriptors. Developed models were validated on an external set of 34 compounds collected from additional sources. The external correct classification rates (CCR) of all individual models were between 72 and 87%. Furthermore, the consensus model, based on the prediction average of individual models, showed additional improvement (CCR = 0.93). The validated models could be used to screen external chemical libraries and prioritize chemicals for in vivo screening as potential ocular toxicants. PMID:23148656

  11. QSAR Analysis of Some Antagonists for p38 map kinase Using Combination of Principal Component Analysis and Artificial Intelligence.

    PubMed

    Doosti, Elham; Shahlaei, Mohsen

    2015-01-01

    Quantitative relationships between structures of a set of p38 map kinase inhibitors and their activities were investigated by principal component regression (PCR) and principal componentartificial neural network (PC-ANN). Latent variables (called components) generated by principal component analysis procedure were applied as the input of developed Quantitative structure- activity relationships (QSAR) models. An exact study of predictability of PCR and PC-ANN showed that the later model has much higher ability to calculate the biological activity of the investigated molecules. Also, experimental and estimated biological activities of compounds used in model development step have indicated a good correlation. Obtained results show that a non-linear model explaining the relationship between the pIC50s and the calculated principal components (that extract from structural descriptors of the studied molecules) is superior than linear model. Some typical figures of merit for QSAR studies explaining the accuracy and predictability of the suggested models were calculated. Therefore, to design novel inhibitors of p38 map kinase with high potency and low undesired effects the developed QSAR models were used to estimate biological pIC50 of the studied compounds. PMID:26234506

  12. Identification of potential influenza virus endonuclease inhibitors through virtual screening based on the 3D-QSAR model.

    PubMed

    Kim, J; Lee, C; Chong, Y

    2009-01-01

    Influenza endonucleases have appeared as an attractive target of antiviral therapy for influenza infection. With the purpose of designing a novel antiviral agent with enhanced biological activities against influenza endonuclease, a three-dimensional quantitative structure-activity relationships (3D-QSAR) model was generated based on 34 influenza endonuclease inhibitors. The comparative molecular similarity index analysis (CoMSIA) with a steric, electrostatic and hydrophobic (SEH) model showed the best correlative and predictive capability (q(2) = 0.763, r(2) = 0.969 and F = 174.785), which provided a pharmacophore composed of the electronegative moiety as well as the bulky hydrophobic group. The CoMSIA model was used as a pharmacophore query in the UNITY search of the ChemDiv compound library to give virtual active compounds. The 3D-QSAR model was then used to predict the activity of the selected compounds, which identified three compounds as the most likely inhibitor candidates. PMID:19343586

  13. Human intestinal transporter database: QSAR modeling and virtual profiling of drug uptake, efflux and interactions

    PubMed Central

    Sedykh, Alexander; Fourches, Denis; Duan, Jianmin; Hucke, Oliver; Garneau, Michel; Zhu, Hao; Bonneau, Pierre; Tropsha, Alexander

    2013-01-01

    Purpose Membrane transporters mediate many biological effects of chemicals and play a major role in pharmacokinetics and drug resistance. The selection of viable drug candidates among biologically active compounds requires the assessment of their transporter interaction profiles. Methods Using public sources, we have assembled and curated the largest, to our knowledge, human intestinal transporter database (>5,000 interaction entries for >3,700 molecules). This data was used to develop thoroughly validated classification Quantitative Structure-Activity Relationship (QSAR) models of transport and/or inhibition of several major transporters including MDR1, BCRP, MRP1-4, PEPT1, ASBT, OATP2B1, OCT1, and MCT1. Results & Conclusions QSAR models have been developed with advanced machine learning techniques such as Support Vector Machines, Random Forest, and k Nearest Neighbors using Dragon and MOE chemical descriptors. These models afforded high external prediction accuracies of 71–100% estimated by 5-fold external validation, and showed hit retrieval rates with up to 20-fold enrichment in the virtual screening of DrugBank compounds. The compendium of predictive QSAR models developed in this study can be used for virtual profiling of drug candidates and/or environmental agents with the optimal transporter profiles. PMID:23269503

  14. 4-Aminoquinolines Active against Chloroquine-Resistant Plasmodium falciparum: Basis of Antiparasite Activity and Quantitative Structure-Activity Relationship Analyses▿

    PubMed Central

    Hocart, Simon J.; Liu, Huayin; Deng, Haiyan; De, Dibyendu; Krogstad, Frances M.; Krogstad, Donald J.

    2011-01-01

    Chloroquine (CQ) is a safe and economical 4-aminoquinoline (AQ) antimalarial. However, its value has been severely compromised by the increasing prevalence of CQ resistance. This study examined 108 AQs, including 68 newly synthesized compounds. Of these 108 AQs, 32 (30%) were active only against CQ-susceptible Plasmodium falciparum strains and 59 (55%) were active against both CQ-susceptible and CQ-resistant P. falciparum strains (50% inhibitory concentrations [IC50s], ≤25 nM). All AQs active against both CQ-susceptible and CQ-resistant P. falciparum strains shared four structural features: (i) an AQ ring without alkyl substitution, (ii) a halogen at position 7 (Cl, Br, or I but not F), (iii) a protonatable nitrogen at position 1, and (iv) a second protonatable nitrogen at the end of the side chain distal from the point of attachment to the AQ ring via the nitrogen at position 4. For activity against CQ-resistant parasites, side chain lengths of ≤3 or ≥10 carbons were necessary but not sufficient; they were identified as essential factors by visual comparison of 2-dimensional (2-D) structures in relation to the antiparasite activities of the AQs and were confirmed by computer-based 3-D comparisons and differential contour plots of activity against P. falciparum. The advantage of the method reported here (refinement of quantitative structure-activity relationship [QSAR] descriptors by random assignment of compounds to multiple training and test sets) is that it retains QSAR descriptors according to their abilities to predict the activities of unknown test compounds rather than according to how well they fit the activities of the compounds in the training sets. PMID:21383099

  15. Phenylpropiophenone derivatives as potential anticancer agents: synthesis, biological evaluation and quantitative structure-activity relationship study.

    PubMed

    Ivković, Branka M; Nikolic, Katarina; Ilić, Bojana B; Žižak, Željko S; Novaković, Radmila B; Čudina, Olivera A; Vladimirov, Sote M

    2013-05-01

    Series of twelve chalcone and propafenone derivatives has been synthesized and evaluated for anticancer activities against HeLa, Fem-X, PC-3, MCF-7, LS174 and K562 cell lines. The 2D-QSAR and 3D-QSAR studies were performed for all compounds with cytotoxic activities against each cancer cell line. Partial least squares (PLS) regression has been applied for selection of the most relevant molecular descriptors and QSAR models building. Predictive potentials of the created 2D-QSAR and 3D-QSAR models for each cell line were compared, by use of leave-one-out cross-validation and external validation, and optimal QSAR models for each cancer cell line were selected. The QSAR studies have selected the most significant molecular descriptors and pharmacophores of the chalcone and propafenone derivatives and proposed structures of novel chalcone and propafenone derivatives with enhanced anticancer activity on the HeLa, Fem-X, PC-3, MCF-7, LS174 and K562 cells. PMID:23501110

  16. Towards understanding the mechanism of action of antibacterial N-alkyl-3-hydroxypyridinium salts: Biological activities, molecular modeling and QSAR studies.

    PubMed

    Dolezal, Rafael; Soukup, Ondrej; Malinak, David; Savedra, Ranylson M L; Marek, Jan; Dolezalova, Marie; Pasdiorova, Marketa; Salajkova, Sarka; Korabecny, Jan; Honegr, Jan; Ramalho, Teodorico C; Kuca, Kamil

    2016-10-01

    In this study, we have carried out a combined experimental and computational investigation to elucidate several bred-in-the-bone ideas standing out in rational design of novel cationic surfactants as antibacterial agents. Five 3-hydroxypyridinium salts differing in the length of N-alkyl side chain have been synthesized, analyzed by high performance liquid chromatography, tested for in vitro activity against a panel of pathogenic bacterial and fungal strains, computationally modeled in water by a SCRF B3LYP/6-311++G(d,p) method, and evaluated by a systematic QSAR analysis. Given the results of this work, the hypothesis suggesting that higher positive charge of the quaternary nitrogen should increase antimicrobial efficacy can be rejected since 3-hydroxyl group does increase the positive charge on the nitrogen but, simultaneously, it significantly derogates the antimicrobial activity by lowering the lipophilicity and by escalating the desolvation energy of the compounds in comparison with non-hydroxylated analogues. Herein, the majority of the prepared 3-hydroxylated substances showed notably lower potency than the parent pyridinium structures, although compound 8 with C12 alkyl chain proved a distinctly better antimicrobial activity in submicromolar range. Focusing on this anomaly, we have made an effort to reveal the reason of the observed activity through a molecular dynamics simulation of the interaction between the bacterial membrane and compound 8 in GROMACS software. PMID:27341309

  17. Prediction of Halocarbon Toxicity from Structure: A Hierarchical QSAR Approach

    SciTech Connect

    Gute, B D; Balasubramanian, K; Geiss, K; Basak, S C

    2003-04-11

    Mathematical structural invariants and quantum theoretical descriptors have been used extensively in quantitative structure-activity relationships (QSARs) for the estimation of pharmaceutical activities, biological properties, physicochemical properties, and the toxicities of chemicals. Recently our research team has explored the relative importance of various levels of chemodescriptors, i.e., topostructural, topochemical, geometrical, and quantum theoretical descriptors, in property estimation. This study examines the contribution of chemodescriptors ranging from topostructural to quantum theoretic calculations up to the Gaussian STO-3G level in the prediction of the toxicity of a set of twenty halocarbons. We also report the results of experimental cell-level toxicity studies on these twenty halocarbons to validate our models.

  18. QSAR studies on 3-(4-biphenylmethyl) 4, 5-dihydro-4-oxo-3H-imidazo [4, 5-c] Pyridine derivatives as angiotensin II (AT1) receptor antagonist.

    PubMed

    Sharma, Mukesh C

    2015-06-01

    QSAR studies were performed for correlating the chemical composition of 3-(4-biphenylmethyl) 4, 5-dihydro-4-oxo-3H-imidazo [4, 5-c] pyridines bearing aryl acetic acid esters and acetamides as angiotensin II AT(1) receptor antagonist. Four different quantitative structure-property relationship (QSAR) methods namely two-dimensional (2D-QSAR), group-based QSAR, k-nearest neighbor and Pharmacophore Modeling were employed to obtain statistically significant models. The statistically significant best 2D-QSAR model having correlation coefficient r(2) = 0:8940 and cross-validated squared correlation coefficient q(2) = 0:7648 with external predictive ability of pred_r(2) = 0:8177,pred_r(2)se = 0.4119 and best group-based QSAR model having r(2) = 0:7392 and q(2) = 0:6710with pred_r(2) = 0:7503was developed by SA-principal component regression. The most predictive k-nearest neighbor model derived from the superposition of conformations has good cross-validated q(2) = 0:7637 and satisfied predictive ability r(2)_pred = 0.7143. Continuing with compounds of substituted 4, 5-dihydro-4-oxo-3H-imidazo [4, 5-c] pyridine derivatives chemical feature-based pharmacophore models with lowest RMSD value (0.3292 Å) consists of two Hac (Hydrogen bond acceptor), negative ionizable, and two AroC (Aromatic) features are important for the activity. The study suggested that substitution of group at R, R 1, R 2 and Ar, and position on 4, 5-dihydro-4-oxo-3H-imidazo [4, 5-c] pyridine ring with more electronegative nature and low bulkiness are favorable for the antihypertensive activity. These theoretical results may provide a useful reference for understanding the action mechanism and designing potential angiotensin II (AT1) receptor antagonist. PMID:26215494

  19. Development of Quantitative Structure-Activity Relationship (QSAR) Models to Predict the Carcinogenic Potency of Chemicals

    EPA Science Inventory

    Determining the carcinogenicity and carcinogenic potency of new chemicals is both a labor-intensive and time-consuming process. In order to expedite the screening process, there is a need to either: (1) identify alternative toxicity measures (shorter duration) that may be used as...

  20. QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIP (QSAR) MODELS TO PREDICT CHEMICAL TOXICITY FOR VARIOUS HEALTH ENDPOINTS

    EPA Science Inventory

    Although ranking schemes based on exposure and toxicity have been developed to aid in the prioritization of research funds for identifying chemicals of regulatory concern, there are significant gaps in the availability of experimental toxicity data for most health endpoints. Pred...

  1. Development of quantitative structure-activity relationship (QSAR) models to predict the carcinogenic potency of chemicals

    SciTech Connect

    Venkatapathy, Raghuraman Wang Chingyi; Bruce, Robert Mark; Moudgal, Chandrika

    2009-01-15

    Determining the carcinogenicity and carcinogenic potency of new chemicals is both a labor-intensive and time-consuming process. In order to expedite the screening process, there is a need to identify alternative toxicity measures that may be used as surrogates for carcinogenic potency. Alternative toxicity measures for carcinogenic potency currently being used in the literature include lethal dose (dose that kills 50% of a study population [LD{sub 50}]), lowest-observed-adverse-effect-level (LOAEL) and maximum tolerated dose (MTD). The purpose of this study was to investigate the correlation between tumor dose (TD{sub 50}) and three alternative toxicity measures as an estimator of carcinogenic potency. A second aim of this study was to develop a Classification and Regression Tree (CART) between TD{sub 50} and estimated/experimental predictor variables to predict the carcinogenic potency of new chemicals. Rat TD{sub 50}s of 590 structurally diverse chemicals were obtained from the Cancer Potency Database, and the three alternative toxicity measures considered in this study were estimated using TOPKAT, a toxicity estimation software. Though poor correlations were obtained between carcinogenic potency and the three alternative toxicity (both experimental and TOPKAT) measures for the CPDB chemicals, a CART developed using experimental data with no missing values as predictor variables provided reasonable estimates of TD{sub 50} for nine chemicals that were part of an external validation set. However, if experimental values for the three alternative measures, mutagenicity and logP are not available in the literature, then either the CART developed using missing experimental values or estimated values may be used for making a prediction.

  2. In-silico structure activity relationship study of toxicity endpoints by QSAR modeling (SOT)

    EPA Science Inventory

    Several thousand chemicals were tested in 700 toxicity-related in-vitro HTS bioassays through the EPA’s ToxCast and Tox21 projects. This chemical set only covers a portion of the chemical space of interest for environmental exposure, leading to a need to fill data gaps with alter...

  3. Variable selection based QSAR modeling on Bisphenylbenzimidazole as Inhibitor of HIV-1 reverse transcriptase.

    PubMed

    Kumar, Surendra; Tiwari, Meena

    2013-11-01

    The emergence of mutant virus in drug therapy for HIV-1 infection has steadily risen in the last decade. Inhibition of reverse transcriptase enzyme has emerged as a novel target for the treatment of HIV infection. The aim to decipher the structural features that interact with receptor, we report a quantitative structure activity relationship (QSAR) study on a dataset of thirty seven compounds belonging to bisphenylbenzimidazoles (BPBIs) as reverse transcriptase inhibitors using enhanced replacement method (ERM), stepwise multiple linear regression (Stepwise-MLR) and genetic function approximation (GFA) method for selecting a subset of relevant descriptors, developing the best multiple linear regression model and defining the QSAR model applicability domain boundaries. The enhanced replacement method was found to give better results r²=0.8542, Q²(loo) = 0.7917, r²pred = 0.7812) at five variables as compared to stepwise MLR and GFA method, evidenced by internal and external validation parameters. The modified r² (r²m) of the training set, test set and whole data set were calculated and are in agreement with the enhanced replacement method. The results of QSAR study rationalize the structural requirement for optimum binding of ligands. The developed QSAR model shows that hydrophobicity, flexibility, three dimensional surface area, volume and shape of molecule are important parameters to be considered for designing new compounds and to decipher reverse transcriptase enzyme inhibition activity of these compounds at molecular level. The applicability domain was defined to find the similar analogs with better prediction power. PMID:23106285

  4. QSAR study of some pyrazolo[3,4-d]pyrimidine derivatives as the c-Src inhibitors

    NASA Astrophysics Data System (ADS)

    Shukla, Bindesh Kumar; Yadava, Umesh

    2016-05-01

    Two dimensional quantitative structure activity relationship (QSAR) studies have been carried out on a series of 42 pyrazolo[3,4-d]pyrimidine derivatives to find out the structural requirements for the inhibition of c-SRC phosphorilation. The best predictions were obtained using Heuristic and Best MLR methods from the model where 33 compounds were considered in the training set and the remaining 9 in the test set. Both Best MLR and Heuristic methods indicate that squared correlation coefficient for training and test sets are very close to observed biological activities which designate the good correlation between the experimental and predicted activity. The results that are obtained from 2D-QSAR studies may provide useful insights into the roles of various substitution patterns on the pyrazolo[3,4-d]pyrimidine core and may also help to design more potent compounds.

  5. QSAR models for reproductive toxicity and endocrine disruption in regulatory use – a preliminary investigation†

    PubMed Central

    Jensen, G.E.; Niemelä, J.R.; Wedebye, E.B.; Nikolov, N.G.

    2008-01-01

    A special challenge in the new European Union chemicals legislation, Registration, Evaluation and Authorisation of Chemicals, will be the toxicological evaluation of chemicals for reproductive toxicity. Use of valid quantitative structure–activity relationships (QSARs) is a possibility under the new legislation. This article focuses on a screening exercise by use of our own and commercial QSAR models for identification of possible reproductive toxicants. Three QSAR models were used for reproductive toxicity for the endpoints teratogenic risk to humans (based on animal tests, clinical data and epidemiological human studies), dominant lethal effect in rodents (in vivo) and Drosophila melanogaster sex-linked recessive lethal effect. A structure set of 57,014 European Inventory of Existing Chemical Substances (EINECS) chemicals was screened. A total of 5240 EINECS chemicals, corresponding to 9.2%, were predicted as reproductive toxicants by one or more of the models. The chemicals predicted positive for reproductive toxicity will be submitted to the Danish Environmental Protection Agency as scientific input for a future updated advisory classification list with advisory classifications for concern for humans owing to possible developmental toxic effects: Xn (Harmful) and R63 (Possible risk of harm to the unborn child). The chemicals were also screened in three models for endocrine disruption. PMID:19061080

  6. QSAR analysis of nitroaromatics' toxicity in Tetrahymena pyriformis: structural factors and possible modes of action

    PubMed Central

    Artemenko, A.G.; Muratov, E. N.; Kuz’min, V.E.; Muratov, N.N.; Varlamova, E.V.; Kuz'mina, A.V.; Gorb, L. G.; Golius, A.; Hill, F.C.; Leszczynski, J.; Tropsha, A.

    2012-01-01

    The Hierarchical Technology for Quantitative Structure - Activity Relationships (HiT QSAR) was applied to 95 diverse nitroaromatic compounds (including some widely known explosives) tested for their toxicity (50% inhibition growth concentration, IGC50) against the ciliate Tetrahymena pyriformis. The dataset was divided into subsets according to putative mechanisms of toxicity. Classification and Regression Trees (CART) approach implemented within HiT QSAR has been used for prediction of mechanism of toxicity for new compounds. The resulting models were shown to have ~80% accuracy for external datasets indicating that the mechanistic dataset division was sensible. Then, Partial Least Squares (PLS) statistical approach was used for the development of 2D QSAR models. Validated PLS models were explored to (i) elucidate the effects of different substituents in nitroaromatic compounds on toxicity; (ii) differentiate compounds by probable mechanisms of toxicity based on their structural descriptors; (iii) analyze the role of various physical-chemical factors responsible for compounds’ toxicity. Models were interpreted in terms of molecular fragments promoting or interfering with toxicity. It was also shown that mutual influence of substituents in benzene ring plays the determining role in toxicity variation. Although chemical mechanism based models were statistically significant and externally predictive (R2ext=0.64 for the external set of 63 nitroaromatics identified after all calculations have been completed), they were also shown to have limited coverage (57% for modeling and 76% for external set). PMID:21714735

  7. Structure-thermodynamics-antioxidant activity relationships of selected natural phenolic acids and derivatives: an experimental and theoretical evaluation.

    PubMed

    Chen, Yuzhen; Xiao, Huizhi; Zheng, Jie; Liang, Guizhao

    2015-01-01

    Phenolic acids and derivatives have potential biological functions, however, little is known about the structure-activity relationships and the underlying action mechanisms of these phenolic acids to date. Herein we investigate the structure-thermodynamics-antioxidant relationships of 20 natural phenolic acids and derivatives using DPPH• scavenging assay, density functional theory calculations at the B3LYP/6-311++G(d,p) levels of theory, and quantitative structure-activity relationship (QSAR) modeling. Three main working mechanisms (HAT, SETPT and SPLET) are explored in four micro-environments (gas-phase, benzene, water and ethanol). Computed thermodynamics parameters (BDE, IP, PDE, PA and ETE) are compared with the experimental radical scavenging activities against DPPH•. Available theoretical and experimental investigations have demonstrated that the extended delocalization and intra-molecular hydrogen bonds are the two main contributions to the stability of the radicals. The C = O or C = C in COOH, COOR, C = CCOOH and C = CCOOR groups, and orthodiphenolic functionalities are shown to favorably stabilize the specific radical species to enhance the radical scavenging activities, while the presence of the single OH in the ortho position of the COOH group disfavors the activities. HAT is the thermodynamically preferred mechanism in the gas phase and benzene, whereas SPLET in water and ethanol. Furthermore, our QSAR models robustly represent the structure-activity relationships of these explored compounds in polar media. PMID:25803685

  8. Structure-Thermodynamics-Antioxidant Activity Relationships of Selected Natural Phenolic Acids and Derivatives: An Experimental and Theoretical Evaluation

    PubMed Central

    Zheng, Jie; Liang, Guizhao

    2015-01-01

    Phenolic acids and derivatives have potential biological functions, however, little is known about the structure-activity relationships and the underlying action mechanisms of these phenolic acids to date. Herein we investigate the structure-thermodynamics-antioxidant relationships of 20 natural phenolic acids and derivatives using DPPH• scavenging assay, density functional theory calculations at the B3LYP/6-311++G(d,p) levels of theory, and quantitative structure-activity relationship (QSAR) modeling. Three main working mechanisms (HAT, SETPT and SPLET) are explored in four micro-environments (gas-phase, benzene, water and ethanol). Computed thermodynamics parameters (BDE, IP, PDE, PA and ETE) are compared with the experimental radical scavenging activities against DPPH•. Available theoretical and experimental investigations have demonstrated that the extended delocalization and intra-molecular hydrogen bonds are the two main contributions to the stability of the radicals. The C = O or C = C in COOH, COOR, C = CCOOH and C = CCOOR groups, and orthodiphenolic functionalities are shown to favorably stabilize the specific radical species to enhance the radical scavenging activities, while the presence of the single OH in the ortho position of the COOH group disfavors the activities. HAT is the thermodynamically preferred mechanism in the gas phase and benzene, whereas SPLET in water and ethanol. Furthermore, our QSAR models robustly represent the structure-activity relationships of these explored compounds in polar media. PMID:25803685

  9. Identification of triazolo[4,5-b]pyrazine derivatives as hepatocyte growth factor receptor inhibitors through structure-activity relationships and molecular docking simulations.

    PubMed

    Dong, Minghui; Ren, Yujie; Gao, Xiaodong

    2015-10-01

    c-MET is a receptor tyrosine kinase and potential oncological target for cancer therapy. The activities of 1,2,3-triazolo[4,5-b]pyrazine series of c-MET inhibitors were analyzed according to the three-dimensional quantitative structure-activity relationship and molecular docking methods. The results indicated that the hydrophobic and electrostatic fields play key roles in activity and QSAR model was reliable enough for activity prediction. Moreover, the docking results do validate the predicted 3D-QSAR scores, vital residues Asp1222, Asp1231, Met1160, Tyr1259 and Tyr1230 found in binding site. Four new c-MET inhibitor analogs designed in this Letter which are being currently synthesized by our laboratories. PMID:26321362

  10. Docking and quantitative structure-activity relationship of oxadiazole derivates as inhibitors of GSK3β.

    PubMed

    Quesada-Romero, Luisa; Caballero, Julio

    2014-02-01

    The binding modes of 42 oxadiazole derivates inside glycogen synthase kinase 3 beta (GSK3β were determined using docking experiments; thus, the preferred active conformations of these inhibitors are proposed. We found that these compounds adopt a scorpion-shaped conformation and they accept a hydrogen bond (HB) from the residue Val135 of the GSK3β ATP-binding site hinge region. In addition, quantitative structure-activity relationship (QSAR) models were constructed to explain the trend of the GSK3β inhibitory activities for the studied compounds. In a first approach, three-dimensional (3D) vectors were calculated using docking conformations and, by using multiple-linear regression, we assessed that GETAWAY vectors were able to describe the reported biological activities. In other QSAR approach, SMILES-based optimal descriptors were calculated. The best model included three-SMILES elements SSSβ leading to the identification of key molecular features that contribute to a high GSK3β inhibitory activity. PMID:24081608

  11. Flavonoids as CDK1 Inhibitors: Insights in Their Binding Orientations and Structure-Activity Relationship

    PubMed Central

    Navarro-Retamal, Carlos

    2016-01-01

    In the last years, the interactions of flavonoids with protein kinases (PKs) have been described by using crystallographic experiments. Interestingly, different orientations have been found for one flavonoid inside different PKs and different chemical substitutions lead to different orientations of the flavonoid scaffold inside one PK. Accordingly, orientation predictions of novel analogues could help to the design of flavonoids with high PK inhibitory activities. With this in mind, we studied the binding modes of 37 flavonoids (flavones and chalcones) inside the cyclin-dependent PK CDK1 using docking experiments. We found that the compounds under study adopted two different orientations into the active site of CDK1 (orientations I and II in the manuscript). In addition, quantitative structure–activity relationship (QSAR) models using CoMFA and CoMSIA methodologies were constructed to explain the trend of the CDK1 inhibitory activities for the studied flavonoids. Template-based and docking-based alignments were used. Models developed starting from docking-based alignment were applied for describing the whole dataset and compounds with orientation I. Adequate R2 and Q2 values were obtained by each method; interestingly, only hydrophobic and hydrogen bond donor fields describe the differential potency of the flavonoids as CDK1 inhibitors for both defined alignments and subsets. Our current application of docking and QSAR together reveals important elements to be drawn for the design of novel flavonoids with increased PK inhibitory activities. PMID:27517610

  12. Development and Validation of Quantitative Structure-Activity Relationship Models for Compounds Acting on Serotoninergic Receptors

    PubMed Central

    Żydek, Grażyna; Brzezińska, Elżbieta

    2012-01-01

    A quantitative structure-activity relationship (QSAR) study has been made on 20 compounds with serotonin (5-HT) receptor affinity. Thin-layer chromatographic (TLC) data and physicochemical parameters were applied in this study. RP2 TLC 60F254 plates (silanized) impregnated with solutions of propionic acid, ethylbenzene, 4-ethylphenol, and propionamide (used as analogues of the key receptor amino acids) and their mixtures (denoted as S1–S7 biochromatographic models) were used in two developing phases as a model of drug-5-HT receptor interaction. The semiempirical method AM1 (HyperChem v. 7.0 program) and ACD/Labs v. 8.0 program were employed to calculate a set of physicochemical parameters for the investigated compounds. Correlation and multiple linear regression analysis were used to search for the best QSAR equations. The correlations obtained for the compounds studied represent their interactions with the proposed biochromatographic models. The good multivariate relationships (R2 = 0.78–0.84) obtained by means of regression analysis can be used for predicting the quantitative effect of biological activity of different compounds with 5-HT receptor affinity. “Leave-one-out” (LOO) and “leave-N-out” (LNO) cross-validation methods were used to judge the predictive power of final regression equations. PMID:22619602

  13. QSAR study of flavonoids and biflavonoids as influenza H1N1 virus neuraminidase inhibitors.

    PubMed

    Mercader, Andrew G; Pomilio, Alicia B

    2010-05-01

    We performed a predictive analysis based on Quantitative Structure-Activity Relationships (QSAR) of a very important property of flavonoids which is the inhibition (IC50) of influenza H1N1 virus neuraminidase. The best linear model constructed from 20 molecular structures incorporated four molecular descriptors, selected from more than a thousand geometrical, topological, quantum-mechanical and electronic types of descriptors. The obtained model suggests that the activity depends on the electric charges, masses and polarizabilities of the atoms present in the molecule as well as its conformation. The model showed good predictive ability established by the theoretical and external test set validations. PMID:20116898

  14. QSAR models for the removal of organic micropollutants in four different river water matrices.

    PubMed

    Sudhakaran, Sairam; Calvin, James; Amy, Gary L

    2012-04-01

    Ozonation is an advanced water treatment process used to remove organic micropollutants (OMPs) such as pharmaceuticals and personal care products (PPCPs). In this study, Quantitative Structure Activity Relationship (QSAR) models, for ozonation and advanced oxidation process (AOP), were developed with percent-removal of OMPs by ozonation as the criterion variable. The models focused on PPCPs and pesticides elimination in bench-scale studies done within natural water matrices: Colorado River, Passaic River, Ohio River and Suwannee synthetic water. The OMPs removal for the different water matrices varied depending on the water quality conditions such as pH, DOC, alkalinity. The molecular descriptors used to define the OMPs physico-chemical properties range from one-dimensional (atom counts) to three-dimensional (quantum-chemical). Based on a statistical modeling approach using more than 40 molecular descriptors as predictors, descriptors influencing ozonation/AOP were chosen for inclusion in the QSAR models. The modeling approach was based on multiple linear regression (MLR). Also, a global model based on neural networks was created, compiling OMPs from all the four river water matrices. The chemically relevant molecular descriptors involved in the QSAR models were: energy difference between lowest unoccupied and highest occupied molecular orbital (E(LUMO)-E(HOMO)), electron-affinity (EA), number of halogen atoms (#X), number of ring atoms (#ring atoms), weakly polar component of the solvent accessible surface area (WPSA) and oxygen to carbon ratio (O/C). All the QSAR models resulted in a goodness-of-fit, R(2), greater than 0.8. Internal and external validations were performed on the models. PMID:22245076

  15. Critically Assessing the Predictive Power of QSAR Models for Human Liver Microsomal Stability.

    PubMed

    Liu, Ruifeng; Schyman, Patric; Wallqvist, Anders

    2015-08-24

    To lower the possibility of late-stage failures in the drug development process, an up-front assessment of absorption, distribution, metabolism, elimination, and toxicity is commonly implemented through a battery of in silico and in vitro assays. As in vitro data is accumulated, in silico quantitative structure-activity relationship (QSAR) models can be trained and used to assess compounds even before they are synthesized. Even though it is generally recognized that QSAR model performance deteriorates over time, rigorous independent studies of model performance deterioration is typically hindered by the lack of publicly available large data sets of structurally diverse compounds. Here, we investigated predictive properties of QSAR models derived from an assembly of publicly available human liver microsomal (HLM) stability data using variable nearest neighbor (v-NN) and random forest (RF) methods. In particular, we evaluated the degree of time-dependent model performance deterioration. Our results show that when evaluated by 10-fold cross-validation with all available HLM data randomly distributed among 10 equal-sized validation groups, we achieved high-quality model performance from both machine-learning methods. However, when we developed HLM models based on when the data appeared and tried to predict data published later, we found that neither method produced predictive models and that their applicability was dramatically reduced. On the other hand, when a small percentage of randomly selected compounds from data published later were included in the training set, performance of both machine-learning methods improved significantly. The implication is that 1) QSAR model quality should be analyzed in a time-dependent manner to assess their true predictive power and 2) it is imperative to retrain models with any up-to-date experimental data to ensure maximum applicability. PMID:26170251

  16. QSAR study for the soybean 15-lipoxygenase inhibitory activity of organosulfur compounds derived from the essential oil of garlic.

    PubMed

    Camargo, Alejandra B; Marchevsky, Eduardo; Luco, Juan M

    2007-04-18

    In this study, multiple linear regression (MLR) and partial least-squares (PLS) techniques were used for modeling the soybean 15-lipoxygenase inhibitory activity of a varied group of mono-, di-, and trisulfides derived from the essential oil of garlic. The structures of the compounds under study were characterized by means of calculated physicochemical parameters and several nonempirical descriptors, such as topological, geometrical, and quantum chemical indices. The results obtained indicate that the inhibitory activity is strongly dependent on the ability of the compounds to participate in dispersive interactions with the enzyme, as expressed by the solvent-accessible surface area (SASA) and the average distance/distance degree descriptor (ADDD) index. On the other hand, the high contribution of the lowest unoccupied molecular orbit term (LUMO) in the PLS models derived for the di- and trisulfides suggests that the solute's electron-acceptor capacity plays a fundamental role in the inhibitory activity exhibited for these compounds. Finally, the geometric features as expressed by the shape parameters included in the models indicate a low but not negligible positive contribution of molecular linearity in the enzyme-inhibitor binding. In summary, the developed quantitative structure-activity relationship approach successfully accounts for the potencies of organosulfur compounds acting on soybean 15-lipoxygenase and thereby offers both a guide for the synthesis of new compounds and a hypothesis for the molecular basis of their activity. PMID:17367159

  17. An advanced application of the quantitative structure-activity relationship concept in electrokinetic chromatography of metal complexes.

    PubMed

    Oszwałdowski, Sławomir; Timerbaev, Andrei R

    2008-02-01

    The relevance of the quantitative structure-activity relationship (QSAR) principle in MEKC and microemulsion EKC (MEEKC) of metal-ligand complexes was evaluated for a better understanding of analyte migration mechanism. A series of gallium chelates were applied as test solutes with available experimental migration data in order to reveal the molecular properties that govern the separation. The QSAR models operating with n-octanol-water partition coefficients or van der Waals volumes were found to be valid for estimation of the retention factors (log k') of neutral compounds when using only an aqueous MEEKC electrolyte. On the other hand, consistent approximations of log k' for both uncharged and charged complexes in either EKC mode (and also with hydro-organic BGEs) were achievable with two-parametric QSARs in which the dipole moment is additionally incorporated as a structural descriptor, reflecting the electrostatic solute-pseudostationary phase interaction. The theoretical analysis of significant molecular parameters in MEKC systems, in which the micellar BGE is modified with an organic solvent, confirmed that concomitant consideration of hydrophobic, electrostatic, and solvation factors is essential for explaining the migration behavior of neutral metal complexes. PMID:18219650

  18. Combining QSAR Modeling and Text-Mining Techniques to Link Chemical Structures and Carcinogenic Modes of Action.

    PubMed

    Papamokos, George; Silins, Ilona

    2016-01-01

    There is an increasing need for new reliable non-animal based methods to predict and test toxicity of chemicals. Quantitative structure-activity relationship (QSAR), a computer-based method linking chemical structures with biological activities, is used in predictive toxicology. In this study, we tested the approach to combine QSAR data with literature profiles of carcinogenic modes of action automatically generated by a text-mining tool. The aim was to generate data patterns to identify associations between chemical structures and biological mechanisms related to carcinogenesis. Using these two methods, individually and combined, we evaluated 96 rat carcinogens of the hematopoietic system, liver, lung, and skin. We found that skin and lung rat carcinogens were mainly mutagenic, while the group of carcinogens affecting the hematopoietic system and the liver also included a large proportion of non-mutagens. The automatic literature analysis showed that mutagenicity was a frequently reported endpoint in the literature of these carcinogens, however, less common endpoints such as immunosuppression and hormonal receptor-mediated effects were also found in connection with some of the carcinogens, results of potential importance for certain target organs. The combined approach, using QSAR and text-mining techniques, could be useful for identifying more detailed information on biological mechanisms and the relation with chemical structures. The method can be particularly useful in increasing the understanding of structure and activity relationships for non-mutagens. PMID:27625608

  19. Combining QSAR Modeling and Text-Mining Techniques to Link Chemical Structures and Carcinogenic Modes of Action

    PubMed Central

    Papamokos, George; Silins, Ilona

    2016-01-01

    There is an increasing need for new reliable non-animal based methods to predict and test toxicity of chemicals. Quantitative structure-activity relationship (QSAR), a computer-based method linking chemical structures with biological activities, is used in predictive toxicology. In this study, we tested the approach to combine QSAR data with literature profiles of carcinogenic modes of action automatically generated by a text-mining tool. The aim was to generate data patterns to identify associations between chemical structures and biological mechanisms related to carcinogenesis. Using these two methods, individually and combined, we evaluated 96 rat carcinogens of the hematopoietic system, liver, lung, and skin. We found that skin and lung rat carcinogens were mainly mutagenic, while the group of carcinogens affecting the hematopoietic system and the liver also included a large proportion of non-mutagens. The automatic literature analysis showed that mutagenicity was a frequently reported endpoint in the literature of these carcinogens, however, less common endpoints such as immunosuppression and hormonal receptor-mediated effects were also found in connection with some of the carcinogens, results of potential importance for certain target organs. The combined approach, using QSAR and text-mining techniques, could be useful for identifying more detailed information on biological mechanisms and the relation with chemical structures. The method can be particularly useful in increasing the understanding of structure and activity relationships for non-mutagens. PMID:27625608

  20. Study of the Differential Activity of Thrombin Inhibitors Using Docking, QSAR, Molecular Dynamics, and MM-GBSA

    PubMed Central

    Mena-Ulecia, Karel; Tiznado, William; Caballero, Julio

    2015-01-01

    Non-peptidic thrombin inhibitors (TIs; 177 compounds) with diverse groups at motifs P1 (such as oxyguanidine, amidinohydrazone, amidine, amidinopiperidine), P2 (such as cyanofluorophenylacetamide, 2-(2-chloro-6-fluorophenyl)acetamide), and P3 (such as phenylethyl, arylsulfonate groups) were studied using molecular modeling to analyze their interactions with S1, S2, and S3 subsites of the thrombin binding site. Firstly, a protocol combining docking and three dimensional quantitative structure–activity relationship was performed. We described the orientations and preferred active conformations of the studied inhibitors, and derived a predictive CoMSIA model including steric, donor hydrogen bond, and acceptor hydrogen bond fields. Secondly, the dynamic behaviors of some selected TIs (compounds 26, 133, 147, 149, 162, and 177 in this manuscript) that contain different molecular features and different activities were analyzed by creating the solvated models and using molecular dynamics (MD) simulations. We used the conformational structures derived from MD to accomplish binding free energetic calculations using MM-GBSA. With this analysis, we theorized about the effect of van der Waals contacts, electrostatic interactions and solvation in the potency of TIs. In general, the contents reported in this article help to understand the physical and chemical characteristics of thrombin-inhibitor complexes. PMID:26599107

  1. Study of the Differential Activity of Thrombin Inhibitors Using Docking, QSAR, Molecular Dynamics, and MM-GBSA.

    PubMed

    Mena-Ulecia, Karel; Tiznado, William; Caballero, Julio

    2015-01-01

    Non-peptidic thrombin inhibitors (TIs; 177 compounds) with diverse groups at motifs P1 (such as oxyguanidine, amidinohydrazone, amidine, amidinopiperidine), P2 (such as cyanofluorophenylacetamide, 2-(2-chloro-6-fluorophenyl)acetamide), and P3 (such as phenylethyl, arylsulfonate groups) were studied using molecular modeling to analyze their interactions with S1, S2, and S3 subsites of the thrombin binding site. Firstly, a protocol combining docking and three dimensional quantitative structure-activity relationship was performed. We described the orientations and preferred active conformations of the studied inhibitors, and derived a predictive CoMSIA model including steric, donor hydrogen bond, and acceptor hydrogen bond fields. Secondly, the dynamic behaviors of some selected TIs (compounds 26, 133, 147, 149, 162, and 177 in this manuscript) that contain different molecular features and different activities were analyzed by creating the solvated models and using molecular dynamics (MD) simulations. We used the conformational structures derived from MD to accomplish binding free energetic calculations using MM-GBSA. With this analysis, we theorized about the effect of van der Waals contacts, electrostatic interactions and solvation in the potency of TIs. In general, the contents reported in this article help to understand the physical and chemical characteristics of thrombin-inhibitor complexes. PMID:26599107

  2. Toxicity of aryl- and benzylhalides to Daphnia magna and classification of their mode of action based on quantitative structure-activity relationship

    SciTech Connect

    Marchini, S.; Passerini, L.; Hoglund, M.D.; Pino, A.; Nendza, M.

    1999-12-01

    The acute toxicity of aryl- and benzylhalides to Daphnia magna was investigated to test the validity of existing classification schemes for chemicals by mode of action, mainly based on fish studies, and the applicability of predictive quantitative structure-activity relationship (QSAR) models. Halobenzenes and halotoluenes are generally agreed to be unambiguous baseline toxicants (class 1) with the major exception of the benzylic structures, which are reactive in fish tests (class 3). Eighty-nine percent of the arylhalides tested in this study match a log P{sub ow}-dependent QSAR, including fluorinated, chlorinated, brominated, and iodinated derivatives, thereby confirming the validity of the baseline models also for variously halogenated compounds (other than only-chloro compounds). The toxicities of the benzylhalides relative to baseline QSARs clearly indicate that these compounds belong to two classes of mode of action, i.e., they either act as narcotic toxicants (class 1) or reveal excess toxicity due to unspecific reactivity (class 3). On some occasions, the assignment to the two classes of F. magna deviates from the structural rules derived from fish, i.e., iodinated compounds as well as {alpha},{alpha}-Cl{sub 2}-toluene's lack reactive excess toxicity but behave as nonpolar nonspecific toxicants. The QSARs derived during this study reveal lower slopes and higher intercepts than typical baseline models and, together with the analysis of mixture toxicity studies, behavioral studies, and critical body burden, advocate the hypothesis that there are several different ways to produce baseline toxicity. Most halobenzenes and halotoluenes are actually baseline chemicals with some extra reactivity and as such form a subgroup, whose limits still have to be defined. Different primary sites of action could explain why the chemicals are discriminated by different classification systems, but still they must have some rate-limiting interaction in common as they fit the

  3. QSAR of Tryptanthrin Analogs via Tunneling Barrier Height Imaging

    NASA Astrophysics Data System (ADS)

    Sriraman, Krishnan

    A new class of potential therapeutic agents, namely indolo[2,1-b]quinazolin-6,12- dione (tryptanthrin), and its analogues, have generated interest due to their broad spectrum of activity against a variety of pathogenic organisms. Little is known about the mechanism of action of tryptanthrins at the cellular and molecular levels. One method that has been employed to understand mechanisms of action and predict biological activities is quantitative structure activity relationship (QSAR). Since previous tryptanthrin studies could not clearly identify the pharmacophore, it was proposed to measure barrier height (BH) energy values for preparing a QSAR vs. IC50 values from the literature. The BH energy values were measured using barrier height spectroscopy which is performed using a scanning tunneling microscope (STM). Topography images (7 x 7 nm size) for tryptanthrin and three of its analogs namely 8-fluorotryptanthrin, 4-aza-8-fluorotryptanthrin, and 4-aza-8-chlorotryptanthrin were collected. Both HOMO and LUMO were collected at an applied bias of +/- 0.8 V and 0.1 nA. Excellent positive bias images (LUMO) of tryptanthrin were collected in which individual molecules and their lobes could be clearly recognized. A comparison with the density functional theory (DFT) calculated image of the LUMO resulted in an excellent match. An interesting outcome of the tryptanthrin LUMO imaging was the arrangement of molecules (parallel alignments) in the image which was explained by considering the intermolecular forces. Excellent BH images with sub-molecular resolution for 4-aza-8-fluorotryptanthrin were observed. BH values were calculated for each of the various lobes in the molecule from the BH image. Preliminary QSAR training sets were constructed using literature values of IC50 for W-2 and D-6 strains of Plasmodium falciparum as well as Leishmanai donovani versus average measured molecular barrier heights. The correlations were found to be fair for all the three pathogens. The

  4. 2D and 3D-QSAR studies on antiproliferative thiazolidine analogs

    NASA Astrophysics Data System (ADS)

    Liao, Si Yan; Qian, Li; Chen, Jin Can; Lu, Hai Liang; Zheng, Kang Cheng

    Two-dimensional (2D) and three-dimensional (3D) quantitative structure-activity relationships (QSARs) of 22 thiazolidine analogs with antiproliferative activity expressed as pIC50, which is defined as the negative value of the logarithm of necessary molar concentration of these compounds to cause 50% growth inhibition against melanoma cell lines WM-164, have been studied by using a combined method of the DFT, MM2 and statistics for 2D, as well as the comparative molecular field analysis (CoMFA) method for 3D. The established 2D-QSAR model in training set comprised of random 18 compounds shows not only significant statistical quality, but also predictive ability, with the square of adjusted correlation coefficient (R2A = 0.832) and the square of the cross-validation coefficient (q2 = 0.803). The same model was further applied to predict pIC50 values of the four compounds in the test set, and the resulting R2pred reaching 0.784, further confirms that this 2D-QSAR model has high predictive ability. The 3D-QSAR model also shows good correlative and predictive capabilities in terms of R2 (0.956) and q2 (0.615) obtained from CoMFA model. Further, the robustness of the CoMFA model was verified by bootstrapping analysis (100 runs) with R2bs (0.979) and SDbs (0.056). It is very interesting to find that the results from 2D- and 3D-QSAR analyses accord with each other, and they all show that the steric interaction plays a crucial role in determining the cytotoxicities of the compounds, and that selecting a moderate-size or appropriate-hydrophobicity substituent R as well as increasing the negative charges of C4 on phenyl ring at the same time are advantageous to improving the cytotoxicity. Such results can offer some useful theoretical references for directing the molecular design and understanding the action mechanism of this kind of compound with antiproliferative activity.

  5. 3D QSAR and docking studies of various amido and benzyl-substituted 3-amino-4-(2-cyanopyrrolidide)pyrrolidinyl analogs as DPP-IV inhibitors.

    PubMed

    Agrawal, Ritesh; Jain, Pratima; Dikshit, Subodh Narayan; Jain, Sourabh

    2013-09-01

    The article describes the development of a robust pharmacophore model and the investigation of structure activity relationship analysis of 3-amino-4-(2-cyanopyrrolidide)pyrrolidinyl analogs reported for DPP-IV inhibition using PHASE module of Schrodinger software. The present works also encompass molecular interaction study of 3-amino-4-(2- cyanopyrrolidide)pyrrolidinyl analogs on maestro 8.5 workstation. The Phase study module comprises the five points pharmacophore model (AAHPR.617), consisting two hydrogen bond acceptor (A), one Hydrophobic (H), one Positive(P) and one aromatic ring (R) and with discrete geometries as pharmacophoric feature. The developed pharmacophore model was used to derive a predictive atom-based 3D QSAR model. The obtained 3D QSAR model has an excellent correlation coefficient value (r2=0.9926) along with good statistical significance as shown by high Fisher ratio (F=671.7). The model also exhibits good predictive power, which is confirmed by high value of cross validated correlation coefficient (q2 = 0.7311). The QSAR model suggests that hydrophobic and aromatic characters are crucial for the DPP-IV inhibitory activity. The QSAR model also suggests that the inclusion of hydrophobic substituents would enhance the DPP-IV inhibition. In addition to the hydrogen bond acceptor, hydrophobic character, electro withdrawing character positively contributes to the DPP-IV inhibition. This study provides a set of guidelines for designing compounds with better DPP-IV inhibitory potency. PMID:23607811

  6. 3D-QSAR and molecular mechanics study for the differences in the azole activity against yeastlike and filamentous fungi and their relation to P450DM inhibition. 1. 3-substituted-4(3H)-quinazolinones.

    PubMed

    Fratev, Filip; Benfenati, Emilio

    2005-01-01

    A combination between 3D-QSAR and molecular mechanics (MM)-docking study was used as a tool to detail and model the mechanism of action of 46 antifungal azoles. Two methods of alignment of the ligands were performed: (i) alignment of the main skeleton without substituents and (ii) alignment of a defined substructure. The best model is characterized by q(2) with the values of 0.70 for yeastlike (yeast), 0.66 for filamentous fungi, and 0.70 for the selectivity against filamentous fungi. 3D-QSAR regression maps derived from six models were used to identify the regions responsible for the differences in the compounds activity against yeast and filamentous fungi. The binding energy of the important substructures (Local Binding Energy-LBE) and its standard deviation were calculated in order to demonstrate quantitatively the contribution of substituents reflecting the diversity of the antifungal activity. The comparisons of these results with the same regions of the contour maps indicated a good correspondence between the 3D-QSAR and MM (LBE) approaches allowing association between the maps and the participating residues in the active sites of P450DM of C. albicans and A. fumigatus. The pi-pi interactions of two or more aromatic groups of the ligands with Phe228 and Tyr132 prove to be most important for the differences in activity against C. albicans. In A. fumigatus there was a better occupation of the inner central I-spiral in the areas around the heme. For the activity against A. fumigatus the pi-pi interactions of aromatic groups of the compounds with Phe509, Phe228, and Tyr132 are significant for the activity. PMID:15921453

  7. QSAR studies on benzodiazepine receptor binding of purines and amino acid derivatives.

    PubMed

    Saha, R N; Meera, J; Agrawal, N; Gupta, S P

    1991-01-01

    Quantitative structure-activity relationship (QSAR) studies are reported on the benzodiazepine receptor binding of a series of substituted 9-benzyl-6-dimethylamino-9H-purines and N-(indol-3-ylglyoxylyl)amino acid derivatives. The nitrogen of the five membered heterocyclic ring and the polar substituent in the aromatic ring, present in both series of compounds, form important centres in the binding interaction. We conclude that the receptor must possess a strong nucleophilic centre and a polar site, and that a hydrophobic pocket exists to accommodate hydrophobic moieties. PMID:1654919

  8. 2D-Qsar for 450 types of amino acid induction peptides with a novel substructure pair descriptor having wider scope

    PubMed Central

    2011-01-01

    Background Quantitative structure-activity relationships (QSAR) analysis of peptides is helpful for designing various types of drugs such as kinase inhibitor or antigen. Capturing various properties of peptides is essential for analyzing two-dimensional QSAR. A descriptor of peptides is an important element for capturing properties. The atom pair holographic (APH) code is designed for the description of peptides and it represents peptides as the combination of thirty-six types of key atoms and their intermediate binding between two key atoms. Results The substructure pair descriptor (SPAD) represents peptides as the combination of forty-nine types of key substructures and the sequence of amino acid residues between two substructures. The size of the key substructures is larger and the length of the sequence is longer than traditional descriptors. Similarity searches on C5a inhibitor data set and kinase inhibitor data set showed that order of inhibitors become three times higher by representing peptides with SPAD, respectively. Comparing scope of each descriptor shows that SPAD captures different properties from APH. Conclusion QSAR/QSPR for peptides is helpful for designing various types of drugs such as kinase inhibitor and antigen. SPAD is a novel and powerful descriptor for various types of peptides. Accuracy of QSAR/QSPR becomes higher by describing peptides with SPAD. PMID:22047717

  9. 3-D QSARS FOR RANKING AND PRIORITIZATION OF LARGE CHEMICAL DATASETS: AN EDC CASE STUDY

    EPA Science Inventory

    The COmmon REactivity Pattern (COREPA) approach is a three-dimensional structure activity (3-D QSAR) technique that permits identification and quantification of specific global and local steroelectronic characteristics associated with a chemical's biological activity. It goes bey...

  10. Structure activity relationships to assess new chemicals under TSCA

    SciTech Connect

    Auletta, A.E.

    1990-12-31

    Under Section 5 of the Toxic Substances Control Act (TSCA), manufacturers must notify the US Environmental Protection Agency (EPA) 90 days before manufacturing, processing, or importing a new chemical substance. This is referred to as a premanufacture notice (PMN). The PMN must contain certain information including chemical identity, production volume, proposed uses, estimates of exposure and release, and any health or environmental test data that are available to the submitter. Because there is no explicit statutory authority that requires testing of new chemicals prior to their entry into the market, most PMNs are submitted with little or no data. As a result, EPA has developed special techniques for hazard assessment of PMN chemicals. These include (1) evaluation of available data on the chemical itself, (2) evaluation of data on analogues of the PMN, or evaluation of data on metabolites or analogues of metabolites of the PMN, (3) use of quantitative structure activity relationships (QSARs), and (4) knowledge and judgement of scientific assessors in the interpretation and integration of the information developed in the course of the assessment. This approach to evaluating potential hazards of new chemicals is used to identify those that are most in need of addition review of further testing. It should not be viewed as a replacement for testing. 4 tabs.

  11. Comparison of Global Reactivity Descriptors Calculated Using Various Density Functionals: A QSAR Perspective.

    PubMed

    Vijayaraj, R; Subramanian, V; Chattaraj, P K

    2009-10-13

    Conceptual density functional theory (DFT) based global reactivity descriptors are used to understand the relationship between structure, stability, and global chemical reactivity. Furthermore, these descriptors are employed in the development of quantitative structure-activity (QSAR), structure-property (QSPR), and structure-toxicity (QSTR) relationships. However, the predictive power of various relationships depends on the reliable estimates of these descriptors. The basic working equations used to calculate these descriptors contain both the ionization potential and the electron affinity of chosen molecules. Therefore, efficiency of different density functionals (DFs) in predicting the ionization potential and the electron affinity has to be systematically evaluated. With a view to benchmark the method of calculation of global reactivity descriptors, comprehensive calculations have been carried out on a series of chlorinated benzenes using a variety of density functionals employing different basis sets. In addition, to assess the utility of global reactivity descriptors, the relationships between the reactivity-electrophilicity and the structure-toxicity have been developed. The ionization potential and the electron affinity values obtained from M05-2X method using the ΔSCF approach are closer to the corresponding experimental values. This method reliably predicts these electronic properties when compared to the other DFT methods. The analysis of a series of QSTR equations reveals that computationally economic DFT functionals can be effectively and routinely applied in the development of QSAR/QSPR/QSTR. PMID:26631787

  12. Prediction of binding affinity and efficacy of thyroid hormone receptor ligands using QSAR and structure-based modeling methods

    SciTech Connect

    Politi, Regina; Rusyn, Ivan; Tropsha, Alexander

    2014-10-01

    The thyroid hormone receptor (THR) is an important member of the nuclear receptor family that can be activated by endocrine disrupting chemicals (EDC). Quantitative Structure–Activity Relationship (QSAR) models have been developed to facilitate the prioritization of THR-mediated EDC for the experimental validation. The largest database of binding affinities available at the time of the study for ligand binding domain (LBD) of THRβ was assembled to generate both continuous and classification QSAR models with an external accuracy of R{sup 2} = 0.55 and CCR = 0.76, respectively. In addition, for the first time a QSAR model was developed to predict binding affinities of antagonists inhibiting the interaction of coactivators with the AF-2 domain of THRβ (R{sup 2} = 0.70). Furthermore, molecular docking studies were performed for a set of THRβ ligands (57 agonists and 15 antagonists of LBD, 210 antagonists of the AF-2 domain, supplemented by putative decoys/non-binders) using several THRβ structures retrieved from the Protein Data Bank. We found that two agonist-bound THRβ conformations could effectively discriminate their corresponding ligands from presumed non-binders. Moreover, one of the agonist conformations could discriminate agonists from antagonists. Finally, we have conducted virtual screening of a chemical library compiled by the EPA as part of the Tox21 program to identify potential THRβ-mediated EDCs using both QSAR models and docking. We concluded that the library is unlikely to have any EDC that would bind to the THRβ. Models developed in this study can be employed either to identify environmental chemicals interacting with the THR or, conversely, to eliminate the THR-mediated mechanism of action for chemicals of concern. - Highlights: • This is the largest curated dataset for ligand binding domain (LBD) of the THRβ. • We report the first QSAR model for antagonists of AF-2 domain of THRβ. • A combination of QSAR and docking enables

  13. A quantitative structure--activity relationship model for the intrinsic activity of uncouplers of oxidative phosphorylation.

    PubMed

    Spycher, Simon; Escher, Beate I; Gasteiger, Johann

    2005-12-01

    A quantitative structure-activity relationship (QSAR) has been derived for the prediction of the activity of phenols in uncoupling oxidative and photophosphorylation. Twenty-one compounds with experimental data for uncoupling activity as well as for the acid dissociation constant, pKa, and for partitioning constants of the neutral and the charged species into model membranes were analyzed. From these measured data, the effective concentration in the membrane was derived, which allowed the study of the intrinsic activity of uncouplers within the membrane. A linear regression model for the intrinsic activity could be established using the following three descriptors: solvation free energies of the anions, an estimate for heterodimer formation describing transport processes, and pKa values describing the speciation of the phenols. In a next step, the aqueous effect concentrations were modeled by combining the model for the intrinsic uncoupling activity with descriptors accounting for the uptake into membranes. Results obtained with experimental membrane-water partitioning data were compared with the results obtained with experimental octanol-water partition coefficients, log Kow, and with calculated log Kow values. The properties of these different measures of lipophilicity were critically discussed. PMID:16359176

  14. Applications of genetic algorithms on the structure-activity relationship analysis of some cinnamamides.

    PubMed

    Hou, T J; Wang, J M; Liao, N; Xu, X J

    1999-01-01

    Quantitative structure-activity relationships (QSARs) for 35 cinnamamides were studied. By using a genetic algorithm (GA), a group of multiple regression models with high fitness scores was generated. From the statistical analyses of the descriptors used in the evolution procedure, the principal features affecting the anticonvulsant activity were found. The significant descriptors include the partition coefficient, the molar refraction, the Hammet sigma constant of the substituents on the benzene ring, and the formation energy of the molecules. It could be found that the steric complementarity and the hydrophobic interaction between the inhibitors and the receptor were very important to the biological activity, while the contribution of the electronic effect was not so obvious. Moreover, by construction of the spline models for these four principal descriptors, the effective range for each descriptor was identified. PMID:10529984

  15. Fish acute toxicity syndromes and their use in the QSAR approach to hazard assessment

    SciTech Connect

    McKim, J.M.; Bradbury, S.P.; Niemi, G.J.

    1987-04-01

    Implementation of the Toxic Substances Control Act of 1977 creates the need to reliably establish testing priorities because laboratory resources are limited and the number of industrial chemicals requiring evaluation is overwhelming. The use of quantitative structure activity relationship (QSAR) models as rapid and predictive screening tools to select more potentially hazardous chemicals for in-depth laboratory evaluation has been proposed. Further implementation and refinement of quantitative structure-toxicity relationships in aqueous toxicology and hazard assessment requires the development of a mode-of-action database. With such a database, a qualitative structure-activity relationship can be formulated to assign the proper mode of action, and respective QSAR, to a given chemical structure. In this review, the development of fish acute toxicity syndromes (FATS), which are toxic-response sets based on various behavioral and physiological-biochemical measurements, and their projected use in the mode-of-action database are outlined. Using behavioral parameters monitored in the fathead minnow during acute toxicity testing, FATS associated with acetylcholinesterase (AChE) inhibitors and narcotics could be reliably predicted. However, compounds classified as oxidative phosphorylation uncouplers or stimulants could not be resolved. Refinement of this approach by using respiratory-cardiovascular responses in the rainbow trout, enabled FATS associated with AChE inhibitors, convulsants, narcotics, respiratory blockers, respiratory membrane irritants, and uncouplers to be correctly predicted.

  16. Insights on Cytochrome P450 Enzymes and Inhibitors Obtained Through QSAR Studies

    PubMed Central

    Sridhar, Jayalakshmi; Liu, Jiawang; Foroozesh, Maryam; Stevens, Cheryl L. Klein

    2013-01-01

    The cytochrome P450 (CYP) superfamily of heme enzymes play an important role in the metabolism of a large number of endogenous and exogenous compounds, including most of the drugs currently on the market. Inhibitors of CYP enzymes have important roles in the treatment of several disease conditions such as numerous cancers and fungal infections in addition to their critical role in drug-drug interactions. Structure activity relationships (SAR), and three-dimensional quantitative structure activity relationships (3D-QSAR) represent important tools in understanding the interactions of the inhibitors with the active sites of the CYP enzymes. A comprehensive account of the QSAR studies on the major human CYPs 1A1, 1A2, 1B1, 2A6, 2B6, 2C9, 2C19, 2D6, 2E1, 3A4 and a few other CYPs are detailed in this review which will provide us with an insight into the individual/common characteristics of the active sites of these enzymes and the enzyme-inhibitor interactions. PMID:22864238

  17. Quantitative structure-activity relationship models for prediction of the toxicity of polybrominated diphenyl ether congeners.

    PubMed

    Wang, Yawei; Liu, Huanxiang; Zhao, Chunyan; Liu, Hanxia; Cai, Zongwei; Jiang, Guibin

    2005-07-01

    Levels of polybrominated diphenyl ethers (PBDEs) are increasing in the environment and may cause long-term health problems in humans. The similarity in the chemical structures of PBDEs and other halogenated aromatic pollutants hints on the possibility that they might share similar toxicological effects. In this work, three-dimensional quantitative structure activity relationships (3-D-QSAR) models, using comparative molecular field analysis (CoMFA) and comparative similarity indices analysis (CoMSIA), were built based on calculated structural indices and a reported experimental toxicology index (aryl hydrocarbon receptor relative binding affinities, RBA) of 18 PBDEs congeners, to determine the factors required for the RBA of these PBDEs. After performing leave-one-out cross-validation, satisfactory results were obtained with cross-validation O2 and R2 values of 0.580 and 0.995 by the CoMFA model and 0.680 and 0.982 by the CoMSIA model, respectively. The results showed clearly that the nonplanar conformations of PBDEs result in the lowest energy level and that the electrostatic index was the main factor reflecting the RBA of PBDEs. The two QSAR models were then used to predict the RBA value of 46 PBDEs for which experimental values are unavailable at present. PMID:16053097

  18. Deep neural nets as a method for quantitative structure-activity relationships.

    PubMed

    Ma, Junshui; Sheridan, Robert P; Liaw, Andy; Dahl, George E; Svetnik, Vladimir

    2015-02-23

    Neural networks were widely used for quantitative structure-activity relationships (QSAR) in the 1990s. Because of various practical issues (e.g., slow on large problems, difficult to train, prone to overfitting, etc.), they were superseded by more robust methods like support vector machine (SVM) and random forest (RF), which arose in the early 2000s. The last 10 years has witnessed a revival of neural networks in the machine learning community thanks to new methods for preventing overfitting, more efficient training algorithms, and advancements in computer hardware. In particular, deep neural nets (DNNs), i.e. neural nets with more than one hidden layer, have found great successes in many applications, such as computer vision and natural language processing. Here we show that DNNs can routinely make better prospective predictions than RF on a set of large diverse QSAR data sets that are taken from Merck's drug discovery effort. The number of adjustable parameters needed for DNNs is fairly large, but our results show that it is not necessary to optimize them for individual data sets, and a single set of recommended parameters can achieve better performance than RF for most of the data sets we studied. The usefulness of the parameters is demonstrated on additional data sets not used in the calibration. Although training DNNs is still computationally intensive, using graphical processing units (GPUs) can make this issue manageable. PMID:25635324

  19. Identification of 3-Nitro-2,4,6-trihydroxybenzamide Derivatives as Photosynthetic Electron Transport Inhibitors by QSAR and Pharmacophore Studies.

    PubMed

    Sharma, Mukesh C

    2016-06-01

    In the present investigation, quantitative structure-activity relationship (QSAR) analysis was performed on a data set consisting of structurally diverse compounds in order to investigate the role of their structural features on their photosynthetic electron transport Inhibitors. The best 2D-QSAR model was selected, having correlation coefficient r (2) = 0.8544 and cross-validated squared correlation coefficient q (2) = 0.7139 with external predictive ability of pred_r (2) = 0.7753. The results obtained in this study indicate that the presence of hydroxy and nitro groups, expressed by the SsOHcount and SddsN (nitro) count, is the most relevant molecular property determining efficiency of photosynthetic inhibitory. Molecular field analysis was used to construct the best k-nearest neighbor (kNN-MFA)-based 3D-QSAR model using SA-PLS method, showing good correlative and predictive capabilities in terms of [Formula: see text] and [Formula: see text]. The pharmacophore model includes three features viz. hydrogen bond donor, hydrogen bond acceptor, and one aromatic feature. The developed model was found to be predictive and can be used to design potent photosynthetic electron transport activities prior to their synthesis for further lead modification. PMID:26245276

  20. Large-scale structure-activity relationship study of hepatitis C virus NS5B polymerase inhibition using SMILES-based descriptors.

    PubMed

    Worachartcheewan, Apilak; Prachayasittikul, Virapong; Toropova, Alla P; Toropov, Andrey A; Nantasenamat, Chanin

    2015-11-01

    Hepatitis C virus (HCV) is composed of structural and non-structural proteins involved in viral transcription and propagation. In particular, NS5B is an RNA-dependent RNA polymerase for viral transcription and genome replication and is a target for designing anti-viral agents. In this study, classification and quantitative structure-activity relationship (QSAR) models of HCV NS5B inhibitors were constructed using the Correlation and Logic software. Molecular descriptors for a set of 970 HCV NS5B inhibitors were encoded using the simplified molecular input line entry system notation, and predictive models were built via the Monte Carlo method. The QSAR models provided acceptable correlation coefficients of [Formula: see text] and [Formula: see text] in the ranges of 0.6038-0.7344 and 0.6171-0.7294, respectively, while the classification models displayed sensitivity, specificity, and accuracy in ranges of 88.24-98.84, 83.87-93.94, and 86.50-94.41 %, respectively. Furthermore, molecular fragments as substructures involved in increased and decreased inhibitory activities were explored. The results provide information on QSAR and classification models for high-throughput screening and mechanistic insights into the inhibitory activity of HCV NS5B polymerase. PMID:26164590

  1. Prediction of binding affinity and efficacy of thyroid hormone receptor ligands using QSAR and structure based modeling methods

    PubMed Central

    Politi, Regina; Rusyn, Ivan; Tropsha, Alexander

    2016-01-01

    The thyroid hormone receptor (THR) is an important member of the nuclear receptor family that can be activated by endocrine disrupting chemicals (EDC). Quantitative Structure-Activity Relationship (QSAR) models have been developed to facilitate the prioritization of THR-mediated EDC for the experimental validation. The largest database of binding affinities available at the time of the study for ligand binding domain (LBD) of THRβ was assembled to generate both continuous and classification QSAR models with an external accuracy of R2=0.55 and CCR=0.76, respectively. In addition, for the first time a QSAR model was developed to predict binding affinities of antagonists inhibiting the interaction of coactivators with the AF-2 domain of THRβ (R2=0.70). Furthermore, molecular docking studies were performed for a set of THRβ ligands (57 agonists and 15 antagonists of LBD, 210 antagonists of the AF-2 domain, supplemented by putative decoys/non-binders) using several THRβ structures retrieved from the Protein Data Bank. We found that two agonist-bound THRβ conformations could effectively discriminate their corresponding ligands from presumed non-binders. Moreover, one of the agonist conformations could discriminate agonists from antagonists. Finally, we have conducted virtual screening of a chemical library compiled by the EPA as part of the Tox21 program to identify potential THRβ-mediated EDCs using both QSAR models and docking. We concluded that the library is unlikely to have any EDC that would bind to the THRβ. Models developed in this study can be employed either to identify environmental chemicals interacting with the THR or, conversely, to eliminate the THR-mediated mechanism of action for chemicals of concern. PMID:25058446

  2. Towards cheminformatics-based estimation of drug therapeutic index: Predicting the protective index of anticonvulsants using a new quantitative structure-index relationship approach.

    PubMed

    Chen, Shangying; Zhang, Peng; Liu, Xin; Qin, Chu; Tao, Lin; Zhang, Cheng; Yang, Sheng Yong; Chen, Yu Zong; Chui, Wai Keung

    2016-06-01

    The overall efficacy and safety profile of a new drug is partially evaluated by the therapeutic index in clinical studies and by the protective index (PI) in preclinical studies. In-silico predictive methods may facilitate the assessment of these indicators. Although QSAR and QSTR models can be used for predicting PI, their predictive capability has not been evaluated. To test this capability, we developed QSAR and QSTR models for predicting the activity and toxicity of anticonvulsants at accuracy levels above the literature-reported threshold (LT) of good QSAR models as tested by both the internal 5-fold cross validation and external validation method. These models showed significantly compromised PI predictive capability due to the cumulative errors of the QSAR and QSTR models. Therefore, in this investigation a new quantitative structure-index relationship (QSIR) model was devised and it showed improved PI predictive capability that superseded the LT of good QSAR models. The QSAR, QSTR and QSIR models were developed using support vector regression (SVR) method with the parameters optimized by using the greedy search method. The molecular descriptors relevant to the prediction of anticonvulsant activities, toxicities and PIs were analyzed by a recursive feature elimination method. The selected molecular descriptors are primarily associated with the drug-like, pharmacological and toxicological features and those used in the published anticonvulsant QSAR and QSTR models. This study suggested that QSIR is useful for estimating the therapeutic index of drug candidates. PMID:27262528

  3. Parameters for Pyrethroid Insecticide QSAR and PBPK/PD Models for Human Risk Assessment

    EPA Science Inventory

    This pyrethroid insecticide parameter review is an extension of our interest in developing quantitative structure–activity relationship–physiologically based pharmacokinetic/pharmacodynamic (QSAR-PBPK/PD) models for assessing health risks, which interest started with the organoph...

  4. Exploring the ligand recognition properties of the human vasopressin V1a receptor using QSAR and molecular modeling studies.

    PubMed

    Contreras-Romo, Martha C; Martínez-Archundia, Marlet; Deeb, Omar; Slusarz, Magdalena J; Ramírez-Salinas, Gema; Garduño-Juárez, Ramón; Quintanar-Stephano, Andrés; Ramírez-Galicia, Guillermo; Correa-Basurto, José

    2014-02-01

    Vaptans are compounds that act as non-peptide vasopressin receptor antagonists. These compounds have diverse chemical structures. In this study, we used a combined approach of protein folding, molecular dynamics simulations, docking, and quantitative structure-activity relationship (QSAR) to elucidate the detailed interaction of the vasopressin receptor V1a (V1aR) with some of its blockers (134). QSAR studies were performed using MLR analysis and were gathered into one group to perform an artificial neural network (ANN) analysis. For each molecule, 1481 molecular descriptors were calculated. Additionally, 15 quantum chemical descriptors were calculated. The final equation was developed by choosing the optimal combination of descriptors after removing the outliers. Molecular modeling enabled us to obtain a reliable tridimensional model of V1aR. The docking results indicated that the great majority of ligands reach the binding site under π-π, π-cation, and hydrophobic interactions. The QSAR studies demonstrated that the heteroatoms N and O are important for ligand recognition, which could explain the structural diversity of ligands that reach V1aR. PMID:24010681

  5. Comparison of Performance of Docking, LIE, Metadynamics and QSAR in Predicting Binding Affinity of Benzenesulfonamides.

    PubMed

    Raškevičius, Vytautas; Kairys, Visvaldas

    2015-01-01

    The design of inhibitors specific for one relevant carbonic anhydrase isozyme is the major challenge in the new therapeutic agents development. Comparative computational chemical structure and biological activity relationship studies on a series of carbonic anhydrase II inhibitors, benzenesulfonamide derivatives, bearing pyrimidine moieties are reported in this paper using docking, Linear Interaction Energy (LIE), Metadynamics and Quantitative Structure Activity Relationship (QSAR) methods. The computed binding affinities were compared with the experimental data with the goal to explore strengths and weaknesses of various approaches applied to the investigated carbonic anhydrase/inhibitor system. From the tested methods initially only QSAR showed promising results (R2=0.83-0.89 between experimentally determined versus predicted pKd values.). Possible reasons for this performance were discussed. A modification of the LIE method was suggested which used an alternative LIE-like equation yielding significantly improved results (R2 between the experimentally determined versus the predicted ΔG(bind) improved from 0.24 to 0.50). PMID:26373640

  6. Improving quantitative structure-activity relationships through multiobjective optimization.

    PubMed

    Nicolotti, Orazio; Giangreco, Ilenia; Miscioscia, Teresa Fabiola; Carotti, Angelo

    2009-10-01

    A multiobjective optimization algorithm was proposed for the automated integration of structure- and ligand-based molecular design. Driven by a genetic algorithm, the herein proposed approach enabled the detection of a number of trade-off QSAR models accounting simultaneously for two independent objectives. The first was biased toward best regressions among docking scores and biological affinities; the second minimized the atom displacements from a properly established crystal-based binding topology. Based on the concept of dominance, 3D QSAR equivalent models profiled the Pareto frontier and were, thus, designated as nondominated solutions of the search space. K-means clustering was, then, operated to select a representative subset of the available trade-off models. These were effectively subjected to GRID/GOLPE analyses for quantitatively featuring molecular determinants of ligand binding affinity. More specifically, it was demonstrated that a) diverse binding conformations occurred on the basis of the ligand ability to profitably contact different part of protein binding site; b) enzyme selectivity was better approached and interpreted by combining diverse equivalent models; and c) trade-off models were successful and even better than docking virtual screening, in retrieving at high sensitivity active hits from a large pool of chemically similar decoys. The approach was tested on a large series, very well-known to QSAR practitioners, of 3-amidinophenylalanine inhibitors of thrombin and trypsin, two serine proteases having rather different biological actions despite a high sequence similarity. PMID:19785453

  7. An integrated QSAR-PBK/D modelling approach for predicting detoxification and DNA adduct formation of 18 acyclic food-borne α,β-unsaturated aldehydes

    SciTech Connect

    Kiwamoto, R. Spenkelink, A.; Rietjens, I.M.C.M.; Punt, A.

    2015-01-01

    Acyclic α,β-unsaturated aldehydes present in food raise a concern because the α,β-unsaturated aldehyde moiety is considered a structural alert for genotoxicity. However, controversy remains on whether in vivo at realistic dietary exposure DNA adduct formation is significant. The aim of the present study was to develop physiologically based kinetic/dynamic (PBK/D) models to examine dose-dependent detoxification and DNA adduct formation of a group of 18 food-borne acyclic α,β-unsaturated aldehydes without 2- or 3-alkylation, and with no more than one conjugated double bond. Parameters for the PBK/D models were obtained using quantitative structure–activity relationships (QSARs) defined with a training set of six selected aldehydes. Using the QSARs, PBK/D models for the other 12 aldehydes were defined. Results revealed that DNA adduct formation in the liver increases with decreasing bulkiness of the molecule especially due to less efficient detoxification. 2-Propenal (acrolein) was identified to induce the highest DNA adduct levels. At realistic dietary intake, the predicted DNA adduct levels for all aldehydes were two orders of magnitude lower than endogenous background levels observed in disease free human liver, suggesting that for all 18 aldehydes DNA adduct formation is negligible at the relevant levels of dietary intake. The present study provides a proof of principle for the use of QSAR-based PBK/D modelling to facilitate group evaluations and read-across in risk assessment. - Highlights: • Physiologically based in silico models were made for 18 α,β-unsaturated aldehydes. • Kinetic parameters were determined by in vitro incubations and a QSAR approach. • DNA adduct formation was negligible at levels relevant for dietary intake. • The use of QSAR-based PBK/D modelling facilitates group evaluations and read-across.

  8. QSAR Accelerated Discovery of Potent Ice Recrystallization Inhibitors

    NASA Astrophysics Data System (ADS)

    Briard, Jennie G.; Fernandez, Michael; de Luna, Phil; Woo, Tom. K.; Ben, Robert N.

    2016-05-01

    Ice recrystallization is the main contributor to cell damage and death during the cryopreservation of cells and tissues. Over the past five years, many small carbohydrate-based molecules were identified as ice recrystallization inhibitors and several were shown to reduce cryoinjury during the cryopreservation of red blood cells (RBCs) and hematopoietic stems cells (HSCs). Unfortunately, clear structure-activity relationships have not been identified impeding the rational design of future compounds possessing ice recrystallization inhibition (IRI) activity. A set of 124 previously synthesized compounds with known IRI activities were used to calibrate 3D-QSAR classification models using GRid INdependent Descriptors (GRIND) derived from DFT level quantum mechanical calculations. Partial least squares (PLS) model was calibrated with 70% of the data set which successfully identified 80% of the IRI active compounds with a precision of 0.8. This model exhibited good performance in screening the remaining 30% of the data set with 70% of active additives successfully recovered with a precision of ~0.7 and specificity of 0.8. The model was further applied to screen a new library of aryl-alditol molecules which were then experimentally synthesized and tested with a success rate of 82%. Presented is the first computer-aided high-throughput experimental screening for novel IRI active compounds.

  9. QSAR Accelerated Discovery of Potent Ice Recrystallization Inhibitors.

    PubMed

    Briard, Jennie G; Fernandez, Michael; De Luna, Phil; Woo, Tom K; Ben, Robert N

    2016-01-01

    Ice recrystallization is the main contributor to cell damage and death during the cryopreservation of cells and tissues. Over the past five years, many small carbohydrate-based molecules were identified as ice recrystallization inhibitors and several were shown to reduce cryoinjury during the cryopreservation of red blood cells (RBCs) and hematopoietic stems cells (HSCs). Unfortunately, clear structure-activity relationships have not been identified impeding the rational design of future compounds possessing ice recrystallization inhibition (IRI) activity. A set of 124 previously synthesized compounds with known IRI activities were used to calibrate 3D-QSAR classification models using GRid INdependent Descriptors (GRIND) derived from DFT level quantum mechanical calculations. Partial least squares (PLS) model was calibrated with 70% of the data set which successfully identified 80% of the IRI active compounds with a precision of 0.8. This model exhibited good performance in screening the remaining 30% of the data set with 70% of active additives successfully recovered with a precision of ~0.7 and specificity of 0.8. The model was further applied to screen a new library of aryl-alditol molecules which were then experimentally synthesized and tested with a success rate of 82%. Presented is the first computer-aided high-throughput experimental screening for novel IRI active compounds. PMID:27216585

  10. QSAR Accelerated Discovery of Potent Ice Recrystallization Inhibitors

    PubMed Central

    Briard, Jennie G.; Fernandez, Michael; De Luna, Phil; Woo, Tom. K.; Ben, Robert N.

    2016-01-01

    Ice recrystallization is the main contributor to cell damage and death during the cryopreservation of cells and tissues. Over the past five years, many small carbohydrate-based molecules were identified as ice recrystallization inhibitors and several were shown to reduce cryoinjury during the cryopreservation of red blood cells (RBCs) and hematopoietic stems cells (HSCs). Unfortunately, clear structure-activity relationships have not been identified impeding the rational design of future compounds possessing ice recrystallization inhibition (IRI) activity. A set of 124 previously synthesized compounds with known IRI activities were used to calibrate 3D-QSAR classification models using GRid INdependent Descriptors (GRIND) derived from DFT level quantum mechanical calculations. Partial least squares (PLS) model was calibrated with 70% of the data set which successfully identified 80% of the IRI active compounds with a precision of 0.8. This model exhibited good performance in screening the remaining 30% of the data set with 70% of active additives successfully recovered with a precision of ~0.7 and specificity of 0.8. The model was further applied to screen a new library of aryl-alditol molecules which were then experimentally synthesized and tested with a success rate of 82%. Presented is the first computer-aided high-throughput experimental screening for novel IRI active compounds. PMID:27216585

  11. Natural products as insecticides: the biology, biochemistry and quantitative structure-activity relationships of spinosyns and spinosoids.

    PubMed

    Sparks, T C; Crouse, G D; Durst, G

    2001-10-01

    The spinosyns, a novel family of insecticidal macrocyclic lactones, are active on a wide variety of insect pests, especially lepidopterans and dipterans. The biological activity of a mixture (spinosad; Tracer, Spin-Tor, Success) of the two most abundant spinosyns (spinosyns A and D) against pest insects is on a par with that of many pyrethroid insecticides. The spinosyns also exhibit a very favorable environmental and toxicological profile, and possess a mode of action that appears unique, with studies to date suggesting that both nicotinic and gamma-aminobutryic acid receptor functions are altered in a novel manner. Compared to pyrethroids such as cypermethrin, spinosyn A is slow to penetrate into insect larvae such as tobacco budworm larvae (Heliothis virescens); however, once inside the insect, spinosyn A is not readily metabolized. To date, more than 20 spinosyns and more than 800 spinosoids (semi-synthetic analogs) have been isolated or synthesized, respectively. Artificial neural network-based quantitative structure activity relationship (QSAR) studies for the spinosyns suggested that modification of the 2',3',4'-tri-O-methylrhamnosyl moiety could improve activity and several spinosoids incorporating these modifications exhibited markedly improved lepidopteran activity compared to spinosad. Multiple linear regression-based QSAR studies also suggest that whole molecule properties such as CLogP and MOPAC dipole moment can explain much of the biological activity observed for the spinosyns and closely related spinosoids. PMID:11695182

  12. 4D-Fingerprint Categorical QSAR Models for Skin Sensitization Based on Classification Local Lymph Node Assay Measures

    PubMed Central

    Li, Yi; Tseng, Yufeng J.; Pan, Dahua; Liu, Jianzhong; Kern, Petra S.; Gerberick, G. Frank; Hopfinger, Anton J.

    2008-01-01

    Currently, the only validated methods to identify skin sensitization effects are in vivo models, such as the Local Lymph Node Assay (LLNA) and guinea pig studies. There is a tremendous need, in particular due to novel legislation, to develop animal alternatives, eg. Quantitative Structure-Activity Relationship (QSAR) models. Here, QSAR models for skin sensitization using LLNA data have been constructed. The descriptors used to generate these models are derived from the 4D-molecular similarity paradigm and are referred to as universal 4D-fingerprints. A training set of 132 structurally diverse compounds and a test set of 15 structurally diverse compounds were used in this study. The statistical methodologies used to build the models are logistic regression (LR), and partial least square coupled logistic regression (PLS-LR), which prove to be effective tools for studying skin sensitization measures expressed in the two categorical terms of sensitizer and non-sensitizer. QSAR models with low values of the Hosmer-Lemeshow goodness-of-fit statistic, χHL2, are significant and predictive. For the training set, the cross-validated prediction accuracy of the logistic regression models ranges from 77.3% to 78.0%, while that of PLS-logistic regression models ranges from 87.1% to 89.4%. For the test set, the prediction accuracy of logistic regression models ranges from 80.0%-86.7%, while that of PLS-logistic regression models ranges from 73.3%-80.0%. The QSAR models are made up of 4D-fingerprints related to aromatic atoms, hydrogen bond acceptors and negatively partially charged atoms. PMID:17226934

  13. Predicting Toxicities of Diverse Chemical Pesticides in Multiple Avian Species Using Tree-Based QSAR Approaches for Regulatory Purposes.

    PubMed

    Basant, Nikita; Gupta, Shikha; Singh, Kunwar P

    2015-07-27

    A comprehensive safety evaluation of chemicals should require toxicity assessment in both the aquatic and terrestrial test species. Due to the application practices and nature of chemical pesticides, the avian toxicity testing is considered as an essential requirement in the risk assessment process. In this study, tree-based multispecies QSAR (quantitative-structure activity relationship) models were constructed for predicting the avian toxicity of pesticides using a set of nine descriptors derived directly from the chemical structures and following the OECD guidelines. Accordingly, the Bobwhite quail toxicity data was used to construct the QSAR models (SDT, DTF, DTB) and were externally validated using the toxicity data in four other test species (Mallard duck, Ring-necked pheasant, Japanese quail, House sparrow). Prior to the model development, the diversity in the chemical structures and end-point were verified. The external predictive power of the QSAR models was tested through rigorous validation deriving a wide series of statistical checks. Intercorrelation analysis and PCA methods provided information on the association of the molecular descriptors related to MW and topology. The S36 and MW were the most influential descriptors identified by DTF and DTB models. The DTF and DTB performed better than the SDT model and yielded a correlation (R(2)) of 0.945 and 0.966 between the measured and predicted toxicity values in test data array. Both these models also performed well in four other test species (R(2) > 0.918). ChemoTyper was used to identify the substructure alerts responsible for the avian toxicity. The results suggest for the appropriateness of the developed QSAR models to reliably predict the toxicity of pesticides in multiple avian test species and can be useful tools in screening the new chemical pesticides for regulatory purposes. PMID:26158470

  14. 3D-QSAR and docking studies of flavonoids as potent Escherichia coli inhibitors.

    PubMed

    Fang, Yajing; Lu, Yulin; Zang, Xixi; Wu, Ting; Qi, XiaoJuan; Pan, Siyi; Xu, Xiaoyun

    2016-01-01

    Flavonoids are potential antibacterial agents. However, key substituents and mechanism for their antibacterial activity have not been fully investigated. The quantitative structure-activity relationship (QSAR) and molecular docking of flavonoids relating to potent anti-Escherichia coli agents were investigated. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were developed by using the pIC50 values of flavonoids. The cross-validated coefficient (q(2)) values for CoMFA (0.743) and for CoMSIA (0.708) were achieved, illustrating high predictive capabilities. Selected descriptors for the CoMFA model were ClogP (logarithm of the octanol/water partition coefficient), steric and electrostatic fields, while, ClogP, electrostatic and hydrogen bond donor fields were used for the CoMSIA model. Molecular docking results confirmed that half of the tested flavonoids inhibited DNA gyrase B (GyrB) by interacting with adenosine-triphosphate (ATP) pocket in a same orientation. Polymethoxyl flavones, flavonoid glycosides, isoflavonoids changed their orientation, resulting in a decrease of inhibitory activity. Moreover, docking results showed that 3-hydroxyl, 5-hydroxyl, 7-hydroxyl and 4-carbonyl groups were found to be crucial active substituents of flavonoids by interacting with key residues of GyrB, which were in agreement with the QSAR study results. These results provide valuable information for structure requirements of flavonoids as antibacterial agents. PMID:27049530

  15. The QSAR and docking calculations of fullerene derivatives as HIV-1 protease inhibitors

    NASA Astrophysics Data System (ADS)

    Saleh, Noha A.

    2015-02-01

    The inhibition of HIV-1 protease is considered as one of the most important targets for drug design and the deactivation of HIV-1. In the present work, the fullerene surface (C60) is modified by adding oxygen atoms as well as hydroxymethylcarbonyl (HMC) groups to form 6 investigated fullerene derivative compounds. These compounds have one, two, three, four or five O atoms + HMC groups at different positions on phenyl ring. The effect of the repeating of these groups on the ability of suggested compounds to inhibit the HIV protease is studied by calculating both Quantitative Structure Activity Relationship (QSAR) properties and docking simulation. Based on the QSAR descriptors, the solubility and the hydrophilicity of studied fullerene derivatives increased with increasing the number of oxygen atoms + HMC groups in the compound. While docking calculations indicate that, the compound with two oxygen atoms + HMC groups could interact and binds with HIV-1 protease active site. This is could be attributed to the active site residues of HIV-1 protease are hydrophobic except the two aspartic acids. So that, the increase in the hydrophilicity and polarity of the compound is preventing and/or decreasing the hydrophobic interaction between the compound and HIV-1 protease active site.

  16. 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.

  17. The QSAR and docking calculations of fullerene derivatives as HIV-1 protease inhibitors.

    PubMed

    Saleh, Noha A

    2014-10-30

    The inhibition of HIV-1 protease is considered as one of the most important targets for drug design and the deactivation of HIV-1. In the present work, the fullerene surface (C60) is modified by adding oxygen atoms as well as hydroxymethylcarbonyl (HMC) groups to form 6 investigated fullerene derivative compounds. These compounds have one, two, three, four or five O atoms+HMC groups at different positions on phenyl ring. The effect of the repeating of these groups on the ability of suggested compounds to inhibit the HIV protease is studied by calculating both Quantitative Structure Activity Relationship (QSAR) properties and docking simulation. Based on the QSAR descriptors, the solubility and the hydrophilicity of studied fullerene derivatives increased with increasing the number of oxygen atoms+HMC groups in the compound. While docking calculations indicate that, the compound with two oxygen atoms+HMC groups could interact and binds with HIV-1 protease active site. This is could be attributed to the active site residues of HIV-1 protease are hydrophobic except the two aspartic acids. So that, the increase in the hydrophilicity and polarity of the compound is preventing and/or decreasing the hydrophobic interaction between the compound and HIV-1 protease active site. PMID:25459714

  18. 3D-QSAR and docking studies of flavonoids as potent Escherichia coli inhibitors

    PubMed Central

    Fang, Yajing; Lu, Yulin; Zang, Xixi; Wu, Ting; Qi, XiaoJuan; Pan, Siyi; Xu, Xiaoyun

    2016-01-01

    Flavonoids are potential antibacterial agents. However, key substituents and mechanism for their antibacterial activity have not been fully investigated. The quantitative structure-activity relationship (QSAR) and molecular docking of flavonoids relating to potent anti-Escherichia coli agents were investigated. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were developed by using the pIC50 values of flavonoids. The cross-validated coefficient (q2) values for CoMFA (0.743) and for CoMSIA (0.708) were achieved, illustrating high predictive capabilities. Selected descriptors for the CoMFA model were ClogP (logarithm of the octanol/water partition coefficient), steric and electrostatic fields, while, ClogP, electrostatic and hydrogen bond donor fields were used for the CoMSIA model. Molecular docking results confirmed that half of the tested flavonoids inhibited DNA gyrase B (GyrB) by interacting with adenosine-triphosphate (ATP) pocket in a same orientation. Polymethoxyl flavones, flavonoid glycosides, isoflavonoids changed their orientation, resulting in a decrease of inhibitory activity. Moreover, docking results showed that 3-hydroxyl, 5-hydroxyl, 7-hydroxyl and 4-carbonyl groups were found to be crucial active substituents of flavonoids by interacting with key residues of GyrB, which were in agreement with the QSAR study results. These results provide valuable information for structure requirements of flavonoids as antibacterial agents. PMID:27049530

  19. Vascular endothelial growth factor receptor-2 (VEGFR-2) inhibitors: development and validation of predictive 3-D QSAR models through extensive ligand- and structure-based approaches.

    PubMed

    Ragno, Rino; Ballante, Flavio; Pirolli, Adele; Wickersham, Richard B; Patsilinakos, Alexandros; Hesse, Stéphanie; Perspicace, Enrico; Kirsch, Gilbert

    2015-08-01

    Vascular endothelial growth factor receptor-2, (VEGFR-2), is a key element in angiogenesis, the process by which new blood vessels are formed, and is thus an important pharmaceutical target. Here, 3-D quantitative structure-activity relationship (3-D QSAR) were used to build a quantitative screening and pharmacophore model of the VEGFR-2 receptors for design of inhibitors with improved activities. Most of available experimental data information has been used as training set to derive optimized and fully cross-validated eight mono-probe and a multi-probe quantitative models. Notable is the use of 262 molecules, aligned following both structure-based and ligand-based protocols, as external test set confirming the 3-D QSAR models' predictive capability and their usefulness in design new VEGFR-2 inhibitors. From a survey on literature, this is the first generation of a wide-ranging computational medicinal chemistry application on VEGFR2 inhibitors. PMID:26194852

  20. Support vector machine based training of multilayer feedforward neural networks as optimized by particle swarm algorithm: application in QSAR studies of bioactivity of organic compounds.

    PubMed

    Lin, Wei-Qi; Jiang, Jian-Hui; Zhou, Yan-Ping; Wu, Hai-Long; Shen, Guo-Li; Yu, Ru-Qin

    2007-01-30

    Multilayer feedforward neural networks (MLFNNs) are important modeling techniques widely used in QSAR studies for their ability to represent nonlinear relationships between descriptors and activity. However, the problems of overfitting and premature convergence to local optima still pose great challenges in the practice of MLFNNs. To circumvent these problems, a support vector machine (SVM) based training algorithm for MLFNNs has been developed with the incorporation of particle swarm optimization (PSO). The introduction of the SVM based training mechanism imparts the developed algorithm with inherent capacity for combating the overfitting problem. Moreover, with the implementation of PSO for searching the optimal network weights, the SVM based learning algorithm shows relatively high efficiency in converging to the optima. The proposed algorithm has been evaluated using the Hansch data set. Application to QSAR studies of the activity of COX-2 inhibitors is also demonstrated. The results reveal that this technique provides superior performance to backpropagation (BP) and PSO training neural networks. PMID:17186488

  1. The Interplay between QSAR/QSPR Studies and Partial Order Ranking and Formal Concept Analyses

    PubMed Central

    Carlsen, Lars

    2009-01-01

    The often observed scarcity of physical-chemical and well as toxicological data hampers the assessment of potentially hazardous chemicals released to the environment. In such cases Quantitative Structure-Activity Relationships/Quantitative Structure-Property Relationships (QSAR/QSPR) constitute an obvious alternative for rapidly, effectively and inexpensively generatng missing experimental values. However, typically further treatment of the data appears necessary, e.g., to elucidate the possible relations between the single compounds as well as implications and associations between the various parameters used for the combined characterization of the compounds under investigation. In the present paper the application of QSAR/QSPR in combination with Partial Order Ranking (POR) methodologies will be reviewed and new aspects using Formal Concept Analysis (FCA) will be introduced. Where POR constitutes an attractive method for, e.g., prioritizing a series of chemical substances based on a simultaneous inclusion of a range of parameters, FCA gives important information on the implications associations between the parameters. The combined approach thus constitutes an attractive method to a preliminary assessment of the impact on environmental and human health by primary pollutants or possibly by a primary pollutant well as a possible suite of transformation subsequent products that may be both persistent in and bioaccumulating and toxic. The present review focus on the environmental – and human health impact by residuals of the rocket fuel 1,1-dimethylhydrazine (heptyl) and its transformation products as an illustrative example. PMID:19468330

  2. Combinatorial Pharmacophore-Based 3D-QSAR Analysis and Virtual Screening of FGFR1 Inhibitors

    PubMed Central

    Zhou, Nannan; Xu, Yuan; Liu, Xian; Wang, Yulan; Peng, Jianlong; Luo, Xiaomin; Zheng, Mingyue; Chen, Kaixian; Jiang, Hualiang

    2015-01-01

    The fibroblast growth factor/fibroblast growth factor receptor (FGF/FGFR) signaling pathway plays crucial roles in cell proliferation, angiogenesis, migration, and survival. Aberration in FGFRs correlates with several malignancies and disorders. FGFRs have proved to be attractive targets for therapeutic intervention in cancer, and it is of high interest to find FGFR inhibitors with novel scaffolds. In this study, a combinatorial three-dimensional quantitative structure-activity relationship (3D-QSAR) model was developed based on previously reported FGFR1 inhibitors with diverse structural skeletons. This model was evaluated for its prediction performance on a diverse test set containing 232 FGFR inhibitors, and it yielded a SD value of 0.75 pIC50 units from measured inhibition affinities and a Pearson’s correlation coefficient R2 of 0.53. This result suggests that the combinatorial 3D-QSAR model could be used to search for new FGFR1 hit structures and predict their potential activity. To further evaluate the performance of the model, a decoy set validation was used to measure the efficiency of the model by calculating EF (enrichment factor). Based on the combinatorial pharmacophore model, a virtual screening against SPECS database was performed. Nineteen novel active compounds were successfully identified, which provide new chemical starting points for further structural optimization of FGFR1 inhibitors. PMID:26110383

  3. Synthesis, Herbicidal Activity, and QSAR of Novel N-Benzothiazolyl- pyrimidine-2,4-diones as Protoporphyrinogen Oxidase Inhibitors.

    PubMed

    Zuo, Yang; Wu, Qiongyou; Su, Sun-Wen; Niu, Cong-Wei; Xi, Zhen; Yang, Guang-Fu

    2016-01-27

    Protoporphyrinogen oxidase (PPO, E.C. 1.3.3.4) is known as a key action target for several structurally diverse herbicides. As a continuation of our research work on the development of new PPO-inhibiting herbicides, a series of novel 3-(2'-halo-5'-substituted-benzothiazol-1'-yl)-1-methyl-6-(trifluoromethyl)pyrimidine-2,4-diones 9 were designed and synthesized. The bioassay results indicated that a number of the newly synthesized compounds exhibited higher inhibition activity against tobacco PPO (mtPPO) than the controls, saflufenacil and sulfentrazone. Compound 9F-5 was identified as the most potent inhibitor with a Ki value of 0.0072 μM against mtPPO, showing about 4.2-fold and 1.4-fold higher potency than sulfentrazone (Ki = 0.03 μM) and saflufenacil (Ki = 0.01 μM), respectively. An additional green house assay demonstrated that compound 9F-6 (Ki = 0.012 μM) displayed the most promising postemergence herbicidal activity with a broad spectrum even at a concentration as low as 37.5 g of active ingredient (ai)/ha. Maize exhibits relative tolerance against compound 9F-6 at the dosage of 150 g ai/ha, but it is susceptible to saflufenacil even at 75 g ai/ha. Thus, compound 9F-6 exhibits the potential to be a new herbicide for weed control in maize fields. PMID:26728549

  4. Quasi-QSAR for mutagenic potential of multi-walled carbon-nanotubes.

    PubMed

    Toropov, Andrey A; Toropova, Alla P

    2015-04-01

    Available on the Internet, the CORAL software (http://www.insilico.eu/coral) has been used to build up quasi-quantitative structure-activity relationships (quasi-QSAR) for prediction of mutagenic potential of multi-walled carbon-nanotubes (MWCNTs). In contrast with the previous models built up by CORAL which were based on representation of the molecular structure by simplified molecular input-line entry system (SMILES) the quasi-QSARs based on the representation of conditions (not on the molecular structure) such as concentration, presence (absence) S9 mix, the using (or without the using) of preincubation were encoded by so-called quasi-SMILES. The statistical characteristics of these models (quasi-QSARs) for three random splits into the visible training set and test set and invisible validation set are the following: (i) split 1: n=13, r(2)=0.8037, q(2)=0.7260, s=0.033, F=45 (training set); n=5, r(2)=0.9102, s=0.071 (test set); n=6, r(2)=0.7627, s=0.044 (validation set); (ii) split 2: n=13, r(2)=0.6446, q(2)=0.4733, s=0.045, F=20 (training set); n=5, r(2)=0.6785, s=0.054 (test set); n=6, r(2)=0.9593, s=0.032 (validation set); and (iii) n=14, r(2)=0.8087, q(2)=0.6975, s=0.026, F=51 (training set); n=5, r(2)=0.9453, s=0.074 (test set); n=5, r(2)=0.8951, s=0.052 (validation set). PMID:25465947

  5. Development and validation of hydrophobic molecular fields derived from the quantum mechanical IEF/PCM-MST solvation models in 3D-QSAR.

    PubMed

    Ginex, Tiziana; Muñoz-Muriedas, Jordi; Herrero, Enric; Gibert, Enric; Cozzini, Pietro; Luque, F J

    2016-05-15

    Since the development of structure-activity relationships about 50 years ago, 3D-QSAR methods belong to the most refined ligand-based in silico techniques for prediction of biological data using physicochemical molecular fields. In this scenario, this study reports the development and validation of quantum mechanical (QM)-based hydrophobic descriptors derived from the parametrized MST continuum solvation model to be used in 3D-QSAR studies within the framework of the Hydrophobic Pharmacophore (HyPhar) method. To this end, five sets of compounds reported in the literature (dopamine D2/D4 antagonists, antifungal 2-aryl-4-chromanones, and inhibitors of GSK-3, cruzain and thermolysin) have been revisited. The results derived from the QM/MST-based hydrophobic descriptors have been compared with previous CoMFA and CoMSIA studies, and examined in light of the available X-ray crystallographic structures of the targets. The analysis reveals that the combination of electrostatic and nonelectrostatic components of the octanol/water partition coefficient yields pharmacophoric models fully comparable with the predictive potential of standard 3D-QSAR techniques. Moreover, the graphical representation of the hydrophobic maps provides a direct linkage with the pattern of interactions found in crystallographic structures. Overall, the introduction of the QM/MST-based descriptors, which could be easily adapted to other continuum solvation formalisms, paves the way to novel computational strategies for disclosing structure-activity relationships in drug design. © 2016 Wiley Periodicals, Inc. PMID:26813046

  6. Computational Study of Quinolone Derivatives to Improve their Therapeutic Index as Anti-malaria Agents: QSAR and QSTR.

    PubMed

    Iman, Maryam; Davood, Asghar; Khamesipour, Ali

    2015-01-01

    Malaria is a parasitic disease caused by five different species of Plasmodium. More than 40% of the world's population is at risk and malaria annual incidence is estimated to be more than two hundred million, malaria is one of the most important public health problems especially in children of the poorest parts of the world, annual mortality is about 1 million. The epidemiological status of the disease justifies to search for control measures, new therapeutic options and development of an effective vaccine. Chemotherapy options in malaria are limited, moreover, drug resistant rate is high. In spite of global efforts to develop an effective vaccine yet there is no vaccine available. In the current study, a series of quinolone derivatives were subjected to quantitative structure activity relationship (QSAR) and quantitative structure toxicity relationship (QSTR) analyses to identify the ideal physicochemical characteristics of potential anti-malaria activity and less cytotoxicity. Quinolone with desirable properties was built using HyperChem program, and conformational studies were performed through the semi-empirical method followed by the PM3 force field. Multi linear regression (MLR) was used as a chemo metric tool for quantitative structure activity relationship modeling and the developed models were shown to be statistically significant according to the validation parameters. The obtained QSAR model reveals that the descriptors PJI2, Mv, PCR, nBM, and VAR mainly affect the anti-malaria activity and descriptors MSD, MAXDP, and X1sol affect the cytotoxicity of the series of ligands. PMID:26330866

  7. Computational Study of Quinolone Derivatives to Improve their Therapeutic Index as Anti-malaria Agents: QSAR and QSTR

    PubMed Central

    Iman, Maryam; Davood, Asghar; Khamesipour, Ali

    2015-01-01

    Malaria is a parasitic disease caused by five different species of Plasmodium. More than 40% of the world’s population is at risk and malaria annual incidence is estimated to be more than two hundred million, malaria is one of the most important public health problems especially in children of the poorest parts of the world, annual mortality is about 1 million. The epidemiological status of the disease justifies to search for control measures, new therapeutic options and development of an effective vaccine. Chemotherapy options in malaria are limited, moreover, drug resistant rate is high. In spite of global efforts to develop an effective vaccine yet there is no vaccine available. In the current study, a series of quinolone derivatives were subjected to quantitative structure activity relationship (QSAR) and quantitative structure toxicity relationship (QSTR) analyses to identify the ideal physicochemical characteristics of potential anti-malaria activity and less cytotoxicity. Quinolone with desirable properties was built using HyperChem program, and conformational studies were performed through the semi-empirical method followed by the PM3 force field. Multi linear regression (MLR) was used as a chemo metric tool for quantitative structure activity relationship modeling and the developed models were shown to be statistically significant according to the validation parameters. The obtained QSAR model reveals that the descriptors PJI2, Mv, PCR, nBM, and VAR mainly affect the anti-malaria activity and descriptors MSD, MAXDP, and X1sol affect the cytotoxicity of the series of ligands. PMID:26330866

  8. Volume learning algorithm artificial neural networks for 3D QSAR studies.

    PubMed

    Tetko, I V; Kovalishyn, V V; Livingstone, D J

    2001-07-19

    The current study introduces a new method, the volume learning algorithm (VLA), for the investigation of three-dimensional quantitative structure-activity relationships (QSAR) of chemical compounds. This method incorporates the advantages of comparative molecular field analysis (CoMFA) and artificial neural network approaches. VLA is a combination of supervised and unsupervised neural networks applied to solve the same problem. The supervised algorithm is a feed-forward neural network trained with a back-propagation algorithm while the unsupervised network is a self-organizing map of Kohonen. The use of both of these algorithms makes it possible to cluster the input CoMFA field variables and to use only a small number of the most relevant parameters to correlate spatial properties of the molecules with their activity. The statistical coefficients calculated by the proposed algorithm for cannabimimetic aminoalkyl indoles were comparable to, or improved, in comparison to the original study using the partial least squares algorithm. The results of the algorithm can be visualized and easily interpreted. Thus, VLA is a new convenient tool for three-dimensional QSAR studies. PMID:11448223

  9. CheS-Mapper 2.0 for visual validation of (Q)SAR models

    PubMed Central

    2014-01-01

    Background Sound statistical validation is important to evaluate and compare the overall performance of (Q)SAR models. However, classical validation does not support the user in better understanding the properties of the model or the underlying data. Even though, a number of visualization tools for analyzing (Q)SAR information in small molecule datasets exist, integrated visualization methods that allow the investigation of model validation results are still lacking. Results We propose visual validation, as an approach for the graphical inspection of (Q)SAR model validation results. The approach applies the 3D viewer CheS-Mapper, an open-source application for the exploration of small molecules in virtual 3D space. The present work describes the new functionalities in CheS-Mapper 2.0, that facilitate the analysis of (Q)SAR information and allows the visual validation of (Q)SAR models. The tool enables the comparison of model predictions to the actual activity in feature space. The approach is generic: It is model-independent and can handle physico-chemical and structural input features as well as quantitative and qualitative endpoints. Conclusions Visual validation with CheS-Mapper enables analyzing (Q)SAR information in the data and indicates how this information is employed by the (Q)SAR model. It reveals, if the endpoint is modeled too specific or too generic and highlights common properties of misclassified compounds. Moreover, the researcher can use CheS-Mapper to inspect how the (Q)SAR model predicts activity cliffs. The CheS-Mapper software is freely available at http://ches-mapper.org. Graphical abstract Comparing actual and predicted activity values with CheS-Mapper.

  10. Biological evaluation and 3D-QSAR studies of curcumin analogues as aldehyde dehydrogenase 1 inhibitors.

    PubMed

    Wang, Hui; Du, Zhiyun; Zhang, Changyuan; Tang, Zhikai; He, Yan; Zhang, Qiuyan; Zhao, Jun; Zheng, Xi

    2014-01-01

    Aldehyde dehydrogenase 1 (ALDH1) is reported as a biomarker for identifying some cancer stem cells, and down-regulation or inhibition of the enzyme can be effective in anti-drug resistance and a potent therapeutic for some tumours. In this paper, the inhibitory activity, mechanism mode, molecular docking and 3D-QSAR (three-dimensional quantitative structure activity relationship) of curcumin analogues (CAs) against ALDH1 were studied. Results demonstrated that curcumin and CAs possessed potent inhibitory activity against ALDH1, and the CAs compound with ortho di-hydroxyl groups showed the most potent inhibitory activity. This study indicates that CAs may represent a new class of ALDH1 inhibitor. PMID:24840575

  11. Assessment of quantitative structure-activity relationship of toxicity prediction models for Korean chemical substance control legislation

    PubMed Central

    Kim, Kwang-Yon; Shin, Seong Eun; No, Kyoung Tai

    2015-01-01

    Objectives For successful adoption of legislation controlling registration and assessment of chemical substances, it is important to obtain sufficient toxicological experimental evidence and other related information. It is also essential to obtain a sufficient number of predicted risk and toxicity results. Particularly, methods used in predicting toxicities of chemical substances during acquisition of required data, ultimately become an economic method for future dealings with new substances. Although the need for such methods is gradually increasing, the-required information about reliability and applicability range has not been systematically provided. Methods There are various representative environmental and human toxicity models based on quantitative structure-activity relationships (QSAR). Here, we secured the 10 representative QSAR-based prediction models and its information that can make predictions about substances that are expected to be regulated. We used models that predict and confirm usability of the information expected to be collected and submitted according to the legislation. After collecting and evaluating each predictive model and relevant data, we prepared methods quantifying the scientific validity and reliability, which are essential conditions for using predictive models. Results We calculated predicted values for the models. Furthermore, we deduced and compared adequacies of the models using the Alternative non-testing method assessed for Registration, Evaluation, Authorization, and Restriction of Chemicals Substances scoring system, and deduced the applicability domains for each model. Additionally, we calculated and compared inclusion rates of substances expected to be regulated, to confirm the applicability. Conclusions We evaluated and compared the data, adequacy, and applicability of our selected QSAR-based toxicity prediction models, and included them in a database. Based on this data, we aimed to construct a system that can be used

  12. Structural interpretation of activity cliffs revealed by systematic analysis of structure-activity relationships in analog series.

    PubMed

    Sisay, Mihiret T; Peltason, Lisa; Bajorath, Jürgen

    2009-10-01

    Discontinuity in structure-activity relationships (SARs) is caused by so-called activity cliffs and represents one of the major caveats in SAR modeling and lead optimization. At activity cliffs, small structural modifications of compounds lead to substantial differences in potency that are essentially unpredictable using quantitative structure-activity relationship (QSAR) methods. In order to better understand SAR discontinuity at the molecular level of detail, we have analyzed different compound series in combinatorial analog graphs and determined substitution patterns that introduce activity cliffs of varying magnitude. So identified SAR determinants were then analyzed on the basis of complex crystal structures to enable a structural interpretation of SAR discontinuity and underlying activity cliffs. In some instances, SAR discontinuity detected within analog series could be well rationalized on the basis of structural data, whereas in others a structural explanation was not possible. This reflects the intrinsic complexity of small molecule SARs and suggests that the analysis of short-range receptor-ligand interactions seen in X-ray structures is insufficient to comprehensively account for SAR discontinuity. However, in other cases, SAR information extracted from ligands was incomplete but could be deduced taking X-ray data into account. Thus, taken together, these findings illustrate the complementarity of ligand-based SAR analysis and structural information. PMID:19761254

  13. Amino substituted benzimidazo[1,2-a]quinolines: Antiproliferative potency, 3D QSAR study and DNA binding properties.

    PubMed

    Perin, Nataša; Nhili, Raja; Cindrić, Maja; Bertoša, Branimir; Vušak, Darko; Martin-Kleiner, Irena; Laine, William; Karminski-Zamola, Grace; Kralj, Marijeta; David-Cordonnier, Marie-Hélène; Hranjec, Marijana

    2016-10-21

    We describe the synthesis, 3D-derived quantitative structure-activity relationship (QSAR), antiproliferative activity and DNA binding properties of a series of 2-amino, 5-amino and 2,5-diamino substituted benzimidazo[1,2-a]quinolines prepared by environmentally friendly uncatalyzed microwave assisted amination. The antiproliferative activities were assessed in vitro against colon, lung and breast carcinoma cell lines; activities ranged from submicromolar to micromolar. The strongest antiproliferative activity was demonstrated by 2-amino-substituted analogues, whereas 5-amino and or 2,5-diamino substituted derivatives resulted in much less activity. Derivatives bearing 4-methyl- or 3,5-dimethyl-1-piperazinyl substituents emerged as the most active. DNA binding properties and the mode of interaction of chosen substituted benzimidazo[1,2-a]quinolines prepared herein were studied using melting temperature studies, a series of spectroscopic studies (UV/Visible, fluorescence, and circular dichroism), and biochemical experiments (topoisomerase I-mediated DNA relaxation and DNase I footprinting experiments). Both compound 36 and its bis-quaternary iodide salt 37 intercalate between adjacent base pairs of the DNA helix while compound 33 presented a very weak topoisomerase I poisoning activity. A 3D-QSAR analysis was performed to identify hydrogen bonding properties, hydrophobicity, molecular flexibility and distribution of hydrophobic regions as these molecular properties had the highest impact on the antiproliferative activity against the three cell lines. PMID:27448912

  14. Deciphering the Structural Requirements of Nucleoside Bisubstrate Analogues for Inhibition of MbtA in Mycobacterium tuberculosis: A FB-QSAR Study and Combinatorial Library Generation for Identifying Potential Hits.

    PubMed

    Maganti, Lakshmi; Das, Sanjit Kumar; Mascarenhas, Nahren Manuel; Ghoshal, Nanda

    2011-10-01

    The re-emergence of tuberculosis infections, which are resistant to conventional drug therapy, has steadily risen in the last decade. Inhibitors of aryl acid adenylating enzyme known as MbtA, involved in siderophore biosynthesis in Mycobacterium tuberculosis, are being explored as potential antitubercular agents. The ability to identify fragments that interact with a biological target is a key step in fragment based drug design (FBDD). To expand the boundaries of quantitative structure activity relationship (QSAR) paradigm, we have proposed a Fragment Based QSAR methodology, referred here in as FB-QSAR, for deciphering the structural requirements of a series of nucleoside bisubstrate analogs for inhibition of MbtA, a key enzyme involved in siderophore biosynthetic pathway. For the development of FB-QSAR models, statistical techniques such as stepwise multiple linear regression (SMLR), genetic function approximation (GFA) and GFAspline were used. The predictive ability of the generated models was validated using different statistical metrics, and similarity-based coverage estimation was carried out to define applicability boundaries. To aid the creation of novel antituberculosis compounds, a bioisosteric database was enumerated using the combichem approach endorsed mining in a lead-like chemical space. The generated library was screened using an integrated in-silico approach and potential hits identified. PMID:27468106

  15. Synthetic cannabinoids: In silico prediction of the cannabinoid receptor 1 affinity by a quantitative structure-activity relationship model.

    PubMed

    Paulke, Alexander; Proschak, Ewgenij; Sommer, Kai; Achenbach, Janosch; Wunder, Cora; Toennes, Stefan W

    2016-03-14

    The number of new synthetic psychoactive compounds increase steadily. Among the group of these psychoactive compounds, the synthetic cannabinoids (SCBs) are most popular and serve as a substitute of herbal cannabis. More than 600 of these substances already exist. For some SCBs the in vitro cannabinoid receptor 1 (CB1) affinity is known, but for the majority it is unknown. A quantitative structure-activity relationship (QSAR) model was developed, which allows the determination of the SCBs affinity to CB1 (expressed as binding constant (Ki)) without reference substances. The chemically advance template search descriptor was used for vector representation of the compound structures. The similarity between two molecules was calculated using the Feature-Pair Distribution Similarity. The Ki values were calculated using the Inverse Distance Weighting method. The prediction model was validated using a cross validation procedure. The predicted Ki values of some new SCBs were in a range between 20 (considerably higher affinity to CB1 than THC) to 468 (considerably lower affinity to CB1 than THC). The present QSAR model can serve as a simple, fast and cheap tool to get a first hint of the biological activity of new synthetic cannabinoids or of other new psychoactive compounds. PMID:26795018

  16. Toward a class-independent quantitative structure--activity relationship model for uncouplers of oxidative phosphorylation.

    PubMed

    Spycher, Simon; Smejtek, Pavel; Netzeva, Tatiana I; Escher, Beate I

    2008-04-01

    A mechanistically based quantitative structure-activity relationship (QSAR) for the uncoupling activity of weak organic acids has been derived. The analysis of earlier experimental studies suggested that the limiting step in the uncoupling process is the rate with which anions can cross the membrane and that this rate is determined by the height of the energy barrier encountered in the hydrophobic membrane core. We use this mechanistic understanding to develop a predictive model for uncoupling. The translocation rate constants of anions correlate well with the free energy difference between the energy well and the energy barrier, Delta G well-barrier,A (-) , in the membrane calculated by a novel approach to describe internal partitioning in the membrane. An existing data set of 21 phenols measured in an in vitro test system specific for uncouplers was extended by 14 highly diverse compounds. A simple regression model based on the experimental membrane-water partition coefficient and Delta G well-barrier,A (-) showed good predictive power and had meaningful regression coefficients. To establish uncoupler QSARs independent of chemical class, it is necessary to calculate the descriptors for the charged species, as the analogous descriptors of the neutral species showed almost no correlation with the translocation rate constants of anions. The substitution of experimental with calculated partition coefficients resulted in a decrease of the model fit. A particular strength of the current model is the accurate calculation of excess toxicity, which makes it a suitable tool for database screening. The applicability domain, limitations of the model, and ideas for future research are critically discussed. PMID:18358007

  17. Therapeutic index modeling and predictive QSAR of novel thiazolidin-4-one analogs against Toxoplasma gondii.

    PubMed

    Asadollahi-Baboli, M; Mani-Varnosfaderani, A

    2015-04-01

    The main idea of this study was to find predictive quantitative structure-activity relationships (QSAR) for the therapeutic index of 68 thiazolidin-4-one analogs against Toxoplasma gondii. Multivariate adaptive regression spline (MARS) together with Monte-Carlo (MC) sampling was proposed as a reliable descriptor subset selection strategy. Basis functions and knot points are also determined for each selected descriptor using generalized cross validation after frequency analysis. Least squares-support vector regression (LS-SVR) with optimized hyper-parameters was employed as mapping tool due to its promising empirical performance. The models were validated and tested through the use of the external prediction set of compounds, leave-one-out and leave-many-out cross validation methods, applicability domain analysis and Y-randomization. The robustness and accuracy of the QSAR models were confirmed by the satisfactory statistical parameters for the experimentally reported dataset (R(2)p=0.853, Q(2)LOO=0.785, R(2)L20%O=0.742 and r(2)m=0.715) and low standard error values (RMSEp=0.208, RMSELOO=0.321 and RMSEL20%O=0.376). The comprehensive analysis carried out in the present contribution using the proposed strategy can provide a considerable basis for the design and development of novel drug-like molecules against T.gondii. PMID:25661424

  18. Approaches to developing alternative and predictive toxicology based on PBPK/PD and QSAR modeling.

    PubMed Central

    Yang, R S; Thomas, R S; Gustafson, D L; Campain, J; Benjamin, S A; Verhaar, H J; Mumtaz, M M

    1998-01-01

    Systematic toxicity testing, using conventional toxicology methodologies, of single chemicals and chemical mixtures is highly impractical because of the immense numbers of chemicals and chemical mixtures involved and the limited scientific resources. Therefore, the development of unconventional, efficient, and predictive toxicology methods is imperative. Using carcinogenicity as an end point, we present approaches for developing predictive tools for toxicologic evaluation of chemicals and chemical mixtures relevant to environmental contamination. Central to the approaches presented is the integration of physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) and quantitative structure--activity relationship (QSAR) modeling with focused mechanistically based experimental toxicology. In this development, molecular and cellular biomarkers critical to the carcinogenesis process are evaluated quantitatively between different chemicals and/or chemical mixtures. Examples presented include the integration of PBPK/PD and QSAR modeling with a time-course medium-term liver foci assay, molecular biology and cell proliferation studies. Fourier transform infrared spectroscopic analyses of DNA changes, and cancer modeling to assess and attempt to predict the carcinogenicity of the series of 12 chlorobenzene isomers. Also presented is an ongoing effort to develop and apply a similar approach to chemical mixtures using in vitro cell culture (Syrian hamster embryo cell transformation assay and human keratinocytes) methodologies and in vivo studies. The promise and pitfalls of these developments are elaborated. When successfully applied, these approaches may greatly reduce animal usage, personnel, resources, and time required to evaluate the carcinogenicity of chemicals and chemical mixtures. Images Figure 6 PMID:9860897

  19. Evaluation of a statistics-based Ames mutagenicity QSAR model and interpretation of the results obtained.

    PubMed

    Barber, Chris; Cayley, Alex; Hanser, Thierry; Harding, Alex; Heghes, Crina; Vessey, Jonathan D; Werner, Stephane; Weiner, Sandy K; Wichard, Joerg; Giddings, Amanda; Glowienke, Susanne; Parenty, Alexis; Brigo, Alessandro; Spirkl, Hans-Peter; Amberg, Alexander; Kemper, Ray; Greene, Nigel

    2016-04-01

    The relative wealth of bacterial mutagenicity data available in the public literature means that in silico quantitative/qualitative structure activity relationship (QSAR) systems can readily be built for this endpoint. A good means of evaluating the performance of such systems is to use private unpublished data sets, which generally represent a more distinct chemical space than publicly available test sets and, as a result, provide a greater challenge to the model. However, raw performance metrics should not be the only factor considered when judging this type of software since expert interpretation of the results obtained may allow for further improvements in predictivity. Enough information should be provided by a QSAR to allow the user to make general, scientifically-based arguments in order to assess and overrule predictions when necessary. With all this in mind, we sought to validate the performance of the statistics-based in vitro bacterial mutagenicity prediction system Sarah Nexus (version 1.1) against private test data sets supplied by nine different pharmaceutical companies. The results of these evaluations were then analysed in order to identify findings presented by the model which would be useful for the user to take into consideration when interpreting the results and making their final decision about the mutagenic potential of a given compound. PMID:26708083

  20. Prediction of rodent carcinogenic potential of naturally occurring chemicals in the human diet using high-throughput QSAR predictive modeling

    SciTech Connect

    Valerio, Luis G. . E-mail: luis.valerio@FDA.HHS.gov; Arvidson, Kirk B.; Chanderbhan, Ronald F.; Contrera, Joseph F.

    2007-07-01

    Consistent with the U.S. Food and Drug Administration (FDA) Critical Path Initiative, predictive toxicology software programs employing quantitative structure-activity relationship (QSAR) models are currently under evaluation for regulatory risk assessment and scientific decision support for highly sensitive endpoints such as carcinogenicity, mutagenicity and reproductive toxicity. At the FDA's Center for Food Safety and Applied Nutrition's Office of Food Additive Safety and the Center for Drug Evaluation and Research's Informatics and Computational Safety Analysis Staff (ICSAS), the use of computational SAR tools for both qualitative and quantitative risk assessment applications are being developed and evaluated. One tool of current interest is MDL-QSAR predictive discriminant analysis modeling of rodent carcinogenicity, which has been previously evaluated for pharmaceutical applications by the FDA ICSAS. The study described in this paper aims to evaluate the utility of this software to estimate the carcinogenic potential of small, organic, naturally occurring chemicals found in the human diet. In addition, a group of 19 known synthetic dietary constituents that were positive in rodent carcinogenicity studies served as a control group. In the test group of naturally occurring chemicals, 101 were found to be suitable for predictive modeling using this software's discriminant analysis modeling approach. Predictions performed on these compounds were compared to published experimental evidence of each compound's carcinogenic potential. Experimental evidence included relevant toxicological studies such as rodent cancer bioassays, rodent anti-carcinogenicity studies, genotoxic studies, and the presence of chemical structural alerts. Statistical indices of predictive performance were calculated to assess the utility of the predictive modeling method. Results revealed good predictive performance using this software's rodent carcinogenicity module of over 1200 chemicals

  1. Quantitative structure-activity relationship models of clinical pharmacokinetics: clearance and volume of distribution.

    PubMed

    Gombar, Vijay K; Hall, Stephen D

    2013-04-22

    Reliable prediction of two fundamental human pharmacokinetic (PK) parameters, systemic clearance (CL) and apparent volume of distribution (Vd), determine the size and frequency of drug dosing and are at the heart of drug discovery and development. Traditionally, estimated CL and Vd are derived from preclinical in vitro and in vivo absorption, distribution, metabolism, and excretion (ADME) measurements. In this paper, we report quantitative structure-activity relationship (QSAR) models for prediction of systemic CL and steady-state Vd (Vdss) from intravenous (iv) dosing in humans. These QSAR models avoid uncertainty associated with preclinical-to-clinical extrapolation and require two-dimensional structure drawing as the sole input. The clean, uniform training sets for these models were derived from the compilation published by Obach et al. (Drug Metab. Disp. 2008, 36, 1385-1405). Models for CL and Vdss were developed using both a support vector regression (SVR) method and a multiple linear regression (MLR) method. The SVR models employ a minimum of 2048-bit fingerprints developed in-house as structure quantifiers. The MLR models, on the other hand, are based on information-rich electro-topological states of two-atom fragments as descriptors and afford reverse QSAR (RQSAR) analysis to help model-guided, in silico modulation of structures for desired CL and Vdss. The capability of the models to predict iv CL and Vdss with acceptable accuracy was established by randomly splitting data into training and test sets. On average, for both CL and Vdss, 75% of test compounds were predicted within 2.5-fold of the value observed and 90% of test compounds were within 5.0-fold of the value observed. The performance of the final models developed from 525 compounds for CL and 569 compounds for Vdss was evaluated on an external set of 56 compounds. The predictions were either better or comparable to those predicted by other in silico models reported in the literature. To

  2. Structure-activity relationship for Fe(III)-salen-like complexes as potent anticancer agents.

    PubMed

    Ghanbari, Zahra; Housaindokht, Mohammad R; Izadyar, Mohammad; Bozorgmehr, Mohammad R; Eshtiagh-Hosseini, Hossein; Bahrami, Ahmad R; Matin, Maryam M; Khoshkholgh, Maliheh Javan

    2014-01-01

    Quantitative structure activity relationship (QSAR) for the anticancer activity of Fe(III)-salen and salen-like complexes was studied. The methods of density function theory (B3LYP/LANL2DZ) were used to optimize the structures. A pool of descriptors was calculated: 1497 theoretical descriptors and quantum-chemical parameters, shielding NMR, and electronic descriptors. The study of structure and activity relationship was performed with multiple linear regression (MLR) and artificial neural network (ANN). In nonlinear method, the adaptive neuro-fuzzy inference system (ANFIS) was applied in order to choose the most effective descriptors. The ANN-ANFIS model with high statistical significance (R (2) train = 0.99, RMSE = 0.138, and Q (2) LOO = 0.82) has better capability to predict the anticancer activity of the new compounds series of this family. Based on this study, anticancer activity of this compound is mainly dependent on the geometrical parameters, position, and the nature of the substituent of salen ligand. PMID:24955417

  3. Structure-Activity Relationship for Fe(III)-Salen-Like Complexes as Potent Anticancer Agents

    PubMed Central

    Ghanbari, Zahra; Housaindokht, Mohammad R.; Izadyar, Mohammad; Bozorgmehr, Mohammad R.; Eshtiagh-Hosseini, Hossein; Bahrami, Ahmad R.; Matin, Maryam M.; Khoshkholgh, Maliheh Javan

    2014-01-01

    Quantitative structure activity relationship (QSAR) for the anticancer activity of Fe(III)-salen and salen-like complexes was studied. The methods of density function theory (B3LYP/LANL2DZ) were used to optimize the structures. A pool of descriptors was calculated: 1497 theoretical descriptors and quantum-chemical parameters, shielding NMR, and electronic descriptors. The study of structure and activity relationship was performed with multiple linear regression (MLR) and artificial neural network (ANN). In nonlinear method, the adaptive neuro-fuzzy inference system (ANFIS) was applied in order to choose the most effective descriptors. The ANN-ANFIS model with high statistical significance (R2train = 0.99, RMSE = 0.138, and Q2LOO = 0.82) has better capability to predict the anticancer activity of the new compounds series of this family. Based on this study, anticancer activity of this compound is mainly dependent on the geometrical parameters, position, and the nature of the substituent of salen ligand. PMID:24955417

  4. Quantitative structure-activity relationships for organophosphates binding to trypsin and chymotrypsin.

    PubMed

    Ruark, Christopher D; Hack, C Eric; Robinson, Peter J; Gearhart, Jeffery M

    2011-01-01

    Organophosphate (OP) nerve agents such as sarin, soman, tabun, and O-ethyl S-[2-(diisopropylamino) ethyl] methylphosphonothioate (VX) do not react solely with acetylcholinesterase (AChE). Evidence suggests that cholinergic-independent pathways over a wide range are also targeted, including serine proteases. These proteases comprise nearly one-third of all known proteases and play major roles in synaptic plasticity, learning, memory, neuroprotection, wound healing, cell signaling, inflammation, blood coagulation, and protein processing. Inhibition of these proteases by OP was found to exert a wide range of noncholinergic effects depending on the type of OP, the dose, and the duration of exposure. Consequently, in order to understand these differences, in silico biologically based dose-response and quantitative structure-activity relationship (QSAR) methodologies need to be integrated. Here, QSAR were used to predict OP bimolecular rate constants for trypsin and α-chymotrypsin. A heuristic regression of over 500 topological/constitutional, geometric, thermodynamic, electrostatic, and quantum mechanical descriptors, using the software Ampac 8.0 and Codessa 2.51 (SemiChem, Inc., Shawnee, KS), was developed to obtain statistically verified equations for the models. General models, using all data subsets, resulted in R(2) values of .94 and .92 and leave-one-out Q(2) values of 0.9 and 0.87 for trypsin and α-chymotrypsin. To validate the general model, training sets were split into independent subsets for test set evaluation. A y-randomization procedure, used to estimate chance correlation, was performed 10,000 times, resulting in mean R(2) values of .24 and .3 for trypsin and α-chymotrypsin. The results show that these models are highly predictive and capable of delineating the complex mechanism of action between OP and serine proteases, and ultimately, by applying this approach to other OP enzyme reactions such as AChE, facilitate the development of biologically based

  5. Quantitative structure activity relationship and risk analysis of some pesticides in the goat milk.

    PubMed

    Muhammad, Faqir; Awais, Mian Muhammad; Akhtar, Masood; Anwar, Muhammad Irfan

    2013-01-01

    The detection and quantification of different pesticides in the goat milk samples collected from different localities of Faisalabad, Pakistan was performed by HPLC using solid phase microextraction. The analysis showed that about 50% milk samples were contaminated with pesticides. The mean±SEM levels (ppm) of cyhalothrin, endosulfan, chlorpyrifos and cypermethrin were 0.34±0.007, 0.063±0.002, 0.034±0.002 and 0.092±0.002, respectively; whereas, methyl parathion was not detected in any of the analyzed samples. Quantitative structure activity relationship (QSAR) models were suggested to predict the residues of unknown pesticides in the goat milk using their known physicochemical characteristics including molecular weight (MW), melting point (MP), and log octanol to water partition coefficient (Ko/w) in relation to the characteristics such as pH, % fat, specific gravity and refractive index of goat milk. The analysis revealed good correlation coefficient (R2 = 0.985) for goat QSAR model. The coefficients for Ko/w and refractive index for the studied pesticides were higher in goat milk. This suggests that these are better determinants for pesticide residue prediction in the milk of these animals. Based upon the determined pesticide residues and their provisional tolerable daily intakes, risk analysis was also conducted which showed that daily intake levels of pesticide residues including cyhalothrin, chlorpyrifos and cypermethrin in present study are 2.68, 5.19 and 2.71 times higher, respectively in the goat milk. This intake of pesticide contaminated milk might pose health hazards to humans in this locality. PMID:23369514

  6. Quantitative structure activity relationship and risk analysis of some pesticides in the goat milk

    PubMed Central

    2013-01-01

    The detection and quantification of different pesticides in the goat milk samples collected from different localities of Faisalabad, Pakistan was performed by HPLC using solid phase microextraction. The analysis showed that about 50% milk samples were contaminated with pesticides. The mean±SEM levels (ppm) of cyhalothrin, endosulfan, chlorpyrifos and cypermethrin were 0.34±0.007, 0.063±0.002, 0.034±0.002 and 0.092±0.002, respectively; whereas, methyl parathion was not detected in any of the analyzed samples. Quantitative structure activity relationship (QSAR) models were suggested to predict the residues of unknown pesticides in the goat milk using their known physicochemical characteristics including molecular weight (MW), melting point (MP), and log octanol to water partition coefficient (Ko/w) in relation to the characteristics such as pH, % fat, specific gravity and refractive index of goat milk. The analysis revealed good correlation coefficient (R2 = 0.985) for goat QSAR model. The coefficients for Ko/w and refractive index for the studied pesticides were higher in goat milk. This suggests that these are better determinants for pesticide residue prediction in the milk of these animals. Based upon the determined pesticide residues and their provisional tolerable daily intakes, risk analysis was also conducted which showed that daily intake levels of pesticide residues including cyhalothrin, chlorpyrifos and cypermethrin in present study are 2.68, 5.19 and 2.71 times higher, respectively in the goat milk. This intake of pesticide contaminated milk might pose health hazards to humans in this locality. PMID:23369514

  7. Estimating the Potential Toxicity of Chemicals Associated with Hydraulic Fracturing Operations Using Quantitative Structure-Activity Relationship Modeling.

    PubMed

    Yost, Erin E; Stanek, John; DeWoskin, Robert S; Burgoon, Lyle D

    2016-07-19

    The United States Environmental Protection Agency (EPA) identified 1173 chemicals associated with hydraulic fracturing fluids, flowback, or produced water, of which 1026 (87%) lack chronic oral toxicity values for human health assessments. To facilitate the ranking and prioritization of chemicals that lack toxicity values, it may be useful to employ toxicity estimates from quantitative structure-activity relationship (QSAR) models. Here we describe an approach for applying the results of a QSAR model from the TOPKAT program suite, which provides estimates of the rat chronic oral lowest-observed-adverse-effect level (LOAEL). Of the 1173 chemicals, TOPKAT was able to generate LOAEL estimates for 515 (44%). To address the uncertainty associated with these estimates, we assigned qualitative confidence scores (high, medium, or low) to each TOPKAT LOAEL estimate, and found 481 to be high-confidence. For 48 chemicals that had both a high-confidence TOPKAT LOAEL estimate and a chronic oral reference dose from EPA's Integrated Risk Information System (IRIS) database, Spearman rank correlation identified 68% agreement between the two values (permutation p-value =1 × 10(-11)). These results provide support for the use of TOPKAT LOAEL estimates in identifying and prioritizing potentially hazardous chemicals. High-confidence TOPKAT LOAEL estimates were available for 389 of 1026 hydraulic fracturing-related chemicals that lack chronic oral RfVs and OSFs from EPA-identified sources, including a subset of chemicals that are frequently used in hydraulic fracturing fluids. PMID:27172125

  8. Quantitative Structure-Activity Relationships Study on the Rate Constants of Polychlorinated Dibenzo-p-Dioxins with OH Radical

    PubMed Central

    Qi, Chuansong; Zhang, Chenxi; Sun, Xiaomin

    2015-01-01

    The OH-initiated reaction rate constants (kOH) are of great importance to measure atmospheric behaviors of polychlorinated dibenzo-p-dioxins (PCDDs) in the environment. The rate constants of 75 PCDDs with the OH radical at 298.15 K have been calculated using high level molecular orbital theory, and the rate constants (kα, kβ, kγ and kOH) were further analyzed by the quantitative structure-activity relationships (QSAR) study. According to the QSAR models, the relations between rate constants and the numbers and positions of Cl atoms, the energy of the highest occupied molecular orbital (EHOMO), the energy of the lowest unoccupied molecular orbital (ELUMO), the difference ΔEHOMO-LUMO between EHOMO and ELUMO, and the dipole of oxidizing agents (D) were discussed. It was found that EHOMO is the main factor in the kOH. The number of Cl atoms is more effective than the number of relative position of these Cl atoms in the kOH. The kOH decreases with the increase of the substitute number of Cl atoms. PMID:26274950

  9. HP-Lattice QSAR for dynein proteins: experimental proteomics (2D-electrophoresis, mass spectrometry) and theoretic study of a Leishmania infantum sequence.

    PubMed

    Dea-Ayuela, María Auxiliadora; Pérez-Castillo, Yunierkis; Meneses-Marcel, Alfredo; Ubeira, Florencio M; Bolas-Fernández, Francisco; Chou, Kuo-Chen; González-Díaz, Humberto

    2008-08-15

    The toxicity and inefficacy of actual organic drugs against Leishmaniosis justify research projects to find new molecular targets in Leishmania species including Leishmania infantum (L. infantum) and Leishmaniamajor (L. major), both important pathogens. In this sense, quantitative structure-activity relationship (QSAR) methods, which are very useful in Bioorganic and Medicinal Chemistry to discover small-sized drugs, may help to identify not only new drugs but also new drug targets, if we apply them to proteins. Dyneins are important proteins of these parasites governing fundamental processes such as cilia and flagella motion, nuclear migration, organization of the mitotic splinde, and chromosome separation during mitosis. However, despite the interest for them as potential drug targets, so far there has been no report whatsoever on dyneins with QSAR techniques. To the best of our knowledge, we report here the first QSAR for dynein proteins. We used as input the Spectral Moments of a Markov matrix associated to the HP-Lattice Network of the protein sequence. The data contain 411 protein sequences of different species selected by ClustalX to develop a QSAR that correctly discriminates on average between 92.75% and 92.51% of dyneins and other proteins in four different train and cross-validation datasets. We also report a combined experimental and theoretic study of a new dynein sequence in order to illustrate the utility of the model to search for potential drug targets with a practical example. First, we carried out a 2D-electrophoresis analysis of L. infantum biological samples. Next, we excised from 2D-E gels one spot of interest belonging to an unknown protein or protein fragment in the region M<20,200 and pI<4. We used MASCOT search engine to find proteins in the L. major data base with the highest similarity score to the MS of the protein isolated from L. infantum. We used the QSAR model to predict the new sequence as dynein with probability of 99.99% without

  10. Understanding Substrate Selectivity of Human UDP-glucuronosyltransferases through QSAR modeling and analysis of homologous enzymes

    PubMed Central

    Dong, Dong; Ako, Roland; Hu, Ming; Wu, Baojian

    2015-01-01

    The UDP-glucuronosyltransferase (UGT) enzyme catalyzes the glucuronidation reaction which is a major metabolic and detoxification pathway in humans. Understanding the mechanisms for substrate recognition by UGT assumes great importance in an attempt to predict its contribution to xenobiotic/drug disposition in vivo. Spurred on by this interest, 2D/3D-quantitative structure activity relationships (QSAR) and pharmacophore models have been established in the absence of a complete mammalian UGT crystal structure. This review discusses the recent progress in modeling human UGT substrates including those with multiple sites of glucuronidation. A better understanding of UGT active site contributing to substrate selectivity (and regioselectivity) from the homologous enzymes (i.e., plant and bacterial UGTs, all belong to family 1 of glycosyltransferase (GT1)) is also highlighted, as these enzymes share a common catalytic mechanism and/or overlapping substrate selectivity. PMID:22385482

  11. QSAR on antiproliferative naphthoquinones based on a conformation-independent approach.

    PubMed

    Duchowicz, Pablo R; Bennardi, Daniel O; Bacelo, Daniel E; Bonifazi, Evelyn L; Rios-Luci, Carla; Padrón, José M; Burton, Gerardo; Misico, Rosana I

    2014-04-22

    The antiproliferative activities of a series of 36 naphthoquinone derivatives were subjected to a Quantitative Structure-Activity Relationships (QSAR) study. For this purpose a panel of four human cancer cell lines was used, namely HBL-100 (breast), HeLa (cervix), SW-1573 (non-small cell lung) and WiDr (colon). A conformation-independent representation of the chemical structure was established in order to avoid leading with the scarce experimental information on X-ray crystal structure of the drug interaction. The 1179 theoretical descriptors derived with E-Dragon and Recon software were simultaneously analyzed through linear regression models based on the Replacement Method variable subset selection technique. The established models were validated and tested through the use of external test sets of compounds, the Leave-One-Out Cross Validation method, Y-Randomization and Applicability Domain analysis. PMID:24631897

  12. Synthesis, pharmacological characterization, and quantitative structure-activity relationship analyses of 3,7,9,9-tetraalkylbispidines: derivatives with specific bradycardic activity.

    PubMed

    Schön, U; Antel, J; Brückner, R; Messinger, J; Franke, R; Gruska, A

    1998-01-29

    A series of 3,7,9,9-tetraalkyl-3,7-diazabicyclo[3.3.1]nonane derivatives (bispidines) was synthesized and identified as potential antiischemic agents. Pharmacological experiments in vitro as well as in vivo are described, and the results are listed. For selection of those compounds fitting best to the desired profile of a specific bradycardic antianginal agent--decrease in heart rate without affecting contractility and blood pressure--these results were scored and ranked. Quantitative structure--activity relationship (QSAR) analyses were performed and discussed a posteriori by means of Hansch, nonelementary discriminant and factor analysis to get insight into the molecular features determining the biological profile. Highly significant equations were obtained, indicating hydrophobic and steric effects. Both pharmacological ranking and QSAR considerations showed compound 6 as the optimum within the structural class under investigation. Compound 6 (tedisamil, KC8857) has been selected as the most promising compound and was chosen for further pharmacological and clinical investigations as an antiischemic drug. PMID:9464363

  13. Quantitative structure-activity relationship modeling on in vitro endocrine effects and metabolic stability involving 26 selected brominated flame retardants.

    PubMed

    Harju, Mikael; Hamers, Timo; Kamstra, Jorke H; Sonneveld, Edwin; Boon, Jan P; Tysklind, Mats; Andersson, Patrik L

    2007-04-01

    In this work, quantitative structure-activity relationships (QSARs) were developed to aid human and environmental risk assessment processes for brominated flame retardants (BFRs). Brominated flame retardants, such as the high-production-volume chemicals polybrominated diphenyl ethers (PBDEs), tetrabromobisphenol A, and hexabromocyclododecane, have been identified as potential endocrine disruptors. Quantitative structure-activity relationship models were built based on the in vitro potencies of 26 selected BFRs. The in vitro assays included interactions with, for example, androgen, progesterone, estrogen, and dioxin (aryl hydrocarbon) receptor, plus competition with thyroxine for its plasma carrier protein (transthyretin), inhibition of estradiol sulfation via sulfotransferase, and finally, rate of metabolization. The QSAR modeling, a number of physicochemical parameters were calculated describing the electronic, lipophilic, and structural characteristics of the molecules. These include frontier molecular orbitals, molecular charges, polarities, log octanol/water partitioning coefficient, and two- and three-dimensional molecularproperties. Experimental properties were included and measured for PBDEs, such as their individual ultraviolet spectra (200-320 nm) and retention times on three different high-performance liquid chromatography columns and one nonpolar gas chromatography column. Quantitative structure-activity relationship models based on androgen antagonism and metabolic degradation rates generally gave similar results, suggesting that lower-brominated PBDEs with bromine substitutions in ortho positions and bromine-free meta- and para positions had the highest potencies and metabolic degradation rates. Predictions made for the constituents of the technical flame retardant Bromkal 70-5DE found BDE 17 to be a potent androgen antagonist and BDE 66, which is a relevant PBDE in environmental samples, to be only a weak antagonist. PMID:17447568

  14. Ligand-based 3D QSAR analysis of reactivation potency of mono- and bis-pyridinium aldoximes toward VX-inhibited rat acetylcholinesterase.

    PubMed

    Dolezal, Rafael; Korabecny, Jan; Malinak, David; Honegr, Jan; Musilek, Kamil; Kuca, Kamil

    2015-03-01

    To predict unknown reactivation potencies of 12 mono- and bis-pyridinium aldoximes for VX-inhibited rat acetylcholinesterase (rAChE), three-dimensional quantitative structure-activity relationship (3D QSAR) analysis has been carried out. Utilizing molecular interaction fields (MIFs) calculated by molecular mechanical (MMFF94) and quantum chemical (B3LYP/6-31G*) methods, two satisfactory ligand-based CoMFA models have been developed: 1. R(2)=0.9989, Q(LOO)(2)=0.9090, Q(LTO)(2)=0.8921, Q(LMO(20%))(2)=0.8853, R(ext)(2)=0.9259, SDEP(ext)=6.8938; 2. R(2)=0.9962, Q(LOO)(2)=0.9368, Q(LTO)(2)=0.9298, Q(LMO(20%))(2)=0.9248, R(ext)(2)=0.8905, SDEP(ext)=6.6756. High statistical significance of the 3D QSAR models has been achieved through the application of several data noise reduction techniques (i.e. smart region definition SRD, fractional factor design FFD, uninformative/iterative variable elimination UVE/IVE) on the original MIFs. Besides the ligand-based CoMFA models, an alignment molecular set constructed by flexible molecular docking has been also studied. The contour maps as well as the predicted reactivation potencies resulting from 3D QSAR analyses help better understand which structural features are associated with increased reactivation potency of studied compounds. PMID:25588616

  15. QSAR models for oxidation of organic micropollutants in water based on ozone and hydroxyl radical rate constants and their chemical classification.

    PubMed

    Sudhakaran, Sairam; Amy, Gary L

    2013-03-01

    Ozonation is an oxidation process for the removal of organic micropollutants (OMPs) from water and the chemical reaction is governed by second-order kinetics. An advanced oxidation process (AOP), wherein the hydroxyl radicals (OH radicals) are generated, is more effective in removing a wider range of OMPs from water than direct ozonation. Second-order rate constants (k(OH) and k(O3) are good indices to estimate the oxidation efficiency, where higher rate constants indicate more rapid oxidation. In this study, quantitative structure activity relationships (QSAR) models for O(3) and AOP processes were developed, and rate constants, k(OH) and [Formula: see text] , were predicted based on target compound properties. The k(O3) and k(OH) values ranged from 5 * 10(-4) to 10(5) M(-1)s(-1) and 0.04 to 18 * (10(9)) M(-1) s(-1), respectively. Several molecular descriptors which potentially influence O(3) and OH radical oxidation were identified and studied. The QSAR-defining descriptors were double bond equivalence (DBE), ionisation potential (IP), electron-affinity (EA) and weakly-polar component of solvent accessible surface area (WPSA), and the chemical and statistical significance of these descriptors was discussed. Multiple linear regression was used to build the QSAR models, resulting in high goodness-of-fit, r(2) (>0.75). The models were validated by internal and external validation along with residual plots. PMID:23260175

  16. Three-dimensional quantitative structure-activity relationship study on anti-cancer activity of 3,4-dihydroquinazoline derivatives against human lung cancer A549 cells

    NASA Astrophysics Data System (ADS)

    Cho, Sehyeon; Choi, Min Ji; Kim, Minju; Lee, Sunhoe; Lee, Jinsung; Lee, Seok Joon; Cho, Haelim; Lee, Kyung-Tae; Lee, Jae Yeol

    2015-03-01

    A series of 3,4-dihydroquinazoline derivatives with anti-cancer activities against human lung cancer A549 cells were subjected to three-dimensional quantitative structure-activity relationship (3D-QSAR) studies using the comparative molecular similarity indices analysis (CoMSIA) approaches. The most potent compound, 1 was used to align the molecules. As a result, the best prediction was obtained with CoMSIA combined the steric, electrostatic, hydrophobic, hydrogen bond donor, and hydrogen bond acceptor fields (q2 = 0.720, r2 = 0.897). This model was validated by an external test set of 6 compounds giving satisfactory predictive r2 value of 0.923 as well as the scrambling stability test. This model would guide the design of potent 3,4-dihydroquinazoline derivatives as anti-cancer agent for the treatment of human lung cancer.

  17. Angiotensin-converting enzyme inhibitory effects by plant phenolic compounds: a study of structure activity relationships.

    PubMed

    Al Shukor, Nadin; Van Camp, John; Gonzales, Gerard Bryan; Staljanssens, Dorien; Struijs, Karin; Zotti, Moises J; Raes, Katleen; Smagghe, Guy

    2013-12-01

    In this study, 22 phenolic compounds were investigated to inhibit the angiotensin-converting enzyme (ACE). Tannic acid showed the highest activity (IC50 = 230 μM). The IC50 values obtained for phenolic acids and flavonoids ranged between 0.41 and 9.3 mM. QSAR analysis confirmed that the numbers of hydroxyl groups on the benzene ring play an important role for activity of phenolic compounds and that substitution of hydroxyl groups by methoxy groups decreased activity. Docking studies indicated that phenolic acids and flavonoids inhibit ACE via interaction with the zinc ion and this interaction is stabilized by other interactions with amino acids in the active site. Other compounds, such as resveratrol and pyrogallol, may inhibit ACE via interactions with amino acids at the active site, thereby blocking the catalytic activity of ACE. These structure-function relationships are useful for designing new ACE inhibitors and potential blood-pressure-lowering compounds based on phenolic compounds. PMID:24219111

  18. 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. PMID:26748251

  19. Synthesis, Molecular Structure, Metabolic Stability and QSAR Studies of a Novel Series of Anticancer N-Acylbenzenesulfonamides.

    PubMed

    Żołnowska, Beata; Sławiński, Jarosław; Belka, Mariusz; Bączek, Tomasz; Kawiak, Anna; Chojnacki, Jarosław; Pogorzelska, Aneta; Szafrański, Krzysztof

    2015-01-01

    A series of novel N-acyl-4-chloro-5-methyl-2-(R¹-methylthio)benzenesulfonamides 18-47 have been synthesized by the reaction of N-[4-chloro-5-methyl-2-(R¹-methylthio) benzenesulfonyl]cyanamide potassium salts with appropriate carboxylic acids. Some of them showed anticancer activity toward the human cancer cell lines MCF-7, HCT-116 and HeLa, with the growth percentages (GPs) in the range from 7% to 46%. Quantitative structure-activity relationship (QSAR) studies on the cytotoxic activity of N-acylsulfonamides toward MCF-7, HCT-116 and HeLa were performed by using topological, ring and charge descriptors based on the stepwise multiple linear regression technique (MLR). The QSAR studies revealed three predictive and statistically significant models for the investigated compounds. The results obtained with these models indicated that the anticancer activity of N-acylsulfonamides depends on topological distances, number of ring system, maximum positive charge and number of atom-centered fragments. The metabolic stability of the selected compounds had been evaluated on pooled human liver microsomes and NADPH, both R¹ and R² substituents of the N-acylsulfonamides simultaneously affected them. PMID:26506328

  20. Dual Allosteric Effect in Glycine/NMDA Receptor Antagonism: A Comparative QSAR Approach

    PubMed Central

    Sharma, Manish; Gupta, Vipin B.

    2010-01-01

    A comparative Hansch type QSAR study was conducted using multiple regression analysis on various sets of quinoxalines, quinoxalin-4-ones, quinazoline-2-carboxylates, 4-hydroxyquinolin-2(1H)-ones, 2-carboxytetrahydroquinolines, phenyl-hydroxy-quinolones, nitroquinolones and 4-substituted-3-phenylquinolin-2(1H)-ones as selective glycine/NMDA site antagonists. Ten statistically validated equations were developed, which indicated the importance of CMR, Verloop’s sterimol L1 and ClogP parameters in contributing towards biological activity. Interestingly, normal and inverse parabolic relationships were found with CMR in different series, indicating a dual allosteric binding mode in glycine/NMDA antagonism. Equations reveal an optimum CMR of 10 ± 10% is required for good potency of antagonists. Other equations indicate the presence of anionic functionality at 4-position of quinoline/quinolone ring system is not absolutely required for effective binding. The observations are laterally validated and in accordance with previous studies.

  1. Expert QSAR system for predicting the bioconcentration factor under the REACH regulation.

    PubMed

    Grisoni, Francesca; Consonni, Viviana; Vighi, Marco; Villa, Sara; Todeschini, Roberto

    2016-07-01

    Expert systems are a rational integration of several models that generally aim to exploit their advantages and overcome their drawbacks. This work is founded on our previously published Quantitative Structure-Activity Relationship (QSAR) classification scheme, which detects compounds whose Bioconcentration Factor (BCF) is (1) well predicted by the octanol-water partition coefficient (KOW), (2) underestimated by KOW or (3) overestimated by KOW. The classification scheme served as the starting point to identify and combine the best BCF model for each class among three VEGA models and one KOW-based equation. The rationalized model integration showed stability and surprising performance on unknown data when compared with benchmark BCF models. Model simplicity, transparency and mechanistic interpretation were fostered in order to allow for its application and acceptance within the REACH framework. PMID:27152714

  2. QSAR and docking studies of anthraquinone derivatives by similarity cluster prediction.

    PubMed

    Harsa, Alexandra M; Harsa, Teodora E; Diudea, Mircea V

    2016-06-01

    Forty anthraquinone derivatives have been downloaded from PubChem database and investigated in a quantitative structure-activity relationships (QSAR) study. The models describing log P and LD50 of this set were built up on the hypermolecule scheme that mimics the investigated receptor space; the models were validated by the leave-one-out procedure, in the external test set and in a new version of prediction by using similarity clusters. Molecular docking approach using Lamarckian Genetic Algorithm was made on this class of anthraquinones with respect to 3Q3B receptor. The best scored molecules in the docking assay were used as leaders in the similarity clustering procedure. It is demonstrated that the LD50 data of this set of anthraquinones are related to the binding energies of anthraquinone ligands to the 3Q3B receptor. PMID:26018421

  3. Mode of action and the assessment of chemical hazards in the presence of limited data: use of structure-activity relationships (SAR) under TSCA, Section 5.

    PubMed Central

    Auer, C M; Nabholz, J V; Baetcke, K P

    1990-01-01

    Section 5 of the Toxic Substances Control Act (TSCA) requires that manufacturers and importers of new chemicals must submit a Premanufacture Notification (PMN) to the U.S. Environmental Protection Agency 90 days before they intend to commence manufacture or import. Certain information such as chemical identity, uses, etc., must be included in the notification. The submission of test data on the new substance, however, is not required, although any available health and environmental information must be provided. Nonetheless, over half of all PMNs submitted to the agency do not contain any test data; because PMN chemicals are new, no test data is generally available in the scientific literature. Given this situation, EPA has had to develop techniques for hazard assessment that can be used in the presence of limited test data. EPA's approach has been termed "structure-activity relationships" (SAR) and involves three major components: the first is critical evaluation and interpretation of available toxicity data on the chemical; the second component involves evaluation of test data available on analogous substances and/or potential metabolites; and the third component involves the use of mathematical expressions for biological activity known as "quantitative structure-activity relationships" (QSARs). At present, the use of QSARs is limited to estimating physical chemical properties, environmental toxicity, and bioconcentration factors. An important overarching element in EPA's approach is the experience and judgment of scientific assessors in interpreting and integrating the available data and information. Examples are provided that illustrate EPA's approach to hazard assessment for PMN chemicals. PMID:2269224

  4. DEVELOPMENT OF QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIPS (QSARS) TO PREDICT TOXICITY FOR A VARIETY OF HUMAN AND ECOLOGICAL ENDPOINTS

    EPA Science Inventory

    A web accessible software tool is being developed to predict the toxicity of unknown chemicals for a wide variety of endpoints. The tool will enable a user to easily predict the toxicity of a query compound by simply entering its structure in a 2-dimensional (2-D) chemical sketc...

  5. Mining Discriminative Patterns from Graph Data with Multiple Labels and Its Application to Quantitative Structure-Activity Relationship (QSAR) Models.

    PubMed

    Shao, Zheng; Hirayama, Yuya; Yamanishi, Yoshihiro; Saigo, Hiroto

    2015-12-28

    Graph data are becoming increasingly common in machine learning and data mining, and its application field pervades to bioinformatics and cheminformatics. Accordingly, as a method to extract patterns from graph data, graph mining recently has been studied and developed rapidly. Since the number of patterns in graph data is huge, a central issue is how to efficiently collect informative patterns suitable for subsequent tasks such as classification or regression. In this paper, we consider mining discriminative subgraphs from graph data with multiple labels. The resulting task has important applications in cheminformatics, such as finding common functional groups that trigger multiple drug side effects, or identifying ligand functional groups that hit multiple targets. In computational experiments, we first verify the effectiveness of the proposed approach in synthetic data, then we apply it to drug adverse effect prediction problem. In the latter dataset, we compared the proposed method with L1-norm logistic regression in combination with the PubChem/Open Babel fingerprint, in that the proposed method showed superior performance with a much smaller number of subgraph patterns. Software is available from https://github.com/axot/GLP. PMID:26549421

  6. Design, synthesis, biological activities, and 3D-QSAR of new N,N'-diacylhydrazines containing 2-(2,4-dichlorophenoxy)propane moiety.

    PubMed

    Liu, Xing-Hai; Pan, Li; Ma, Yi; Weng, Jian-Quan; Tan, Cheng-Xia; Li, Yong-Hong; Shi, Yan-Xia; Li, Bao-Ju; Li, Zheng-Ming; Zhang, Yong-Gang

    2011-10-01

    A series of new N,N'-diacylhydrazine derivatives were synthesized efficiently under microwave irradiation. Their structures were characterized by (1) H NMR, MS, and elemental analysis. Various biological activities of these compounds were tested. Most of them exhibited higher herbicidal activities against dicotyledonous weeds than monocotyledonous weeds. In addition, favorable in vivo fungicidal activities were also found of these compounds against Cladosporium cucumerinum, Corynespora cassiicola, Sclerotinia sclerotiorum(Lib.)de Bary, Erysiphe cichoracearum, and Colletotrichum orbiculare (Berk aLMont) Arx. All compounds displayed excellent plant growth regulatory activities: 100% inhibition was achieved against the radicle growth of cucumber. To further investigate the structure-activity relationship, comparative molecular field analysis was performed on the basis of herbicidal activity data, resulting in a statistically reliable model with good predictive power (r(2) = 0.913, q(2) =0.556). Based on the calculation, five additional novel compounds were designed and synthesized. Satisfyingly, compound 4u displayed excellent herbicidal activity (94.7%) at 1500 g/ha, although it is less active than 2,4-D. Meanwhile, this compound also exhibited good fungicidal activity against C. orbiculare (Berk aLMont) Arx (82.16%). PMID:21816005

  7. Design, Synthesis, Antifungal Activities and 3D-QSAR of New N,N′-Diacylhydrazines Containing 2,4-Dichlorophenoxy Moiety

    PubMed Central

    Sun, Na-Bo; Shi, Yan-Xia; Liu, Xing-Hai; Ma, Yi; Tan, Cheng-Xia; Weng, Jian-Quan; Jin, Jian-Zhong; Li, Bao-Ju

    2013-01-01

    A series of new N,N′-diacylhydrazine derivatives were designed and synthesized. Their structures were verified by 1H-NMR, mass spectra (MS) and elemental analysis. The antifungal activities of these N,N′-diacylhydrazines were evaluated. The bioassay results showed that most of these N,N′-diacylhydrazines showed excellent antifungal activities against Cladosporium cucumerinum, Corynespora cassiicola, Sclerotinia sclerotiorum, Erysiphe cichoracearum, and Colletotrichum orbiculare in vivo. The half maximal effective concentration (EC50) of one of the compounds was also determined, and found to be comparable with a commercial drug. To further investigate the structure–activity relationship, comparative molecular field analysis (CoMFA) was performed on the basis of antifungal activity data. Both the steric and electronic field distributions of CoMFA are in good agreement in this study. PMID:24189221

  8. Design, synthesis, antifungal activities and 3D-QSAR of new N,N'-diacylhydrazines containing 2,4-dichlorophenoxy moiety.

    PubMed

    Sun, Na-Bo; Shi, Yan-Xia; Liu, Xing-Hai; Ma, Yi; Tan, Cheng-Xia; Weng, Jian-Quan; Jin, Jian-Zhong; Li, Bao-Ju

    2013-01-01

    A series of new N,N'-diacylhydrazine derivatives were designed and synthesized. Their structures were verified by 1H-NMR, mass spectra (MS) and elemental analysis. The antifungal activities of these N,N'-diacylhydrazines were evaluated. The bioassay results showed that most of these N,N'-diacylhydrazines showed excellent antifungal activities against Cladosporium cucumerinum, Corynespora cassiicola, Sclerotinia sclerotiorum, Erysiphe cichoracearum, and Colletotrichum orbiculare in vivo. The half maximal effective concentration (EC50) of one of the compounds was also determined, and found to be comparable with a commercial drug. To further investigate the structure-activity relationship, comparative molecular field analysis (CoMFA) was performed on the basis of antifungal activity data. Both the steric and electronic field distributions of CoMFA are in good agreement in this study. PMID:24189221

  9. Modelling for antimicrobial activities of ionic liquids towards Escherichia coli, Staphylococcus aureus and Candida albicans using linear free energy relationship descriptors.

    PubMed

    Cho, Chul-Woong; Park, Jeong-Soo; Stolte, Stefan; Yun, Yeoung-Sang

    2016-07-01

    To predict antimicrobial activities i.e., minimal inhibitory concentration (MIC) and minimal biocidal concentration (MBC) for ionic liquids (ILs) against Escherichia coli, Staphylococcus aureus and Candida albicans, six quantitative structure-activity relationship (QSAR) models were developed using linear free energy relationship (LFER) descriptors calculated by density functional theory and conductor screening model. The LFER descriptors are excess molar refraction, dipolarity/polarizability, H-bonding acidity, H-bonding basicity, McGowan volume, cationic interaction, and anionic interaction. By excluding some descriptors with ignorable contributions to training set, components of the QSAR models were simplified. Their estimated predictabilities were in R(2)=0.900, standard error (SE; in log unit of μM)=0.430 for log 1/MIC of E. coli, R(2)=0.934, SE=0.370 for log 1/MBC of E. coli, R(2)=0.910, SE=0.470 for log 1/MIC of S. aureus, R(2)=0.947, SE=0.350 for log 1/MBC of S. aureus, R(2)=0.892, SE=0.362 for log 1/MIC of C. albicans and R(2)=0.803, SE=0.233 for log 1/MBC of C. albicans. Then, except for log 1/MBC of C. albicans due to lack of data points, the models were validated by comparing between observed and calculated values of test set; its checked correlations were all within R(2) of 0.921. PMID:26974242

  10. Synthesis and Quantitative Structure-activity Relationships Study for Arylpropenamide Derivatives as Inhibitors of Hepatitis B Virus Replication.

    PubMed

    Min, Ma; Xingjun, Jiang; Xueding, Wang; Hao, Zou; Weiqing, Yang; Yuanyuan, Zhang; Changrong, Peng; Zicheng, Li; Jing, Yang; Quan, Du; Menglin, Ma

    2016-09-01

    A series of new arylpropenamide derivatives containing different aryl groups were synthesized, characterized, and evaluated for their anti-hepatitis B virus (HBV) activities. A new high accuracy QSAR model of arylpropenamide was constructed based on a more completely activities data and calculation parameter. The 2D-QSAR equations, by using DFT and multiple linear regression analysis methods, revealed that higher value of thermal energy (TE) and lower entropy (S(ө) ) increase the anti-HBV activities of the arylpropenamide molecules. Predictive 3D-QSAR models were established by SYBYL multifit molecular alignment rule. The optimum models were all statistically significant with cross-validated and conventional coefficients, indicating that they were reliable enough for activity prediction. PMID:27085815

  11. 2D and 3D QSAR models for identifying diphenylpyridylethanamine based inhibitors against cholesteryl ester transfer protein.

    PubMed

    Chen, Meimei; Yang, Xuemei; Lai, Xinmei; Gao, Yuxing

    2015-10-15

    Cholesteryl ester transfer protein (CETP) inhibitors hold promise as new agents against coronary heart disease. Molecular modeling techniques such as 2D-QSAR and 3D-QSAR analysis were applied to establish models to distinguish potent and weak CETP inhibitors. 2D and 3D QSAR models-based a series of diphenylpyridylethanamine (DPPE) derivatives (newly identified as CETP inhibitors) were then performed to elucidate structural and physicochemical requirements for higher CETP inhibitory activity. The linear and spline 2D-QSAR models were developed through multiple linear regression (MLR) and support vector machine (SVM) methods. The best 2D-QSAR model obtained by SVM gave a high predictive ability (R(2)train=0.929, R(2)test=0.826, Q(2)LOO=0.780). Also, the 2D-QSAR models uncovered that SlogP_VSA0, E_sol and Vsurf_DW23 were important features in defining activity. In addition, the best 3D-QSAR model presented higher predictive ability (R(2)train=0.958, R(2)test=0.852, Q(2)LOO=0.734) based on comparative molecular field analysis (CoMFA). Meanwhile, the derived contour maps from 3D-QSAR model revealed the significant structural features (steric and electronic effects) required for improving CETP inhibitory activity. Consequently, twelve newly designed DPPE derivatives were proposed to be robust and potent CETP inhibitors. Overall, these derived models may help to design novel DPPE derivatives with better CETP inhibitory activity. PMID:26346366

  12. Facile synthesis and quantitative structure-activity relationship study of antitumor active 2-(4-oxo-thiazolidin-2-ylidene)-3-oxo-propionitriles.

    PubMed

    Hanna, Mona Maurice; George, Riham François

    2012-01-01

    2-(5-Arylidene-4-oxo-3-phenyl-thiazolidin-2-ylidene)-3-oxo-propionitriles 4a-j were prepared via condensation of aromatic aldehydes with 4-thiazolidinones 3a,b. The latter was obtained via electrophilic attack of phenylisothiocyanate on 3-oxo-propionitriles 1a,b followed by reaction with chloroacetyl chloride under basic condition. Additionally, 2-(5-heteroalicyclic methylene) analogues 5a-h were prepared via Mannich reaction of the appropriate secondary amines and formaldehyde with 4-thiazolidinones 3a,b. Many of the synthesized compounds exhibited promising antitumor properties against colon HCT116 and breast T47D cell lines. 3D-Pharmacophore modeling and quantitative structure-activity relationship (QSAR) analysis were combined to explain the observed antitumor properties. PMID:22976330

  13. Structural alerts for predicting clastogenic activity of pro-oxidant flavonoid compounds: quantitative structure-activity relationship study.

    PubMed

    Yordi, Estela Guardado; Pérez, Enrique Molina; Matos, Maria Joao; Villares, Eugenio Uriarte

    2012-02-01

    Flavonoids have been reported to exert multiple biological effects that include acting as pro-oxidants at very high doses. The authors determined a structural alert to identify the clastogenic activity of a series of flavonoids with pro-oxidant activity. The methodology was based on a quantitative structure-activity relationship (QSAR) study. Specifically, the authors developed a virtual screening method for a clastogenic model using the topological substructural molecular design (TOPS-MODE) approach. It represents a useful platform for the automatic generation of structural alerts, based on the calculation of spectral moments of molecular bond matrices appropriately weighted, taking into account the hydrophobic, electronic, and steric molecular features. Therefore, it was possible to establish the structural criteria for maximal clastogenicity of pro-oxidant flavonoids: the presence of a 3-hydroxyl group and a 4-carbonyl group in ring C, the maximal number of hydroxyl groups in ring B, the presence of methoxyl and phenyl groups, the absence of a 2,3-double bond in ring C, and the presence of 5,7 hydroxyl groups in ring A. The presented clastogenic model may be useful for screening new pro-oxidant compounds. This alert could help in the design of new and efficient flavonoids, which could be used as bioactive compounds in nutraceuticals and functional food. PMID:21940715

  14. 3D QSAR studies on substituted benzimidazole derivatives as angiotensin II-AT1 receptor antagonist.

    PubMed

    Vyas, Vivek K; Ghate, Manjunath; Chintha, Chetan; Patel, Paresh

    2013-09-01

    This study investigated 3D quantitative structure-activity relationships (QSAR) for a range of substituted benzimidazole derivatives as AngII-AT1 receptor antagonists by comparative molecular field analysis (CoMFA) and comparative molecular similarity indices (CoMSIA). The alignment strategy was used for these compounds by means of Distill function defined in SYBYL X 1.2. The best CoMFA and CoMSIA models were obtained for the training set compounds was statistically significant with leave-one-out (LOO) validation correlation coefficient (q²) of 0.613 and 0.622, cross validated coefficient (r²cv) of 0.617 and 0.607, respectively and conventional coefficient (r²ncv) of 0.886 and 0.859, respectively. Both the models were validated by a test set of 18 compounds giving satisfactory predicted correlation coefficient (r²pred) of 0.714 and 0.549 for CoMFA and CoMSIA models, respectively. Generated 3D QSAR models were used for the prediction of pIC50 of an external dataset of 10 compounds for predictive validation, which gave conventional r² of 0.893 for CoMFA model, and 0.774 for CoMSIA model. We identified some key features in substituted benzimidazole derivatives, such as the importance of lipophilicity and H-bonding at 2- and 5, 6, 7- position of benzimidazole ring, respectively, for good antagonistic activity. CoMFA and CoMSIA models generated in this work provide useful information for the design of new compounds and helped in prediction of antagonistic activity. PMID:24010938

  15. QSAR, docking, dynamic simulation and quantum mechanics studies to explore the recognition properties of cholinesterase binding sites.

    PubMed

    Correa-Basurto, J; Bello, M; Rosales-Hernández, M C; Hernández-Rodríguez, M; Nicolás-Vázquez, I; Rojo-Domínguez, A; Trujillo-Ferrara, J G; Miranda, René; Flores-Sandoval, C A

    2014-02-25

    A set of 84 known N-aryl-monosubstituted derivatives (42 amides: series 1 and 2, and 42 imides: series 3 an 4, from maleic and succinic anhydrides, respectively) that display inhibitory activity toward both acetylcholinesterase and butyrylcholinesterase (ChEs) was considered for Quantitative structure-activity relationship (QSAR) studies. These QSAR studies employed docking data from both ChEs that were previously submitted to molecular dynamics (MD) simulations. Donepezil and galanthamine stereoisomers were included to analyze their quantum mechanics properties and for validating the docking procedure. Quantum parameters such as frontier orbital energies, dipole moment, molecular volume, atomic charges, bond length and reactivity parameters were measured, as well as partition coefficients, molar refractivity and polarizability were also analyzed. In order to evaluate the obtained equations, four compounds: 1a (4-oxo-4-(phenylamino)butanoic acid), 2a ((2Z)-4-oxo-4-(phenylamino)but-2-enoic acid), 3a (2-phenylcyclopentane-1,3-dione) and 4a (2-phenylcyclopent-4-ene-1,3-dione) were employed as independent data set, using only equations with r(m(test))²>0.5. It was observed that residual values gave low value in almost all series, excepting in series 1 for compounds 3a and 4a, and in series 4 for compounds 1a, 2a and 3a, giving a low value for 4a. Consequently, equations seems to be specific according to the structure of the evaluated compound, that means, series 1 fits better for compound 1a, series 3 or 4 fits better for compounds 3a or 4a. Same behavior was observed in the butyrylcholinesterase (BChE). Therefore, obtained equations in this QSAR study could be employed to calculate the inhibition constant (Ki) value for compounds having a similar structure as N-aryl derivatives described here. The QSAR study showed that bond lengths, molecular electrostatic potential and frontier orbital energies are important in both ChE targets. Docking studies revealed that

  16. Quantitative Structure-Cytotoxic Activity Relationship 1-(Benzoyloxy)urea and Its Derivative.

    PubMed

    Hardjono, Suko; Siswodihardjo, Siswandono; Pramono, Purwanto; Darmanto, Win

    2016-01-01

    Drug development is originally carried out on a trial and error basis and it is cost-prohibitive. To minimize the trial and error risks, drug design is needed. One of the compound development processes to get a new drug is by designing a structure modification of the mother compound whose activities are recognized. A substitution of the mother compounds alters the physicochemical properties: lipophilic, electronic and steric properties. In Indonesia, one of medical treatments to cure cancer is through chemotherapy and hydroxyurea. Some derivatives, phenylthiourea, phenylurea, benzoylurea, thiourea and benzoylphenylurea, have been found to be anticancer drug candidates. To predict the activity of the drug compound before it is synthesized, the in-silico test is required. From the test, Rerank Score which is the energy of interaction between the receptor and the ligand molecule is then obtained. Hydroxyurea derivatives were synthesized by modifying Schotten-Baumann's method by the addition of benzoyl group and its homologs resulted in the increase of lipophilic, electronic and steric properties, and cytotoxic activity. Synthesized compounds were 1-(benzoyloxy)urea and its derivatives. Structure characterization was obtained by the spectrum of UV, IR, H NMR, C NMR and Mass Spectrometer. Anticancer activity was carried out using MTT method on HeLa cells. The Quantitative Structure-Cytotoxic Activity Relationships of 1-(benzoyloxy)urea compound and its derivatives was calculated using SPSS. The chemical structure was described, namely: ClogP, π, σ, RS, CMR and Es; while, the cytotoxic activity was indicated by log (1 / IC50). The results show that the best equation of Quantitative Structure-Cytotoxic Activity Relationships (QSAR) of 1- (benzoyloxy)urea compound and its derivatives is Log 1/IC50 = - 0.205 (+ 0.068) σ - 0.051 (+ 0.022) Es - 1.911 (+ 0.020). PMID:27222144

  17. Autocorrelation descriptor improvements for QSAR: 2DA_Sign and 3DA_Sign.

    PubMed

    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. PMID:26721261

  18. Structure based 3D-QSAR studies of Interleukin-2 inhibitors: Comparing the quality and predictivity of 3D-QSAR models obtained from different alignment methods and charge calculations.

    PubMed

    Halim, Sobia Ahsan; Zaheer-ul-Haq

    2015-08-01

    Interleukin-2 is an essential cytokine in an innate immune response, and is a promising drug target for several immunological disorders. In the present study, structure-based 3D-QSAR modeling was carried out via Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Index Analysis (CoMSIA) methods. Six different partial charge calculation methods were used in combination with two different alignment methods to scrutinize their effects on the predictive power of 3D-QSAR models. The best CoMFA and CoMSIA models were obtained with the AM1 charges when used with co-conformer based substructure alignment (CCBSA) method. The obtained models posses excellent correlation coefficient value and also exhibited good predictive power (for CoMFA: q(2)=0.619; r(2)=0.890; r(2)Pred=0.765 and for CoMSIA: q(2)=0.607; r(2)=0.884; r(2)Pred=0.655). The developed models were further validated by using a set of another sixteen compounds as external test set 2 and both models showed strong predictive power with r(2)Pred=>0.8. The contour maps obtained from these models better interpret the structure activity relationship; hence the developed models would help to design and optimize more potent IL-2 inhibitors. The results might have implications for rational design of specific anti-inflammatory compounds with improved affinity and selectivity. PMID:26051521

  19. Elicitation of the most important structural properties of ionic liquids affecting ecotoxicity in limnic green algae; a QSAR approach.

    PubMed

    Izadiyan, Parisa; Fatemi, M H; Izadiyan, Mahsa

    2013-01-01

    Many ionic liquids are soluble in water and their impact on the aquatic environment has to be evaluated. However, due to the large number of ionic liquids and lack of experimental data, it is necessary to develop estimation procedures in order to reduce the materials and time consumption. In this study using multilayer perceptron neural network (MLP), ant colony optimization (ACO) and multiple linear regression (MLR) strategies, good predictive quantitative structure-activity relationships (QSAR) models were introduced and structural parameters affecting ecotoxicity of ionic liquids in limnic green algae (Scenedesmus vacuolatus) were revealed. Moreover, principal component analysis (PCA) and cluster analysis (CA) approaches were also applied to visualize any possible patterns or relationships among ionic liquids data. It was revealed that selected descriptors of the MLR model are also capable of clustering ionic liquids according to their four level of toxicity. PMID:23107477

  20. Regression methods for developing QSAR and QSPR models to predict compounds of specific pharmacodynamic, pharmacokinetic and toxicological properties.

    PubMed

    Yap, C W; Li, H; Ji, Z L; Chen, Y Z

    2007-11-01

    Quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR) models have been extensively used for predicting compounds of specific pharmacodynamic, pharmacokinetic, or toxicological property from structure-derived physicochemical and structural features. These models can be developed by using various regression methods including conventional approaches (multiple linear regression and partial least squares) and more recently explored genetic (genetic function approximation) and machine learning (k-nearest neighbour, neural networks, and support vector regression) approaches. This article describes the algorithms of these methods, evaluates their advantages and disadvantages, and discusses the application potential of the recently explored methods. Freely available online and commercial software for these regression methods and the areas of their applications are also presented. PMID:18045213

  1. The conformation and activity relationship of benzofuran type of angiotensin II receptor antagonists.

    PubMed

    Yoo, S E; Kim, S K; Lee, S H; Kim, N J; Lee, D W

    2000-09-01

    As a continuing effort to establish the structure and activity relationship in a benzofuran type of angiotensin II antagonist, we synthesized various regioisomers and performed a series of QSAR analyses. The conformational analyses of target isomers were carried out using molecular mechanics and fine-tuned using the information from the NMR NOE experiment. The conformations of compounds with a good binding activity are quite similar to that of DuP753, a prototype of AII antagonist, suggesting that these compounds also bind to the same site of AII receptor. We then studied the compounds with a varied length of the hydroxyl group bearing side chain to find out the optimum distance between the hydroxyl group and the imidazole ring. The CoMFA with these compounds gave acceptable statistical measures (cross-validated r2 and conventional r2 to be 0.881 and 0.974, respectively) and the map was well consistent with the previously proposed pharmacophore. PMID:11026543

  2. Mixed-model QSAR at the human mineralocorticoid receptor: predicting binding mode and affinity of anabolic steroids.

    PubMed

    Peristera, Ourania; Spreafico, Morena; Smiesko, Martin; Ernst, Beat; Vedani, Angelo

    2009-09-28

    We present a computational study on the human mineralocorticoid receptor (hMR) that is based on multi-dimensional quantitative structure-activity relationships (mQSAR). Therein, we identified the binding mode of 48 steroid and non-steroid homologues by flexible docking to the crystal structure (software Yeti) and quantified it using 6D-QSAR (software Quasar). The receptor surrogate, evolved using a genetic algorithm, converged at a cross-validated r2 of 0.810, and yielded a predictive r2 of 0.661. The model was challenged by a series of scramble tests and by consensus scoring (software Raptor: r2=0.844, predictive r(2)=0.620). The model was then employed to predict the binding affinity of 26 anabolic steroids, demonstrating to which extent they might disrupt the endocrine system via binding to the hMR. The model for the hMR was added to the VirtualToxLab, a technology developed by the Biographics Laboratory 3R, allows the identification of the endocrine-disrupting potential of drugs, chemicals and natural products in silico. PMID:19523507

  3. Exploration of Novel Inhibitors for Class I Histone Deacetylase Isoforms by QSAR Modeling and Molecular Dynamics Simulation Assays

    PubMed Central

    Noor, Zainab; Afzal, Noreen; Rashid, Sajid

    2015-01-01

    Histone deacetylases (HDAC) are metal-dependent enzymes and considered as important targets for cell functioning. Particularly, higher expression of class I HDACs is common in the onset of multiple malignancies which results in deregulation of many target genes involved in cell growth, differentiation and survival. Although substantial attempts have been made to control the irregular functioning of HDACs by employing various inhibitors with high sensitivity towards transformed cells, limited success has been achieved in epigenetic cancer therapy. Here in this study, we used ligand-based pharmacophore and 2-dimensional quantitative structure activity relationship (QSAR) modeling approaches for targeting class I HDAC isoforms. Pharmacophore models were generated by taking into account the known IC50 values and experimental energy scores with extensive validations. The QSAR model having an external R2 value of 0.93 was employed for virtual screening of compound libraries. 10 potential lead compounds (C1-C10) were short-listed having strong binding affinities for HDACs, out of which 2 compounds (C8 and C9) were able to interact with all members of class I HDACs. The potential binding modes of HDAC2 and HDAC8 to C8 were explored through molecular dynamics simulations. Overall, bioactivity and ligand efficiency (binding energy/non-hydrogen atoms) profiles suggested that proposed hits may be more effective inhibitors for cancer therapy. PMID:26431201

  4. Substructural QSAR approaches and topological pharmacophores.

    PubMed Central

    Franke, R; Huebel, S; Streich, W J

    1985-01-01

    For large and diverse data sets, simple QSAR methods based on linear and additive models can no longer be applied. In such cases topological methods using descriptors directly derivable from two-dimensional chemical structures provide a useful alternative. The results of such analyses can be used for lead optimization, to guide biological testing and even aid in the design of novel compounds. Various types of topological descriptors and algorithms are briefly discussed. Which of those is to be selected depends on the objective of the investigation and the properties of the data set. Two new methods, LOGANA and LOCON, are discussed in some more detail. With the help of these methods, substructural patterns ("topological pharmacophores") characteristic of compounds possessing a certain biological property can be evaluated. Both methods are designed in such a way that full use can be made of the data handling capacity of computers while maintaining an optimal impact of the experience of the researcher. They are model-free and do not require any mathematical knowledge. While LOGANA deals with semiquantitative or even qualitative biological data, LOCON can be applied to activity data on a continuous scale. The basic procedure in both cases consists in the stepwise combination of substructural descriptors by the logical operations "and," "or" and "not." With a simple example the utility of the methods is demonstrated. PMID:3905376

  5. Development of 3D-QSAR Model for Acetylcholinesterase Inhibitors Using a Combination of Fingerprint, Molecular Docking, and Structure-Based Pharmacophore Approaches.

    PubMed

    Lee, Sehan; Barron, Mace G

    2015-11-01

    Acetylcholinesterase (AChE), a serine hydrolase vital for regulating the neurotransmitter acetylcholine in animals, has been used as a target for drugs and pesticides. With the increasing availability of AChE crystal structures, with or without ligands bound, structure-based approaches have been successfully applied to AChE inhibitors (AChEIs). The major limitation of these approaches has been the small applicability domain due to the lack of structural diversity in the training set. In this study, we developed a 3 dimensional quantitative structure-activity relationship (3D-QSAR) for inhibitory activity of 89 reversible and irreversible AChEIs including drugs and insecticides. A 3D-fingerprint descriptor encoding protein-ligand interactions was developed using molecular docking and structure-based pharmacophore to rationalize the structural requirements responsible for the activity of these compounds. The obtained 3D-QSAR model exhibited high correlation value (R(2) = 0.93) and low mean absolute error (MAE = 0.32 log units) for the training set (n = 63). The model was predictive across a range of structures as shown by the leave-one-out cross-validated correlation coefficient (Q(2) = 0.89) and external validation results (n = 26, R(2) = 0.89, and MAE = 0.38 log units). The model revealed that the compounds with high inhibition potency had proper conformation in the active site gorge and interacted with key amino acid residues, in particular Trp84 and Phe330 at the catalytic anionic site, Trp279 at the peripheral anionic site, and Gly118, Gly119, and Ala201 at the oxyanion hole. The resulting universal 3D-QSAR model provides insight into the multiple molecular interactions determining AChEI potency that may guide future chemical design and regulation of toxic AChEIs. PMID:26202430

  6. Insights into the Interactions between Maleimide Derivates and GSK3β Combining Molecular Docking and QSAR

    PubMed Central

    Quesada-Romero, Luisa; Mena-Ulecia, Karel; Tiznado, William; Caballero, Julio

    2014-01-01

    Many protein kinase (PK) inhibitors have been reported in recent years, but only a few have been approved for clinical use. The understanding of the available molecular information using computational tools is an alternative to contribute to this process. With this in mind, we studied the binding modes of 77 maleimide derivates inside the PK glycogen synthase kinase 3 beta (GSK3β) using docking experiments. We found that the orientations that these compounds adopt inside GSK3β binding site prioritize the formation of hydrogen bond (HB) interactions between the maleimide group and the residues at the hinge region (residues Val135 and Asp133), and adopt propeller-like conformations (where the maleimide is the propeller axis and the heterocyclic substituents are two slanted blades). In addition, quantitative structure–activity relationship (QSAR) models using CoMSIA methodology were constructed to explain the trend of the GSK3β inhibitory activities for the studied compounds. We found a model to explain the structure–activity relationship of non-cyclic maleimide (NCM) derivatives (54 compounds). The best CoMSIA model (training set included 44 compounds) included steric, hydrophobic, and HB donor fields and had a good Q2 value of 0.539. It also predicted adequately the most active compounds contained in the test set. Furthermore, the analysis of the plots of the steric CoMSIA field describes the elements involved in the differential potency of the inhibitors that can be considered for the selection of suitable inhibitors. PMID:25010341

  7. Insights into the interactions between maleimide derivates and GSK3β combining molecular docking and QSAR.

    PubMed

    Quesada-Romero, Luisa; Mena-Ulecia, Karel; Tiznado, William; Caballero, Julio

    2014-01-01

    Many protein kinase (PK) inhibitors have been reported in recent years, but only a few have been approved for clinical use. The understanding of the available molecular information using computational tools is an alternative to contribute to this process. With this in mind, we studied the binding modes of 77 maleimide derivates inside the PK glycogen synthase kinase 3 beta (GSK3β) using docking experiments. We found that the orientations that these compounds adopt inside GSK3β binding site prioritize the formation of hydrogen bond (HB) interactions between the maleimide group and the residues at the hinge region (residues Val135 and Asp133), and adopt propeller-like conformations (where the maleimide is the propeller axis and the heterocyclic substituents are two slanted blades). In addition, quantitative structure-activity relationship (QSAR) models using CoMSIA methodology were constructed to explain the trend of the GSK3β inhibitory activities for the studied compounds. We found a model to explain the structure-activity relationship of non-cyclic maleimide (NCM) derivatives (54 compounds). The best CoMSIA model (training set included 44 compounds) included steric, hydrophobic, and HB donor fields and had a good Q(2) value of 0.539. It also predicted adequately the most active compounds contained in the test set. Furthermore, the analysis of the plots of the steric CoMSIA field describes the elements involved in the differential potency of the inhibitors that can be considered for the selection of suitable inhibitors. PMID:25010341

  8. Structure-activity relationships of dibenzoylhydrazines for the inhibition of P-glycoprotein-mediated quinidine transport.

    PubMed

    Miyata, Ken-Ichi; Nakagawa, Yoshiaki; Kimura, Yasuhisa; Ueda, Kazumitsu; Akamatsu, Miki

    2016-07-15

    We previously demonstrated that dibenzoylhydrazines (DBHs) are not only P-glycoprotein (P-gp) substrates, but also inhibitors. In the present study, we evaluated the inhibition of P-gp-mediated quinidine transport by two series of DBHs and performed a classical QSAR analysis and docking simulation in order to investigate the mechanisms underlying P-gp substrate/inhibitor recognition. The results of the QSAR analysis identified the hydrophobic factor as the most important for inhibitory activities, while electronic and steric effects also influenced the activities. The different substituent effects observed in each series suggested the different binding modes of each series of DBHs, which was supported by the results of the docking simulation. PMID:27262425

  9. GTM-Based QSAR Models and Their Applicability Domains.

    PubMed

    Gaspar, H A; Baskin, I I; Marcou, G; Horvath, D; Varnek, A

    2015-06-01

    In this paper we demonstrate that Generative Topographic Mapping (GTM), a machine learning method traditionally used for data visualisation, can be efficiently applied to QSAR modelling using probability distribution functions (PDF) computed in the latent 2-dimensional space. Several different scenarios of the activity assessment were considered: (i) the "activity landscape" approach based on direct use of PDF, (ii) QSAR models involving GTM-generated on descriptors derived from PDF, and, (iii) the k-Nearest Neighbours approach in 2D latent space. Benchmarking calculations were performed on five different datasets: stability constants of metal cations Ca(2+) , Gd(3+) and Lu(3+) complexes with organic ligands in water, aqueous solubility and activity of thrombin inhibitors. It has been shown that the performance of GTM-based regression models is similar to that obtained with some popular machine-learning methods (random forest, k-NN, M5P regression tree and PLS) and ISIDA fragment descriptors. By comparing GTM activity landscapes built both on predicted and experimental activities, we may visually assess the model's performance and identify the areas in the chemical space corresponding to reliable predictions. The applicability domain used in this work is based on data likelihood. Its application has significantly improved the model performances for 4 out of 5 datasets. PMID:27490381

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

    PubMed

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

    2016-05-17

    New energetic compounds are designed to minimize their potential environmental impacts, which includes their transformation and the fate and effects of their transformation products. The nitro groups of energetic compounds are readily reduced to amines, and the resulting aromatic amines are subject to oxidation and coupling reactions. Manganese dioxide (MnO2) is a common environmental oxidant and model system for kinetic studies of aromatic amine oxidation. In this study, a training set of new and previously reported kinetic data for the oxidation of model and energetic-derived aromatic amines was assembled and subjected to correlation analysis against descriptor variables that ranged from general purpose [Hammett σ constants (σ(-)), pKas of the amines, and energies of the highest occupied molecular orbital (EHOMO)] to specific for the likely rate-limiting step [one-electron oxidation potentials (Eox)]. The selection of calculated descriptors (pKa, EHOMO, and Eox) was based on validation with experimental data. All of the correlations gave satisfactory quantitative structure-activity relationships (QSARs), but they improved with the specificity of the descriptor. The scope of correlation analysis was extended beyond MnO2 to include literature data on aromatic amine oxidation by other environmentally relevant oxidants (ozone, chlorine dioxide, and phosphate and carbonate radicals) by correlating relative rate constants (normalized to 4-chloroaniline) to EHOMO (calculated with a modest level of theory). PMID:27074054

  11. Trainable structure-activity relationship model for virtual screening of CYP3A4 inhibition.

    PubMed

    Didziapetris, Remigijus; Dapkunas, Justas; Sazonovas, Andrius; Japertas, Pranas

    2010-11-01

    A new structure-activity relationship model predicting the probability for a compound to inhibit human cytochrome P450 3A4 has been developed using data for >800 compounds from various literature sources and tested on PubChem screening data. Novel GALAS (Global, Adjusted Locally According to Similarity) modeling methodology has been used, which is a combination of baseline global QSAR model and local similarity based corrections. GALAS modeling method allows forecasting the reliability of prediction thus defining the model applicability domain. For compounds within this domain the statistical results of the final model approach the data consistency between experimental data from literature and PubChem datasets with the overall accuracy of 89%. However, the original model is applicable only for less than a half of PubChem database. Since the similarity correction procedure of GALAS modeling method allows straightforward model training, the possibility to expand the applicability domain has been investigated. Experimental data from PubChem dataset served as an example of in-house high-throughput screening data. The model successfully adapted itself to both data classified using the same and different IC₅₀ threshold compared with the training set. In addition, adjustment of the CYP3A4 inhibition model to compounds with a novel chemical scaffold has been demonstrated. The reported GALAS model is proposed as a useful tool for virtual screening of compounds for possible drug-drug interactions even prior to the actual synthesis. PMID:20814717

  12. Quantitative structure-activity relationships for the toxicity of nitrobenzenes to Tetrahymena thermophila.

    PubMed

    Xu, Jing-Bo; Jing, Ti-Song; Pauli, W; Berger, S

    2002-01-01

    In this study IGC50 (50% inhibitory growth concentration) values of 26 nitrobenzenes were determined for population growth endpoint of Tetrahymena thermophila. The toxicity order of the observed compounds has been found as follows: dinitro compounds > mono-nitro compounds; dichloronitrobenzenes > monochloronitrobenzenes; and meta-substituted nitrobenzenes > ortho-/para-substituted nitrobenzenes (NT, NPh, NAnis) except for the dinitrobenzenes and nitroanilines (DNB, NAn). Quantitative structure activity relationships (QSARs) were developed using log of the inverse of the IGC50 (logIGC50(-1)) in mole liter as the dependent variable and six molecular descriptors--logP, 1X(V), I, K alpha, sigma sigma- and E(LUMO) as the independent variables. Through multiplicate regression analysis, one best equation was obtained: log IGC50(-1) = 2.93 + 0.830sigma sigma- + 0.350I, n = 26, r = 0.923, r2 = 0.852, s = 0.265, f = 66.4 The equation was used to estimate IGC50 for seven analogues. PMID:12046656

  13. Computational insight into the structure-activity relationship of novel N-substituted phthalimides with gibberellin-like activity.

    PubMed

    Li, Dongling; Du, Shaoqing; Tan, Weiming; Duan, Hongxia

    2015-10-01

    N-substituted phthalimides (NSPs) that show multiple gibberellin (GA)-like effects on the growth and development of higher plants have been reported. These NSPs may represent a potential alternative to commercial GAs. Therefore, in this work, molecular docking and molecular dynamics simulations were used to explore the mode of interaction between some NSPs and the GA receptor GID1A in order to clarify the relationship between structure and GA-like activity in the NSPs. The results obtained demonstrate that both a multiple-hydrogen-bond network and a "hat-shaped" hydrophobic interaction play important roles in the binding of the NSPs to GID1A. The carbonyl group of a phthalimide fragment in the NSPs acted in a similar manner to the pharmacophore group 6-COOH in GAs, forming multiple-hydrogen-bond interactions with residues Ser191 and Tyr322 in the binding domain of GID1A. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were used to further study the 3D quantitative structure-activity relationship (3D-QSAR) of the NSPs. It was confirmed that the GA-like activity of these NSPs is strongly linked to a few H-bond donor and acceptor field contributions of the NSPs to the H-bond interactions with GID1A. Five new NSP molecules D1-D5 were designed using the binding domain of GID1A and then docked into the receptor. D1 and D4 were shown to have good docking scores due to enhanced hydrophobic contact. We hope that these results will provide useful guidance in the rational design of new NSPs. PMID:26412055

  14. The use of quantitative structure-activity relationship models to develop optimized processes for the removal of tobacco host cell proteins during biopharmaceutical production.

    PubMed

    Buyel, J F; Woo, J A; Cramer, S M; Fischer, R

    2013-12-27

    The production of recombinant pharmaceutical proteins in plants benefits from the low cost of upstream production and the greater scalability of plants compared to fermenter-based systems. Now that manufacturing processes that comply with current good manufacturing practices have been developed, plants can compete with established platforms on equal terms. However, the costs of downstream processing remain high, in part because of the dedicated process steps required to remove plant-specific process-related impurities. We therefore investigated whether the ideal strategy for the chromatographic removal of tobacco host cell proteins can be predicted by quantitative structure-activity relationship (QSAR) modeling to reduce the process development time and overall costs. We identified more than 100 tobacco proteins by mass spectrometry and their structures were reconstructed from X-ray crystallography, nuclear magnetic resonance spectroscopy and/or homology modeling data. The resulting three-dimensional models were used to calculate protein descriptors, and significant descriptors were selected based on recently-published retention data for model proteins to develop QSAR models for protein retention on anion, cation and mixed-mode resins. The predicted protein retention profiles were compared with experimental results using crude tobacco protein extracts. Because of the generic nature of the method, it can easily be transferred to other expression systems such as mammalian cells. The quality of the models and potential improvements are discussed. PMID:24268820

  15. Molecular determinants of thyroid hormone receptor selectivity in a series of phosphonic acid derivatives: 3D-QSAR analysis and molecular docking.

    PubMed

    Wang, Fang-Fang; Yang, Wei; Shi, Yong-Hui; Le, Guo-Wei

    2015-10-01

    A mathematical study was performed on a set of phosphonic acid derivatives that are substrates for thyroid hormone receptor β (TRβ) and thyroid hormone receptor α (TRα), three-dimensional quantitative structure-activity relationship (3D-QSAR) models using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methods were employed to investigate the structural requirements for this series of compounds with improved activity. Some descriptors were also employed to significantly improve the performance of the derived models. The CoMFA model for TRβ exhibited Rcv(2) of 0.612, Rpred(2) of 0.7218, whereas CoMSIA model showed Rcv(2) of 0.621, R(2)pred of 0.7358; the CoMFA model for TRα displayed Rcv(2) of 0.678, Rpred(2) of 0.6424, and the CoMSIA model had Rcv(2) of 0.671, Rpred(2) of 0.6932, which indicate that the constructed models are statistically significant. The derived contour maps further pointed out the regions where interactive fields may influence the activity. In order to validate the QSAR models and explore the origin of the selectivity at the amino acid level, molecular docking was developed, and the results indicate that Arg282, Arg320, Asn331, Gly332, Thr329 and His435 for TRβ, but Ala225, Arg228, Met259, Arg262 and His381 for TRα, respectively are important residues. The information obtained from the QSAR models can be used in the design of more potent TR agonists. PMID:26363198

  16. Homology modeling and QSAR analysis of 1,3,4-thiadiazole and 1,3,4-triazole derivatives as carbonic anhydrase inhibitors.

    PubMed

    Akula, N V Murali Krishna; Kumar, Surendra; Singh, Vineet; Tiwari, Meena

    2010-08-01

    Carbonic anhydrase (CA) inhibitors are very interesting target for designing anticancer (hypoxic) and antiglaucoma drugs. In the present study, a 3D homology modeling of human carbonic anhydrase-IX (hCA-IX) isozyme, based upon the crystal structure of murine CA-XIVA (PDB CODE 1RJ5) was performed, as no experimental 3D structures are available. A homology model of hCA-IX was developed and validated. To explore the responsible physicochemical properties of 1,3,4-thiadiazole and 1,3,4-triazole derivatives for carbonic anhydrase inhibition, a quantitative structure activity relationship (QSAR) study was performed having hCA-II and hCA-IX inhibitory activity respectively. In hCA-II and hCA-IX inhibitory activities, four significant models with good correlations (> or = 0.945 & > or = 0.926) were obtained; two models (models 1 and 3) were selected based on statistical criterion. The QSAR study revealed that in case of hCA-II, overall increase in size and volume of molecule, introduction of electropositive surfaces might increase the inhibitory activity, whereas in case of hCA-IX, decreasing the hydrophobicity and introduction of electron releasing substituents might increase the hCA-IX inhibitory activity. PMID:21174951

  17. Combating the threat of anthrax: a quantitative structure-activity relationship approach.

    PubMed

    Verma, Rajeshwar P; Hansch, Corwin

    2008-01-01

    Bacterial agents or products more likely to be used as biological weapons of mass destruction are Bacillus anthracis, Francisella tularensis, Yersinia pestis, and the neurotoxin of Clostridium botulinum. Anthrax is an acute infectious disease with a high mortality rate caused by Bacillus anthracis, reinforcing the need for better adjunctive therapy and prevention strategies. In this paper, we developed 7 QSAR models on penicillin-based inhibitors of the class A and B beta-lactamases from B. anthracis and inhibitors of anthrax lethal factor to understand the chemical-biological interactions. Hydrophobic and steric factors are found to be the most important determinants of the activity. Internal (cross-validation ( q (2)), quality factor ( Q), Fischer statistics ( F), and Y-randomization) and external validation tests have validated all the QSAR models. PMID:18611038

  18. Relationships between solar activity and climate change

    NASA Technical Reports Server (NTRS)

    Roberts, W. O.

    1975-01-01

    The relationship between recurrent droughts in the High Plains of the United States and the double sunspot cycle is discussed in detail. It is suggested that high solar activity is generally related to an increase in meridional circulation and blocking patterns at high and intermediate latitudes, especially in winter, and the effect is related to the sudden formation of cirrus clouds during strong geomagnetic activity that originates in the solar corpuscular emission.

  19. Virtual Screening and Molecular Design Based on Hierarchical Qsar Technology

    NASA Astrophysics Data System (ADS)

    Kuz'min, Victor E.; Artemenko, A. G.; Muratov, Eugene N.; Polischuk, P. G.; Ognichenko, L. N.; Liahovsky, A. V.; Hromov, A. I.; Varlamova, E. V.

    This chapter is devoted to the hierarchical QSAR technology (HiT QSAR) based on simplex representation of molecular structure (SiRMS) and its application to different QSAR/QSPR tasks. The essence of this technology is a sequential solution (with the use of the information obtained on the previous steps) of the QSAR paradigm by a series of enhanced models based on molecular structure description (in a specific order from 1D to 4D). Actually, it's a system of permanently improved solutions. Different approaches for domain applicability estimation are implemented in HiT QSAR. In the SiRMS approach every molecule is represented as a system of different simplexes (tetratomic fragments with fixed composition, structure, chirality, and symmetry). The level of simplex descriptors detailed increases consecutively from the 1D to 4D representation of the molecular structure. The advantages of the approach presented are an ability to solve QSAR/QSPR tasks for mixtures of compounds, the absence of the "molecular alignment" problem, consideration of different physical-chemical properties of atoms (e.g., charge, lipophilicity), and the high adequacy and good interpretability of obtained models and clear ways for molecular design. The efficiency of HiT QSAR was demonstrated by its comparison with the most popular modern QSAR approaches on two representative examination sets. The examples of successful application of the HiT QSAR for various QSAR/QSPR investigations on the different levels (1D-4D) of the molecular structure description are also highlighted. The reliability of developed QSAR models as the predictive virtual screening tools and their ability to serve as the basis of directed drug design was validated by subsequent synthetic, biological, etc. experiments. The HiT QSAR is realized as the suite of computer programs termed the "HiT QSAR" software that so includes powerful statistical capabilities and a number of useful utilities.

  20. 3D-QSAR (CoMFA and CoMSIA) and pharmacophore (GALAHAD) studies on the differential inhibition of aldose reductase by flavonoid compounds.

    PubMed

    Caballero, Julio

    2010-11-01

    Inhibitory activities of flavonoid derivatives against aldose reductase (AR) enzyme were modelled by using CoMFA, CoMSIA and GALAHAD methods. CoMFA and CoMSIA methods were used for deriving quantitative structure-activity relationship (QSAR) models. All QSAR models were trained with 55 compounds, after which they were evaluated for predictive ability with additional 14 compounds. The best CoMFA model included both steric and electrostatic fields, meanwhile, the best CoMSIA model included steric, hydrophobic and H-bond acceptor fields. These models had a good predictive quality according to both internal and external validation criteria. On the other hand, GALAHAD was used for deriving a 3D pharmacophore model. Twelve active compounds were used for deriving this model. The obtained model included hydrophobe, hydrogen bond acceptor and hydrogen bond donor features; it was able to identify the active AR inhibitors from the remaining compounds. These in silico tools might be useful in the rational design of new AR inhibitors. PMID:20863730

  1. Building up a QSAR model for toxicity toward Tetrahymena pyriformis by the Monte Carlo method: A case of benzene derivatives.

    PubMed

    Toropova, Alla P; Schultz, Terry W; Toropov, Andrey A

    2016-03-01

    Data on toxicity toward Tetrahymena pyriformis is indicator of applicability of a substance in ecologic and pharmaceutical aspects. Quantitative structure-activity relationships (QSARs) between the molecular structure of benzene derivatives and toxicity toward T. pyriformis (expressed as the negative logarithms of the population growth inhibition dose, mmol/L) are established. The available data were randomly distributed three times into the visible training and calibration sets, and invisible validation sets. The statistical characteristics for the validation set are the following: r(2)=0.8179 and s=0.338 (first distribution); r(2)=0.8682 and s=0.341 (second distribution); r(2)=0.8435 and s=0.323 (third distribution). These models are built up using only information on the molecular structure: no data on physicochemical parameters, 3D features of the molecular structure and quantum mechanics descriptors are involved in the modeling process. PMID:26851376

  2. Development of docking-based 3D QSAR models for the design of substituted quinoline derivatives as human dihydroorotate dehydrogenase (hDHODH) inhibitors.

    PubMed

    Vyas, V K; Ghate, M

    2013-08-01

    This study has investigated docking-based 3D quantitative structure-activity relationships (QSARs) for a range of quinoline carboxylic acid derivatives by comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). A docking study has shown that most of the compounds formed H-bonds with Arg136 and Gln47, which have already been shown to be essential for the binding of ligands at the active site of the hydroorotate dehydrogenase adenovirus (hDHODH). Bioactive conformations of all the molecules obtained from the docking study were used for the 3D QSAR study. The best CoMFA and CoMSIA models were obtained for the training set and were found to be statistically significant, with cross-validated coefficients (q²) of 0.672 and 0.613, r² cv of 0.635 and 0.598 and coefficients of determination (r²) of 0.963 and 0.896, respectively. Both models were validated by a test set of 15 compounds, giving satisfactory predicted correlation coefficients (r² pred) of 0.824 and 0.793 for the CoMFA and CoMSIA models, respectively. From the docking-based 3D QSAR study we designed 34 novel quinoline-based compounds and performed structure-based virtual screening. Finally, in silico pharmacokinetics and toxicities were predicted for 24 of the best docked molecules. The study provides valuable information for the understanding of interactions between hDHODH and the novel compounds. PMID:23714018

  3. Mechanistic QSAR models for interpreting degradation rates of sulfonamides in UV-photocatalysis systems.

    PubMed

    Huang, Xiangfeng; Feng, Yi; Hu, Cui; Xiao, Xiaoyu; Yu, Daliang; Zou, Xiaoming

    2015-11-01

    Photocatalysis is one of the most effective methods for treating antibiotic wastewater. Thus, it is of great significance to determine the relationship between degradation rates and structural characteristics of antibiotics in photocatalysis processes. In the present study, the photocatalytic degradation characteristics of 10 sulfonamides (SAs) were studied using two photocatalytic systems composed of nanophase titanium dioxide (nTiO2) plus ultraviolet (UV) and nTiO2/activated carbon fiber (ACF) plus UV. The results indicated that the largest apparent SA degradation rate constant (Kapp) is approximately 5 times as large as that of the smallest one. Based on the degradation mechanism and the partial least squares regression (PLS) method, optimum Quantitative Structure Activity Relationship (QSAR) models were developed for the two systems. Mechanistic models indicated that the degradation rule of SAs in the TiO2 systems strongly relates to their highest occupied molecular orbital (Ehomo), the maximum values of nucleophilic attack (f(+)x), and the minimum values of the most negative partial charge on a main-chain atom (q(C)min), whereas the maximum values of OH radical attack (f(0)x) and the apparent adsorption rate constant values (kad) are key factors affecting the degradation rule of SAs in the TiO2/ACF system. PMID:26070083

  4. QSAR study of the toxicity of nitrobenzenes to river bacteria and photobacterium phosphoreum

    SciTech Connect

    Yuan, X.; Lu, G.; Lang, P.

    1997-01-01

    Since nitrobenzenes constitute a class of industrial chemicals that are present in Songhua River and probably in many other industrialized countries as well, it is useful to gain insight into their potential hazard to aquatic organisms. For this reason, it was decided to determine data on the toxicity for bacteria in the Songhua River. Furthermore, the toxicity to Ph. phosphoreum was determined in the Microtox assay, in order to further evaluate the usefulness of this assay for hazard assessment. Quantitative structure-activity relationships (QSARs) have been developed for aromatic nitro compound toxicity to aquatic species, but no data on the toxicity of nitrobenzenes to environmental bacteria were used. In this study, the toxicity of various substituted nitrobenzenes to bacteria in Songhua River and to Ph. phosphoreum has been investigated, establishing quantitative structure-activity relationships with n-octanol-water partition coefficient (log P), the energy of the lowest unoccupied molecular orbital (E{sub LUMO}) and the sum of substituent constant ({Sigma}{sigma}-). 12 refs., 2 tabs.

  5. Structure—activity relationships for insecticidal carbamates*

    PubMed Central

    Metcalf, Robert L.

    1971-01-01

    Carbamate insecticides are biologically active because of their structural complementarity to the active site of acetylcholinesterase (AChE) and their consequent action as substrates with very low turnover numbers. Carbamates behave as synthetic neurohormones that produce their toxic action by interrupting the normal action of AChE so that acetylcholine accumulates at synaptic junctions. The necessary properties for a suitable insecticidal carbamate are lipid solubility, suitable structural complementarity to AChE, and sufficient stability to multifunction-oxidase detoxification. The relationships between the structure and the activity of a large number of synthetic carbamates are analysed in detail, with particular attention to the second of these properties. PMID:5315358

  6. 3D-QSAR study of tetrahydro-3H-imidazo[4,5-c]pyridine derivatives as VEGFR-2 kinase inhibitors using various charge schemes.

    PubMed

    Balupuri, Anand; Balasubramanian, Pavithra K; Cho, Seung Joo

    2015-08-01

    Vascular endothelial growth factor-2 receptor (VEGFR-2) kinase is a promising target for the development of novel anticancer drugs. Three-dimensional quantitative structure-activity relationship (3D-QSAR) study was performed on a series of tetrahydro-3H-imidazo[4,5-c]pyridine derivatives to understand the structural basis for VEGFR-2 inhibitory activity. Several 3D-QSAR models were developed using various partial atomic charge schemes. Comparative molecular field analysis (CoMFA) and Comparative molecular similarity indices analysis (CoMSIA) methods were employed to derive these models. The CoMFA models performed better than the CoMSIA models. The reliable CoMFA model was obtained with the Gasteiger-Marsili charge scheme. The model produced statistically significant results with a cross-validated correlation coefficient (q(2)) of 0.635 and a coefficient of determination (r(2)) of 0.930. The model showed reasonable predictive power with predictive correlation coefficient ([Formula: see text]) of 0.582. Robustness of the model was further checked by leave-five-out cross-validation, bootstrapping and progressive scrambling analysis. The model was found to be statistically robust and expected to assist in the design of novel compounds with enhanced VEGFR-2 inhibitory activity. PMID:25874606

  7. Combined 3D-QSAR modeling and molecular docking study on azacycles CCR5 antagonists

    NASA Astrophysics Data System (ADS)

    Ji, Yongjun; Shu, Mao; Lin, Yong; Wang, Yuanqiang; Wang, Rui; Hu, Yong; Lin, Zhihua

    2013-08-01

    The beta chemokine receptor 5 (CCR5) is an attractive target for pharmaceutical industry in the HIV-1, inflammation and cancer therapeutic areas. In this study, we have developed quantitative structure activity relationship (QSAR) models for a series of 41 azacycles CCR5 antagonists using comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), and Topomer CoMFA methods. The cross-validated coefficient q2 values of 3D-QASR (CoMFA, CoMSIA, and Topomer CoMFA) methods were 0.630, 0.758, and 0.852, respectively, the non-cross-validated R2 values were 0.979, 0.978, and 0.990, respectively. Docking studies were also employed to determine the most probable binding mode. 3D contour maps and docking results suggested that bulky groups and electron-withdrawing groups on the core part would decrease antiviral activity. Furthermore, docking results indicated that H-bonds and π bonds were favorable for antiviral activities. Finally, a set of novel derivatives with predicted activities were designed.

  8. 3D-QSAR and Docking Studies of Pyrido[2,3-d]pyrimidine Derivatives as Wee1 Inhibitors

    NASA Astrophysics Data System (ADS)

    Zeng, Guo-hua; Wu, Wen-juan; Zhang, Rong; Sun, Jun; Xie, Wen-guo; Shen, Yong

    2012-06-01

    In order to investigate the inhibiting mechanism and obtain some helpful information for designing functional inhibitors against Wee1, three-dimensional quantitative structure-activity relationship (3D-QSAR) and docking studies have been performed on 45 pyrido[2,3-d] pyrimidine derivatives acting as Wee1 inhibitors. Two optimal 3D-QSAR models with significant statistical quality and satisfactory predictive ability were established, including the CoMFA model (q2=0.707, R2=0.964) and CoMSIA model (q2=0.645, R2=0.972). The external validation indicated that both CoMFA and CoMSIA models were quite robust and had high predictive power with the predictive correlation coefficient values of 0.707 and 0.794, essential parameter rm2 values of 0.792 and 0.826, the leave-one-out r2m(LOO) values of 0.781 and 0.809, r2m(overall) values of 0.787 and 0.810, respectively. Moreover, the appropriate binding orientations and conformations of these compounds interacting with Wee1 were revealed by the docking studies. Based on the CoMFA and CoMSIA contour maps and docking analyses, several key structural requirements of these compounds responsible for inhibitory activity were identified as follows: simultaneously introducing high electropositive groups to the substituents R1 and R5 may increase the activity, the substituent R2 should be smaller bulky and higher electronegative, moderate-size and strong electron-withdrawing groups for the substituent R3 is advantageous to the activity, but the substituent X should be medium-size and hydrophilic. These theoretical results help to understand the action mechanism and design novel potential Wee1 inhibitors.

  9. Anti-hepatocellular carcinoma activity using human HepG2 cells and hepatotoxicity of 6-substituted methyl 3-aminothieno[3,2-b]pyridine-2-carboxylate derivatives: in vitro evaluation, cell cycle analysis and QSAR studies.

    PubMed

    Abreu, Rui M V; Ferreira, Isabel C F R; Calhelha, Ricardo C; Lima, Raquel T; Vasconcelos, M Helena; Adega, Filomena; Chaves, Raquel; Queiroz, Maria-João R P

    2011-12-01

    Hepatocellular carcinoma (HCC) is a highly complex cancer, resistant to commonly used treatments and new therapeutic agents are urgently needed. A total of thirty-two thieno[3,2-b]pyridine derivatives of two series: methyl 3-amino-6-(hetero)arylthieno[3,2-b]pyridine-2-carboxylates (1a-1t) and methyl 3-amino-6-[(hetero)arylethynyl]thieno[3,2-b]pyridine-2-carboxylates (2a-2n), previously prepared by some of us, were evaluated as new potential anti-HCC agents by studying their in vitro cell growth inhibition on human HepG2 cells and hepatotoxicity using a porcine liver primary cell culture (PLP1). The presence of amino groups linked to a benzene moiety emerges as the key element for the anti-HCC activity. The methyl 3-amino-6-[(3-aminophenyl)ethynyl]thieno[3,2-b]pyridine-2-carboxylate (2f) is the most potent compound presenting GI(50) values on HepG2 cells of 1.2 μM compared to 2.9 μM of the positive control ellipticine, with no observed hepatotoxicity (PLP1 GI(50) > 125 μM against 3.3 μM of ellipticine). Moreover this compound changes the cell cycle profile of the HepG2 cells, causing a decrease in the % of cells in the S phase and a cell cycle arrest in the G2/M phase. QSAR studies were also performed and the correlations obtained using molecular and 1D descriptors revealed the importance of the presence of amino groups and hydrogen bond donors for anti-HCC activity, and hydrogen bond acceptors for hepatotoxicity. The best correlations were obtained with 3D descriptors belonging to different subcategories for anti-HCC activity and hepatotoxicity, respectively. These results point to different molecular mechanisms of action of the compounds in anti-HCC activity and hepatotoxicity. This work presents some promising thieno[3,2-b]pyridine derivatives for potential use in the therapy of HCC. These compounds can also be used as scaffolds for further synthesis of more potent analogs. PMID:22014996

  10. 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). PMID:25786522

  11. Structure-guided unravelling: Phenolic hydroxyls contribute to reduction of acrylamide using multiplex quantitative structure-activity relationship modelling.

    PubMed

    Zhang, Yu; Huang, Mengmeng; Wang, Qiao; Cheng, Jun

    2016-05-15

    We reported a structure-activity relationship study on unravelling phenolic hydroxyls instead of alcoholic hydroxyls contribute to the reduction of acrylamide formation by flavonoids. The dose-dependent study shows a close correlation between the number of phenolic hydroxyls of flavonoids and their reduction effects. In view of positions of hydroxyls, the 3',4'(ortho)-dihydroxyls in B cycle, 3-hydroxyl or hydroxyls of 3-gallate in C cycle, and 5,7(meta)-dihydroxyls in A cycle of flavonoid structures play an important role in the reduction of acrylamide. Flavone C-glycosides are more effective at reducing the formation of acrylamide than flavone O-glycosides when sharing the same aglycone. The current multiplex quantitative structure-activity relationship (QSAR) equations effectively predict the inhibitory rates of acrylamide using selected chemometric parameters (R(2): 0.835-0.938). This pioneer study opens a broad understanding on the chemoprevention of acrylamide contaminants on a structural basis. PMID:26776000

  12. Electron-correlation based externally predictive QSARs for mutagenicity of nitrated-PAHs in Salmonella typhimurium TA100.

    PubMed

    Reenu; Vikas

    2014-03-01

    In quantitative modeling, there are two major aspects that decide reliability and real external predictivity of a structure-activity relationship (SAR) based on quantum chemical descriptors. First, the information encoded in employed molecular descriptors, computed through a quantum-mechanical method, should be precisely estimated. The accuracy of the quantum-mechanical method, however, is dependent upon the amount of electron-correlation it incorporates. Second, the real external predictivity of a developed quantitative SAR (QSAR) should be validated employing an external prediction set. In this work, to analyze the role of electron-correlation, QSAR models are developed for a set of 51 ubiquitous pollutants, namely, nitrated monocyclic and polycyclic aromatic hydrocarbons (nitrated-AHs and PAHs) having mutagenic activity in TA100 strain of Salmonella typhimurium. The quality of the models, through state-of-the-art external validation procedures employing an external prediction set, is compared to the best models known in the literature for mutagenicity. The molecular descriptors whose electron-correlation contribution is analyzed include total energy, energy of HOMO and LUMO, and commonly employed electron-density based descriptors such as chemical hardness, chemical softness, absolute electronegativity and electrophilicity index. The electron-correlation based QSARs are also compared with those developed using quantum-mechanical descriptors computed with advanced semi-empirical (SE) methods such as PM6, PM7, RM1, and ab initio methods, namely, the Hartree-Fock (HF) and the density functional theory (DFT). The models, developed using electron-correlation contribution of the quantum-mechanical descriptors, are found to be not only reliable but also satisfactorily predictive when compared to the existing robust models. The robustness of the models based on descriptors computed through advanced SE methods, is also observed to be comparable to those developed with

  13. QSAR analyses of DDT analogues and their in silico validation using molecular docking study against voltage-gated sodium channel of Anopheles funestus.

    PubMed

    Saini, V; Kumar, A

    2014-01-01

    DDT has enjoyed the reputation of a successful pesticide in disease control programme and agricultural practices along with the serious opposition and ban later on due to its biomagnification and toxic action against non-target organisms. The present work was carried out to develop QSAR models for analysing DDT analogues for their pesticidal activity and in silico validation of these models. A 2D-QSAR model was generated using stepwise with multiple regression, and the model with a value of r(2) = 0.7324; q(2) = 0.6215; pred r(2) = 0.7038, containing five descriptors, was selected for further study. The 3D QSAR with CoMFA analysis showed that steric contribution of 21% and electrostatic contribution of about 79% were required for larvicidal activity of DDT analogues. A set of 3430 molecules was generated using the basic DDT skeleton as template, and these were evaluated for their mosquito larvicidal activity using the generated QSAR models and DDT as standard. Eleven molecules were selected for in silico validation of these models. For this, a docking study of the selected molecules against the homology-modelled voltage-gated sodium channel of Anopheles funestus was conducted. The study showed that the activities of these analogues as predicted by 2D-QSAR, 3D-QSAR with CoMFA and dock score were observed to be well correlated. PMID:25271473

  14. Structure-activity relationship of anthelmintic cyclooctadepsipeptides.

    PubMed

    Ohyama, Makoto; Okada, Yumiko; Takahashi, Masaaki; Sakanaka, Osamu; Matsumoto, Maki; Atsumi, Kunio

    2011-01-01

    The relationship between cyclooctadepsipeptides and their anthelmintic efficacy was examined by converting the natural products, PF1022A, PF1022E and PF1022H. Some analogues substituted at the para position of the phenyllactate moiety showed higher or equivalent activity against the parasitic nematode, Ascaridia galli in chicken when compared with the parent compounds. It is suggested that lipophilicity and the polar surface area, in addition to structural requirements of the derivatives, influenced the anthelmintic efficacy in vivo. PMID:21737929

  15. QSAR and pharmacophore modeling of natural and synthetic antimalarial prodiginines.

    PubMed

    Singh, Baljinder; Vishwakarma, Ram A; Bharate, Sandip B

    2013-09-01

    Prodiginines are a family of linear and cyclic oligopyrrole red-pigmented compounds possessing antibacterial, anticancer and immunosuppressive activities and are produced by actinomycetes and other eubacteria. Recently, prodiginines have been reported to possess potent in vitro as well as in vivo antimalarial activity against chloroquine sensitive D6 and multi-drug resistant Dd2 strains of Plasmodium falciparum. In the present paper, a QSAR and pharmacophore modeling for a series of natural and synthetic prodiginines was performed to find out structural features which are crucial for antimalarial activity against these D6 and Dd2 Plasmodium strains. The study indicated that inertia moment 2 length, Kier Chi6 (path) index, kappa 3 index and Wiener topological index plays important role in antimalarial activity against D6 strain whereas descriptors inertia moment 2 length, ADME H-bond donors, VAMP polarization XX component and VAMP quadpole XZ component play important role in antimalarial activity against Dd2 strain. Furthermore, a five-point pharmacophore (ADHRR) model with one H-bond acceptor (A), one H-bond donor (D), one hydrophobic group (H) and two aromatic rings (R) as pharmacophore features was developed for D6 strain by PHASE module of Schrodinger suite. Similarly a six-point pharmacophore AADDRR was developed for Dd2 strain activity. All developed QSAR models showed good correlation coefficient (r² > 0.7), higher F value (F >20) and excellent predictive power (Q² > 0.6). Developed models will be highly useful for predicting antimalarial activity of new compounds and could help in designing better molecules with enhanced antimalarial activity. Furthermore, calculated ADME properties indicated drug-likeness of prodiginines. PMID:24010933

  16. Inhibition of /sup 125/I-labeled ristocetin binding to Micrococcus luteus cells by the peptides related to bacterial cell wall mucopeptide precursors: quantitative structure-activity relationships

    SciTech Connect

    Kim, K.H.; Martin, Y.; Otis, E.; Mao, J.

    1989-01-01

    Quantitative structure-activity relationships (QSAR) of N-Ac amino acids, N-Ac dipeptides, and N-Ac tripeptides in inhibition of /sup 125/I-labeled ristocetin binding to Micrococcus luteus cell wall have been developed to probe the details of the binding between ristocetin and N-acetylated peptides. The correlation equations indicate that (1) the binding is stronger for peptides in which the side chain of the C-terminal amino acid has a large molar refractivity (MR) value, (2) the binding is weaker for peptides with polar than for those with nonpolar C-terminal side chains, (3) the N-terminal amino acid in N-Ac dipeptides contributes 12 times that of the C-terminal amino acid to binding affinity, and (4) the interactions between ristocetin and the N-terminal amino acid of N-acetyl tripeptides appear to be much weaker than those with the first two amino acids.

  17. QSAR studies on triazole derivatives as sglt inhibitors via CoMFA and CoMSIA

    NASA Astrophysics Data System (ADS)

    Zhi, Hui; Zheng, Junxia; Chang, Yiqun; Li, Qingguo; Liao, Guochao; Wang, Qi; Sun, Pinghua

    2015-10-01

    Forty-six sodium-dependent glucose cotransporters-2 (SGLT-2) inhibitors with hypoglycemic activity were selected to develop three-dimensional quantitative structure-activity relationship (3D-QSAR) using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models. A training set of 39 compounds were used to build up the models, which were then evaluated by a series of internal and external cross-validation techniques. A test set of 7 compounds was used for the external validation. The CoMFA model predicted a q2 value of 0.792 and an r2 value of 0.985. The best CoMSIA model predicted a q2 value of 0.633 and an r2 value of 0.895 based on a combination of steric, electrostatic, hydrophobic and hydrogen-bond acceptor effects. The predictive correlation coefficients of CoMFA and CoMSIA models were 0.872 and 0.839, respectively. The analysis of the contour maps from each model provided insight into the structural requirements for the development of more active sglt inhibitors, and on the basis of the models 8 new sglt inhibitors were designed and predicted.

  18. Synthesis, 3D-QSAR analysis and biological evaluation of quinoxaline 1,4-di-N-oxide derivatives as antituberculosis agents.

    PubMed

    Pan, Yuanhu; Li, Panpan; Xie, Shuyu; Tao, Yanfei; Chen, Dongmei; Dai, Menghong; Hao, Haihong; Huang, Lingli; Wang, Yulian; Wang, Liye; Liu, Zhenli; Yuan, Zonghui

    2016-08-15

    A series of quinoxaline 1,4-di-N-oxide derivatives variously substituted at C-2 position were synthesized and evaluated for in vitro antimycobacterial activity. Seventeen compounds exhibited potential activity (MIC ⩽6.25μg/mL) against Mycobacterium tuberculosis (H37Rv), in particular the compounds 3d and 3j having an MIC value of 0.39μg/mL. None of the compounds exhibited cytotoxicity when using an MTT assay in VERO cells. To further investigate the structure-activity relationship, CoMFA (q(2)=0.507, r(2)=0.923) and CoMSIA (q(2)=0.665, r(2)=0.977) models were performed on the basis of antimycobacterial activity data. The 3D-QSAR study of these compounds can provide useful information for further rational design of novel quinoxaline 1,4-di-N-oxides for treatment of tuberculosis. PMID:27426298

  19. Relationship between potential platelet activation and LCS

    NASA Astrophysics Data System (ADS)

    Shadden, Shawn

    2010-11-01

    In the study of blood flow, emphasis is often directed at understanding shear stress at the vessel wall due to its potentially disruptive influence on the endothelium. However, it is also known that shear stress has a potent effect on platelet activation. Platelet activation is a precursor for blood clotting, which in turn is the cause of most forms of death. Since most platelets are contained in the flow domain, it is important to consider stresses acting on the platelet as they are convected. Locations of high stress can correspond to boundaries between different dynamic regions and locations of hyperbolic points in the Eulerian sense. In the computation of LCS, strain in typically considered in the Lagrangian sense. In this talk we discuss the relationship between locations of potential platelet activation due to increased stress and locations of LCS marking increase Lagrangian deformation.

  20. New biaryl-chalcone derivatives of pregnenolone via Suzuki-Miyaura cross-coupling reaction. Synthesis, CYP17 hydroxylase inhibition activity, QSAR, and molecular docking study.

    PubMed

    Al-Masoudi, Najim A; Kadhim, Rawaa A; Abdul-Rida, Nabeel A; Saeed, Bahjat A; Engel, Matthias

    2015-09-01

    A new class of steroids is being synthesized for its ability to prevent intratumoral androgen production by inhibiting the activity of CYP17 hydroxylase enzyme. The scheme involved the synthesis of chalcone derivative of pregnenolone 5 which was further modified to the corresponding biaryl-chalcone pregnenolone analogs 16-25 using Suzuki-Miyaura cross-coupling reaction. The synthesized compounds were tested for activity using human CYP17α hydroxylase expressed in Escherichia coli. Compounds 21 was the most active inhibitor in this series, with IC50 values of 0.61μM and selectivity profile of 88.7% inhibition of hydroxylase enzyme. Molecular docking study of 21 was performed and showed the hydrogen bonds and hydrophobic interaction with the amino acid residues of the active site of CYP17. PMID:26051784

  1. 3D-QSAR studies on chromone derivatives as HIV-1 protease inhibitors

    NASA Astrophysics Data System (ADS)

    Ungwitayatorn, Jiraporn; Samee, Weerasak; Pimthon, Jutarat

    2004-02-01

    The three-dimensional quantitative structure-activity relationship (3D-QSAR) approach using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) was applied to a series of 30 chromone derivatives, a new class of HIV-1 protease inhibitors. The best predictive CoMFA model gives cross-validated r2 ( q2)=0.763, non-cross-validated r2=0.967, standard error of estimate ( S)=5.092, F=90.701. The best CoMSIA model has q2=0.707, non-cross-validated r2=0.943, S=7.018, F=51.734, included steric, electrostatic, hydrophobic, and hydrogen bond donor fields. The predictive ability of these models was validated by a set of five compounds that were not included in the training set. The calculated (predicted) and experimental inhibitory activities were well correlated. The contour maps obtained from CoMFA and CoMSIA models were in agreement with the previous docking study for this chromone series.

  2. 3D-QSAR-aided design, synthesis, in vitro and in vivo evaluation of dipeptidyl boronic acid proteasome inhibitors and mechanism studies.

    PubMed

    Lei, Meng; Feng, Huayun; Wang, Cheng; Li, Hailing; Shi, Jingmiao; Wang, Jia; Liu, Zhaogang; Chen, Shanshan; Hu, Shihe; Zhu, Yongqiang

    2016-06-01

    Proteasome had been clinically validated as an effective target for the treatment of cancers. Up to now, many structurally diverse proteasome inhibitors were discovered. And two of them were launched to treat multiple myeloma (MM) and mantle cell lymphoma (MCL). Based on our previous biological results of dipeptidyl boronic acid proteasome inhibitors, robust 3D-QSAR models were developed and structure-activity relationship (SAR) was summarized. Several structurally novel compounds were designed based on the theoretical models and finally synthesized. Biological results showed that compound 12e was as active as the standard bortezomib in enzymatic and cellular activities. In vivo pharmacokinetic profiles suggested compound 12e showed a long half-life, which indicated that it could be administered intravenously. Cell cycle analysis indicated that compound 12e inhibited cell cycle progression at the G2M stage. PMID:27117691

  3. Identification of putative estrogen receptor-mediated endocrine disrupting chemicals using QSAR- and structure-based virtual screening approaches

    SciTech Connect

    Zhang, Liying; Sedykh, Alexander; Tripathi, Ashutosh; Zhu, Hao; Afantitis, Antreas; Mouchlis, Varnavas D.; Melagraki, Georgia; Rusyn, Ivan; Tropsha, Alexander

    2013-10-01

    Identification of endocrine disrupting chemicals is one of the important goals of environmental chemical hazard screening. We report on the development of validated in silico predictors of chemicals likely to cause estrogen receptor (ER)-mediated endocrine disruption to facilitate their prioritization for future screening. A database of relative binding affinity of a large number of ERα and/or ERβ ligands was assembled (546 for ERα and 137 for ERβ). Both single-task learning (STL) and multi-task learning (MTL) continuous quantitative structure–activity relationship (QSAR) models were developed for predicting ligand binding affinity to ERα or ERβ. High predictive accuracy was achieved for ERα binding affinity (MTL R{sup 2} = 0.71, STL R{sup 2} = 0.73). For ERβ binding affinity, MTL models were significantly more predictive (R{sup 2} = 0.53, p < 0.05) than STL models. In addition, docking studies were performed on a set of ER agonists/antagonists (67 agonists and 39 antagonists for ERα, 48 agonists and 32 antagonists for ERβ, supplemented by putative decoys/non-binders) using the following ER structures (in complexes with respective ligands) retrieved from the Protein Data Bank: ERα agonist (PDB ID: 1L2I), ERα antagonist (PDB ID: 3DT3), ERβ agonist (PDB ID: 2NV7), and ERβ antagonist (PDB ID: 1L2J). We found that all four ER conformations discriminated their corresponding ligands from presumed non-binders. Finally, both QSAR models and ER structures were employed in parallel to virtually screen several large libraries of environmental chemicals to derive a ligand- and structure-based prioritized list of putative estrogenic compounds to be used for in vitro and in vivo experimental validation. - Highlights: • This is the largest curated dataset inclusive of ERα and β (the latter is unique). • New methodology that for the first time affords acceptable ERβ models. • A combination of QSAR and docking enables prediction of affinity and function.

  4. Desirability-based multiobjective optimization for global QSAR studies: application to the design of novel NSAIDs with improved analgesic, antiinflammatory, and ulcerogenic profiles.

    PubMed

    Cruz-Monteagudo, Maykel; Borges, Fernanda; Cordeiro, M Natália D S

    2008-11-15

    Up to now, very few reports have been published concerning the application of multiobjective optimization (MOOP) techniques to quantitative structure-activity relationship (QSAR) studies. However, none reports the optimization of objectives related directly to the desired pharmaceutical profile of the drug. In this work, for the first time, it is proposed a MOOP method based on Derringer's desirability function that allows conducting global QSAR studies considering simultaneously the pharmacological, pharmacokinetic and toxicological profile of a set of molecule candidates. The usefulness of the method is demonstrated by applying it to the simultaneous optimization of the analgesic, antiinflammatory, and ulcerogenic properties of a library of fifteen 3-(3-methylphenyl)-2-substituted amino-3H-quinazolin-4-one compounds. The levels of the predictor variables producing concurrently the best possible compromise between these properties is found and used to design a set of new optimized drug candidates. Our results also suggest the relevant role of the bulkiness of alkyl substituents on the C-2 position of the quinazoline ring over the ulcerogenic properties for this family of compounds. Finally, and most importantly, the desirability-based MOOP method proposed is a valuable tool and shall aid in the future rational design of novel successful drugs. PMID:18452123

  5. Investigation of Antigen-Antibody Interactions of Sulfonamides with a Monoclonal Antibody in a Fluorescence Polarization Immunoassay Using 3D-QSAR Models

    PubMed Central

    Wang, Zhanhui; Kai, Zhenpeng; Beier, Ross C.; Shen, Jianzhong; Yang, Xinling

    2012-01-01

    A three-dimensional quantitative structure-activity relationship (3D-QSAR) model of sulfonamide analogs binding a monoclonal antibody (MAbSMR) produced against sulfamerazine was carried out by Distance Comparison (DISCOtech), comparative molecular field analysis (CoMFA), and comparative molecular similarity indices analysis (CoMSIA). The affinities of the MAbSMR, expressed as Log10IC50, for 17 sulfonamide analogs were determined by competitive fluorescence polarization immunoassay (FPIA). The results demonstrated that the proposed pharmacophore model containing two hydrogen-bond acceptors, two hydrogen-bond donors and two hydrophobic centers characterized the structural features of the sulfonamides necessary for MAbSMR binding. Removal of two outliers from the initial set of 17 sulfonamide analogs improved the predictability of the models. The 3D-QSAR models of 15 sulfonamides based on CoMFA and CoMSIA resulted in q2 cv values of 0.600 and 0.523, and r2 values of 0.995 and 0.994, respectively, which indicates that both methods have significant predictive capability. Connolly surface analysis, which mainly focused on steric force fields, was performed to complement the results from CoMFA and CoMSIA. This novel study combining FPIA with pharmacophore modeling demonstrates that multidisciplinary research is useful for investigating antigen-antibody interactions and also may provide information required for the design of new haptens. PMID:22754368

  6. Bee algorithm and adaptive neuro-fuzzy inference system as tools for QSAR study toxicity of substituted benzenes to Tetrahymena pyriformis.

    PubMed

    Zarei, Kobra; Atabati, Morteza; Kor, Kamalodin

    2014-06-01

    A quantitative structure-activity relationship (QSAR) was developed to predict the toxicity of substituted benzenes to Tetrahymena pyriformis. A set of 1,497 zero- to three-dimensional descriptors were used for each molecule in the data set. A major problem of QSAR is the high dimensionality of the descriptor space; therefore, descriptor selection is one of the most important steps. In this paper, bee algorithm was used to select the best descriptors. Three descriptors were selected and used as inputs for adaptive neuro-fuzzy inference system (ANFIS). Then the model was corrected for unstable compounds (the compounds that can be ionized in the aqueous solutions or can easily metabolize under some conditions). Finally squared correlation coefficients were obtained as 0.8769, 0.8649 and 0.8301 for training, test and validation sets, respectively. The results showed bee-ANFIS can be used as a powerful model for prediction of toxicity of substituted benzenes to T. pyriformis. PMID:24638918

  7. In Silico Exploration of 1,7-Diazacarbazole Analogs as Checkpoint Kinase 1 Inhibitors by Using 3D QSAR, Molecular Docking Study, and Molecular Dynamics Simulations.

    PubMed

    Gao, Xiaodong; Han, Liping; Ren, Yujie

    2016-01-01

    Checkpoint kinase 1 (Chk1) is an important serine/threonine kinase with a self-protection function. The combination of Chk1 inhibitors and anti-cancer drugs can enhance the selectivity of tumor therapy. In this work, a set of 1,7-diazacarbazole analogs were identified as potent Chk1 inhibitors through a series of computer-aided drug design processes, including three-dimensional quantitative structure-activity relationship (3D-QSAR) modeling, molecular docking, and molecular dynamics simulations. The optimal QSAR models showed significant cross-validated correlation q² values (0.531, 0.726), fitted correlation r² coefficients (higher than 0.90), and standard error of prediction (less than 0.250). These results suggested that the developed models possess good predictive ability. Moreover, molecular docking and molecular dynamics simulations were applied to highlight the important interactions between the ligand and the Chk1 receptor protein. This study shows that hydrogen bonding and electrostatic forces are key interactions that confer bioactivity. PMID:27164065

  8. Analysis of B-Raf[Formula: see text] inhibitors using 2D and 3D-QSAR, molecular docking and pharmacophore studies.

    PubMed

    Aalizadeh, Reza; Pourbasheer, Eslam; Ganjali, Mohammad Reza

    2015-11-01

    In the present work, a molecular modeling study was carried out using 2D and 3D quantitative structure-activity relationships for the various series of compounds known as B-Raf[Formula: see text] inhibitors. For 2D-QSAR analysis, a linear model was developed by MLR based on GA-OLS with appropriate results [Formula: see text], which was validated by several external validation techniques. To perform a 3D-QSAR analysis, CoMFA and CoMSIA methods were used. The selected CoMFA model could provide reliable statistical values [Formula: see text] based on the training set in the biases of the selected alignment. Using the same selected alignment, a statistically reliable CoMSIA model, out of thirty-one different combinations, was also obtained [Formula: see text]. The predictive accuracy of the derived models was rigorously evaluated with the external test set of nineteen compounds based on several validation techniques. Molecular docking simulations and pharmacophore analyses were also performed to derive the true conformations of the most potent inhibitors with B-Raf[Formula: see text] kinase. PMID:26276566

  9. In silico screening for identification of novel β-1,3-glucan synthase inhibitors using pharmacophore and 3D-QSAR methodologies.

    PubMed

    Meetei, Potshangbam Angamba; Rathore, R S; Prabhu, N Prakash; Vindal, Vaibhav

    2016-01-01

    The enzyme β-1,3-glucan synthase, which catalyzes the synthesis of β-1,3-glucan, an essential and unique structural component of the fungal cell wall, has been considered as a promising target for the development of less toxic anti-fungal agents. In this study, a robust pharmacophore model was developed and structure activity relationship analysis of 42 pyridazinone derivatives as β-1,3-glucan synthase inhibitors were carried out. A five-point pharmacophore model, consisting of two aromatic rings (R) and three hydrogen bond acceptors (A) was generated. Pharmacophore based 3D-QSAR model was developed for the same reported data sets. The generated 3D-QSAR model yielded a significant correlation coefficient value (R (2) = 0.954) along with good predictive power confirmed by the high value of cross-validated correlation coefficient (Q (2) = 0.827). Further, the pharmacophore model was employed as a 3D search query to screen small molecules database retrieved from ZINC to select new scaffolds. Finally, ADME studies revealed the pharmacokinetic efficiency of these compounds. PMID:27429875

  10. DYNAMIC 3D QSAR TECHNIQUES: APPLICATIONS IN TOXICOLOGY

    EPA Science Inventory

    Two dynamic techniques recently developed to account for conformational flexibility of chemicals in 3D QSARs are presented. In addition to the impact of conformational flexibility of chemicals in 3D QSAR models, the applicability of various molecular descriptors is discussed. The...

  11. Peptide Bacteriocins--Structure Activity Relationships.

    PubMed

    Etayash, Hashem; Azmi, Sarfuddin; Dangeti, Ramana; Kaur, Kamaljit

    2015-01-01

    With the growing concerns in the scientific and health communities over increasing levels of antibiotic resistance, antimicrobial peptide bacteriocins have emerged as promising alternatives to conventional small molecule antibiotics. A substantial attention has recently focused on the utilization of bacteriocins in food preservation and health safety. Despite the fact that a large number of bacteriocins have been reported, only a few have been fully characterized and structurally elucidated. Since knowledge of the molecular structure is a key for understanding the mechanism of action and therapeutic effects of peptide, we centered our focus in this review on the structure-activity relationships of bacteriocins with a particular focus in seven bacteriocins, namely, nisin, microcin J25, microcin B17, microcin C, leucocin A, sakacin P, and pediocin PA-1. Significant structural changes responsible for the altered activity of the recent bacteriocin analogues are discussed here. PMID:26265354

  12. 3D-QSAR and 3D-QSSR studies of thieno[2,3-d]pyrimidin-4-yl hydrazone analogues as CDK4 inhibitors by CoMFA analysis

    PubMed Central

    Cai, Bao-qin; Jin, Hai-xiao; Yan, Xiao-jun; Zhu, Peng; Hu, Gui-xiang

    2014-01-01

    Aim: To investigate the structural basis underlying potency and selectivity of a series of novel analogues of thieno[2,3-d]pyrimidin-4-yl hydrazones as cyclin-dependent kinase 4 (CDK4) inhibitors and to use this information for drug design strategies. Methods: Three-dimensional quantitative structure-activity relationship (3D-QSAR) and three-dimensional quantitative structure-selectivity relationship (3D-QSSR) models using comparative molecular field analysis (CoMFA) were conducted on a training set of 48 compounds. Partial least squares (PLS) analysis was employed. External validation was performed with a test set of 9 compounds. Results: The obtained 3D-QSAR model (q2=0.724, r2=0.965, r2pred=0.945) and 3D-QSSR model (q2=0.742, r2=0.923, r2pred=0.863) were robust and predictive. Contour maps with good compatibility to active binding sites provided insight into the potentially important structural features required to enhance activity and selectivity. The contour maps indicated that bulky groups at R1 position could potentially enhance CDK4 inhibitory activity, whereas bulky groups at R3 position have the opposite effect. Appropriate incorporation of bulky electropositive groups at R4 position is favorable and could improve both potency and selectivity to CDK4. Conclusion: These two models provide useful information to guide drug design strategies aimed at obtaining potent and selective CDK4 inhibitors. PMID:24122012

  13. Quantitative Structure Activity Relationship for Inhibition of Human Organic Cation/Carnitine Transporter (OCTN2)

    PubMed Central

    Diao, Lei; Ekins, Sean; Polli, James E.

    2010-01-01

    Organic cation/carnitine transporter (OCTN2; SLC22A5) is an important transporter for L-carnitine homeostasis, but can be inhibited by drugs, which may cause L-carnitine deficiency and possibly other OCTN2-mediated drug-drug interactions. One objective was to develop a quantitative structure–activity relationship (QSAR) of OCTN2 inhibitors, in order to predict and identify other potential OCTN2 inhibitors and infer potential clinical interactions. A second objective was to assess two high renal clearance drugs that interact with OCTN2 in vitro (cetirizine and cephaloridine) for possible OCTN2-mediated drug-drug interactions. Using previously generated in vitro data of 22 drugs, a 3D quantitative pharmacophore model and a Bayesian machine learning model were developed. The four pharmacophore features include two hydrophobic groups, one hydrogen-bond acceptor, and one positive ionizable center. The Bayesian machine learning model was developed using simple interpretable descriptors and function class fingerprints of maximum diameter 6 (FCFP_6). An external test set of 27 molecules, including 15 newly identified OCTN2 inhibitors, and a literature test set of 22 molecules were used to validate both models. The computational models afforded good capability to identify structurally diverse OCTN2 inhibitors, providing a valuable tool to predict new inhibitors efficiently. Inhibition results confirmed our previously observed association between rhabdomyolysis and Cmax/Ki ratio. The two high renal clearance drugs cetirizine and cephaloridine were found not to be OCTN2 substrates and their diminished elimination by other drugs is concluded not to be mediated by OCTN2. PMID:20831193

  14. Quantitative structure–activity relationship analysis of the pharmacology of para-substituted methcathinone analogues

    PubMed Central

    Bonano, J S; Banks, M L; Kolanos, R; Sakloth, F; Barnier, M L; Glennon, R A; Cozzi, N V; Partilla, J S; Baumann, M H; Negus, S S

    2015-01-01

    Background and Purpose Methcathinone (MCAT) is a potent monoamine releaser and parent compound to emerging drugs of abuse including mephedrone (4-CH3 MCAT), the para-methyl analogue of MCAT. This study examined quantitative structure–activity relationships (QSAR) for MCAT and six para-substituted MCAT analogues on (a) in vitro potency to promote monoamine release via dopamine and serotonin transporters (DAT and SERT, respectively), and (b) in vivo modulation of intracranial self-stimulation (ICSS), a behavioural procedure used to evaluate abuse potential. Neurochemical and behavioural effects were correlated with steric (Es), electronic (σp) and lipophilic (πp) parameters of the para substituents. Experimental Approach For neurochemical studies, drug effects on monoamine release through DAT and SERT were evaluated in rat brain synaptosomes. For behavioural studies, drug effects were tested in male Sprague-Dawley rats implanted with electrodes targeting the medial forebrain bundle and trained to lever-press for electrical brain stimulation. Key Results MCAT and all six para-substituted analogues increased monoamine release via DAT and SERT and dose- and time-dependently modulated ICSS. In vitro selectivity for DAT versus SERT correlated with in vivo efficacy to produce abuse-related ICSS facilitation. In addition, the Es values of the para substituents correlated with both selectivity for DAT versus SERT and magnitude of ICSS facilitation. Conclusions and Implications Selectivity for DAT versus SERT in vitro is a key determinant of abuse-related ICSS facilitation by these MCAT analogues, and steric aspects of the para substituent of the MCAT scaffold (indicated by Es) are key determinants of this selectivity. PMID:25438806

  15. Obstacles to activity pacing: assessment, relationship to activity and functioning.

    PubMed

    Cane, Douglas; McCarthy, Mary; Mazmanian, Dwight

    2016-07-01

    Activity pacing is frequently included among the strategies provided to individuals with chronic pain to manage pain and improve functioning. Individuals with chronic pain may, however, limit their use of activity pacing because they perceive significant obstacles to its use. This study describes the development of a measure to assess obstacles to activity pacing and examines the relationship of this measure to activity patterns and functioning. A sample of 637 individuals with chronic pain completed items describing potential obstacles to activity pacing as part of their pretreatment assessment. Item analyses were used to construct a 14-item measure of obstacles to activity pacing. A subset of these individuals completed the measure again after completion of a group treatment program. The resulting measure demonstrated excellent internal consistency and was minimally affected by social desirability. Correlations with measures of activity and psychosocial functioning provided initial construct validity for the measure. Sex differences were found with women initially identifying more obstacles to activity pacing. Fewer obstacles were identified by both men and women after treatment, and these changes were related to modest changes in activity patterns and functioning. The present results identify a number of obstacles that may limit the use of activity pacing by individuals with chronic pain. Treatment may result in a decrease in the number of obstacles identified, and this change is related to changes in the individual's activity pattern and psychosocial functioning. PMID:26963845

  16. Structural relationships and vasorelaxant activity of monoterpenes

    PubMed Central

    2012-01-01

    Background and purpose of the study The hypotensive activity of the essential oil of Mentha x villosa and its main constituent, the monoterpene rotundifolone, have been reported. Therefore, our objective was to evaluate the vasorelaxant effect of monoterpenes found in medicinal plants and establish the structure-activity relationship of rotundifolone and its structural analogues on the rat superior mesenteric artery. Methods Contractions of the vessels were induced with 10 μM of phenylephine (Phe) in rings with endothelium. During the tonic phase of the contraction, the monoterpenes (10-8 - 10-3, cumulatively) were added to the organ bath. The extent of relaxation was expressed as the percentage of Phe-induced contraction. Results The results from the present study showed that both oxygenated terpenes (rotundifolone, (+)-limonene epoxide, pulegone epoxide, carvone epoxide, and (+)-pulegone) and non-oxygenated terpene ((+)-limonene) exhibit relaxation activity. The absence of an oxygenated molecular structure was not a critical requirement for the molecule to be bioactive. Also it was found that the position of ketone and epoxide groups in the monoterpene structures influence the vasorelaxant potency and efficacy. Major conclusion The results suggest that the presence of functional groups in the chemical structure of rotundifolone is not essential for its vasorelaxant activity. PMID:23351149

  17. Predictions of Cleavability of Calpain Proteolysis by Quantitative Structure-Activity Relationship Analysis Using Newly Determined Cleavage Sites and Catalytic Efficiencies of an Oligopeptide Array*

    PubMed Central

    Shinkai-Ouchi, Fumiko; Koyama, Suguru; Ono, Yasuko; Hata, Shoji; Ojima, Koichi; Shindo, Mayumi; duVerle, David; Ueno, Mika; Kitamura, Fujiko; Doi, Naoko; Takigawa, Ichigaku; Mamitsuka, Hiroshi; Sorimachi, Hiroyuki

    2016-01-01

    Calpains are intracellular Ca2+-regulated cysteine proteases that are essential for various cellular functions. Mammalian conventional calpains (calpain-1 and calpain-2) modulate the structure and function of their substrates by limited proteolysis. Thus, it is critically important to determine the site(s) in proteins at which calpains cleave. However, the calpains' substrate specificity remains unclear, because the amino acid (aa) sequences around their cleavage sites are very diverse. To clarify calpains' substrate specificities, 84 20-mer oligopeptides, corresponding to P10-P10′ of reported cleavage site sequences, were proteolyzed by calpains, and the catalytic efficiencies (kcat/Km) were globally determined by LC/MS. This analysis revealed 483 cleavage site sequences, including 360 novel ones. The kcat/Kms for 119 sites ranged from 12.5–1,710 M−1s−1. Although most sites were cleaved by both calpain-1 and −2 with a similar kcat/Km, sequence comparisons revealed distinct aa preferences at P9-P7/P2/P5′. The aa compositions of the novel sites were not statistically different from those of previously reported sites as a whole, suggesting calpains have a strict implicit rule for sequence specificity, and that the limited proteolysis of intact substrates is because of substrates' higher-order structures. Cleavage position frequencies indicated that longer sequences N-terminal to the cleavage site (P-sites) were preferred for proteolysis over C-terminal (P′-sites). Quantitative structure-activity relationship (QSAR) analyses using partial least-squares regression and >1,300 aa descriptors achieved kcat/Km prediction with r = 0.834, and binary-QSAR modeling attained an 87.5% positive prediction value for 132 reported calpain cleavage sites independent of our model construction. These results outperformed previous calpain cleavage predictors, and revealed the importance of the P2, P3′, and P4′ sites, and P1-P2 cooperativity. Furthermore, using our

  18. Predictions of Cleavability of Calpain Proteolysis by Quantitative Structure-Activity Relationship Analysis Using Newly Determined Cleavage Sites and Catalytic Efficiencies of an Oligopeptide Array.

    PubMed

    Shinkai-Ouchi, Fumiko; Koyama, Suguru; Ono, Yasuko; Hata, Shoji; Ojima, Koichi; Shindo, Mayumi; duVerle, David; Ueno, Mika; Kitamura, Fujiko; Doi, Naoko; Takigawa, Ichigaku; Mamitsuka, Hiroshi; Sorimachi, Hiroyuki

    2016-04-01

    Calpains are intracellular Ca(2+)-regulated cysteine proteases that are essential for various cellular functions. Mammalian conventional calpains (calpain-1 and calpain-2) modulate the structure and function of their substrates by limited proteolysis. Thus, it is critically important to determine the site(s) in proteins at which calpains cleave. However, the calpains' substrate specificity remains unclear, because the amino acid (aa) sequences around their cleavage sites are very diverse. To clarify calpains' substrate specificities, 84 20-mer oligopeptides, corresponding to P10-P10' of reported cleavage site sequences, were proteolyzed by calpains, and the catalytic efficiencies (kcat/Km) were globally determined by LC/MS. This analysis revealed 483 cleavage site sequences, including 360 novel ones. Thekcat/Kms for 119 sites ranged from 12.5-1,710 M(-1)s(-1) Although most sites were cleaved by both calpain-1 and -2 with a similarkcat/Km, sequence comparisons revealed distinct aa preferences at P9-P7/P2/P5'. The aa compositions of the novel sites were not statistically different from those of previously reported sites as a whole, suggesting calpains have a strict implicit rule for sequence specificity, and that the limited proteolysis of intact substrates is because of substrates' higher-order structures. Cleavage position frequencies indicated that longer sequences N-terminal to the cleavage site (P-sites) were preferred for proteolysis over C-terminal (P'-sites). Quantitative structure-activity relationship (QSAR) analyses using partial least-squares regression and >1,300 aa descriptors achievedkcat/Kmprediction withr= 0.834, and binary-QSAR modeling attained an 87.5% positive prediction value for 132 reported calpain cleavage sites independent of our model construction. These results outperformed previous calpain cleavage predictors, and revealed the importance of the P2, P3', and P4' sites, and P1-P2 cooperativity. Furthermore, using our binary-QSAR model

  19. IDENTIFICATION OF PUTATIVE ESTROGEN RECEPTOR-MEDIATED ENDOCRINE DISRUPTING CHEMICALS USING QSAR- AND STRUCTURE-BASED VIRTUAL SCREENING APPROACHES

    PubMed Central

    Zhang, Liying; Sedykh, Alexander; Tripathi, Ashutosh; Zhu, Hao; Afantitis, Antreas; Mouchlis, Varnavas D.; Melagraki, Georgia; Rusyn, Ivan; Tropsha, Alexander

    2013-01-01

    Identification of Endocrine Disrupting Chemicals is one of the important goals of environmental chemical hazard screening. We report on the development of validated in silico predictors of chemicals likely to cause Estrogen Receptor (ER)-mediated endocrine disruption to facilitate their prioritization for future screening. A database of relative binding affinity of a large number of ERα and/or ERβ ligands was assembled (546 for ERα and 137 for ERβ). Both single-task learning (STL) and multi-task learning (MTL) continuous Quantitative Structure-Activity Relationships (QSAR) models were developed for predicting ligand binding affinity to ERα or ERβ. High predictive accuracy was achieved for ERα binding affinity (MTL R2=0.71, STL R2=0.73). For ERβ binding affinity, MTL models were significantly more predictive (R2=0.53, p<0.05) than STL models. In addition, docking studies were performed on a set of ER agonists/antagonists (67 agonists and 39 antagonists for ERα, 48 agonists and 32 antagonists for ERβ, supplemented by putative decoys/non-binders) using the following ER structures (in complexes with respective ligands) retrieved from the Protein Data Bank: ERα agonist (PDB ID: 1L2I), ERα antagonist (PDB ID: 3DT3), ERβ agonist (PDB ID: 2NV7), ERβ antagonist (PDB ID: 1L2J). We found that all four ER conformations discriminated their corresponding ligands from presumed non-binders. Finally, both QSAR models and ER structures were employed in parallel to virtually screen several large libraries of environmental chemicals to derive a ligand- and structure-based prioritized list of putative estrogenic compounds to be used for in vitro and in vivo experimental validation. PMID:23707773

  20. Identification of putative estrogen receptor-mediated endocrine disrupting chemicals using QSAR- and structure-based virtual screening approaches.

    PubMed

    Zhang, Liying; Sedykh, Alexander; Tripathi, Ashutosh; Zhu, Hao; Afantitis, Antreas; Mouchlis, Varnavas D; Melagraki, Georgia; Rusyn, Ivan; Tropsha, Alexander

    2013-10-01

    Identification of endocrine disrupting chemicals is one of the important goals of environmental chemical hazard screening. We report on the development of validated in silico predictors of chemicals likely to cause estrogen receptor (ER)-mediated endocrine disruption to facilitate their prioritization for future screening. A database of relative binding affinity of a large number of ERα and/or ERβ ligands was assembled (546 for ERα and 137 for ERβ). Both single-task learning (STL) and multi-task learning (MTL) continuous quantitative structure-activity relationship (QSAR) models were developed for predicting ligand binding affinity to ERα or ERβ. High predictive accuracy was achieved for ERα binding affinity (MTL R(2)=0.71, STL R(2)=0.73). For ERβ binding affinity, MTL models were significantly more predictive (R(2)=0.53, p<0.05) than STL models. In addition, docking studies were performed on a set of ER agonists/antagonists (67 agonists and 39 antagonists for ERα, 48 agonists and 32 antagonists for ERβ, supplemented by putative decoys/non-binders) using the following ER structures (in complexes with respective ligands) retrieved from the Protein Data Bank: ERα agonist (PDB ID: 1L2I), ERα antagonist (PDB ID: 3DT3), ERβ agonist (PDB ID: 2NV7), and ERβ antagonist (PDB ID: 1L2J). We found that all four ER conformations discriminated their corresponding ligands from presumed non-binders. Finally, both QSAR models and ER structures were employed in parallel to virtually screen several large libraries of environmental chemicals to derive a ligand- and structure-based prioritized list of putative estrogenic compounds to be used for in vitro and in vivo experimental validation. PMID:23707773

  1. Construction and analysis of a human hepatotoxicity database suitable for QSAR modeling using post-market safety data.

    PubMed

    Zhu, Xiao; Kruhlak, Naomi L

    2014-07-01

    Drug-induced liver injury (DILI) is one of the most common drug-induced adverse events (AEs) leading to life-threatening conditions such as acute liver failure. It has also been recognized as the single most common cause of safety-related post-market withdrawals or warnings. Efforts to develop new predictive methods to assess the likelihood of a drug being a hepatotoxicant have been challenging due to the complexity and idiosyncrasy of clinical manifestations of DILI. The FDA adverse event reporting system (AERS) contains post-market data that depict the morbidity of AEs. Here, we developed a scalable approach to construct a hepatotoxicity database using post-market data for the purpose of quantitative structure-activity relationship (QSAR) modeling. A set of 2029 unique and modelable drug entities with 13,555 drug-AE combinations was extracted from the AERS database using 37 hepatotoxicity-related query preferred terms (PTs). In order to determine the optimal classification scheme to partition positive from negative drugs, a manually-curated DILI calibration set composed of 105 negatives and 177 positives was developed based on the published literature. The final classification scheme combines hepatotoxicity-related PT data with supporting information that optimize the predictive performance across the calibration set. Data for other toxicological endpoints related to liver injury such as liver enzyme abnormalities, cholestasis, and bile duct disorders, were also extracted and classified. Collectively, these datasets can be used to generate a battery of QSAR models that assess a drug's potential to cause DILI. PMID:24721472

  2. QSAR study of curcumine derivatives as HIV-1 integrase inhibitors.

    PubMed

    Gupta, Pawan; Sharma, Anju; Garg, Prabha; Roy, Nilanjan

    2013-03-01

    A QSAR study was performed on curcumine derivatives as HIV-1 integrase inhibitors using multiple linear regression. The statistically significant model was developed with squared correlation coefficients (r(2)) 0.891 and cross validated r(2) (r(2) cv) 0.825. The developed model revealed that electronic, shape, size, geometry, substitution's information and hydrophilicity were important atomic properties for determining the inhibitory activity of these molecules. The model was also tested successfully for external validation (r(2) pred = 0.849) as well as Tropsha's test for model predictability. Furthermore, the domain analysis was carried out to evaluate the prediction reliability of external set molecules. The model was statistically robust and had good predictive power which can be successfully utilized for screening of new molecules. PMID:23286784

  3. Novel HIV-1 Integrase Inhibitor Development by Virtual Screening Based on QSAR Models.

    PubMed

    Guasch, Laura; Zakharov, Alexey V; Tarasova, Olga A; Poroikov, Vladimir V; Liao, Chenzhong; Nicklaus, Marc C

    2016-01-01

    HIV-1 integrase (IN) plays an important role in the life cycle of HIV and is responsible for integration of the virus into the human genome. We present computational approaches used to design novel HIV-1 IN inhibitors. We created an IN inhibitor database by collecting experimental data from the literature. We developed quantitative structure-activity relationship (QSAR) models of HIV-1 IN strand transfer (ST) inhibitors using this database. The prediction accuracy of these models was estimated by external 5-fold cross-validation as well as with an additional validation set of 308 structurally distinct compounds from the publicly accessible BindingDB database. The validated models were used to screen a small combinatorial library of potential synthetic candidates to identify hits, with a subsequent docking approach applied to further filter out compounds to arrive at a small set of potential HIV-1 IN inhibitors. As result, 236 compounds with good druglikeness properties and with correct docking poses were identified as potential candidates for synthesis. One of the six compounds finally chosen for synthesis was experimentally confirmed to inhibit the ST reaction with an IC50(ST) of 37 µM. The IN inhibitor database is available for download from http://cactus.nci.nih.gov/download/iidb/. PMID:26268340

  4. Dependence of QSAR models on the selection of trial descriptor sets: a demonstration using nanotoxicity endpoints of decorated nanotubes.

    PubMed

    Shao, Chi-Yu; Chen, Sing-Zuo; Su, Bo-Han; Tseng, Yufeng J; Esposito, Emilio Xavier; Hopfinger, Anton J

    2013-01-28

    Little attention has been given to the selection of trial descriptor sets when designing a QSAR analysis even though a great number of descriptor classes, and often a greater number of descriptors within a given class, are now available. This paper reports an effort to explore interrelationships between QSAR models and descriptor sets. Zhou and co-workers (Zhou et al., Nano Lett. 2008, 8 (3), 859-865) designed, synthesized, and tested a combinatorial library of 80 surface modified, that is decorated, multi-walled carbon nanotubes for their composite nanotoxicity using six endpoints all based on a common 0 to 100 activity scale. Each of the six endpoints for the 29 most nanotoxic decorated nanotubes were incorporated as the training set for this study. The study reported here includes trial descriptor sets for all possible combinations of MOE, VolSurf, and 4D-fingerprints (FP) descriptor classes, as well as including and excluding explicit spatial contributions from the nanotube. Optimized QSAR models were constructed from these multiple trial descriptor sets. It was found that (a) both the form and quality of the best QSAR models for each of the endpoints are distinct and (b) some endpoints are quite dependent upon 4D-FP descriptors of the entire nanotube-decorator complex. However, other endpoints yielded equally good models only using decorator descriptors with and without the decorator-only 4D-FP descriptors. Lastly, and most importantly, the quality, significance, and interpretation of a QSAR model were found to be critically dependent on the trial descriptor sets used within a given QSAR endpoint study. PMID:23252880

  5. Structure-activity relationships for hydroxylated polychlorinated biphenyls as inhibitors of the sulfation of dehydroepiandrosterone catalyzed by human hydroxysteroid sulfotransferase SULT2A1.

    PubMed

    Ekuase, Edugie J; Liu, Yungang; Lehmler, Hans-Joachim; Robertson, Larry W; Duffel, Michael W

    2011-10-17

    Polychlorinated biphenyls (PCBs) are persistent worldwide pollutants that are of concern due to their bioaccumulation and health effects. Metabolic oxidation of PCBs results in the formation of hydroxylated metabolites (OHPCBs). Among their biological effects, OHPCBs have been shown to alter the metabolism of endocrine hormones, including inhibition of mammalian cytosolic sulfotransferases (SULTs) that are responsible for the inactivation of thyroid hormones and phenolic steroids (i.e., hSULT1A1, hSULT1B1, and hSULT1E1). OHPCBs also interact with a human hydroxysteroid sulfotransferase that plays a role in the sulfation of endogenous alcohol-containing steroid hormones and bile acids (i.e., hSULT2A1). The objectives of our current study were to examine the effects of a series of OHPCB congeners on the activity of hSULT2A1 and to develop a three-dimensional quantitative structure-activity relationship (3D-QSAR) model for OHPCBs as inhibitors of the enzyme. A total of 15 OHPCBs were examined, and the sulfation of 1 μM [(3)H] dehydroepiandrosterone (DHEA) was utilized as a model reaction catalyzed by the enzyme. All 15 OHPCBs inhibited the sulfation of DHEA, with IC(50) values ranging from 0.6 μM to 96 μM, and eight of these OHPCBs were also substrates for the enzyme. Comparative molecular field analysis (CoMFA) provided a predictive 3D-QSAR model with a q(2) value of 0.697 and an r(2) value of 0.949. The OHPCBs that had the highest potency as inhibitors of DHEA sulfation were those with a 3, 5-dichloro-4-hydroxy substitution pattern on the biphenyl ring system, and these congeners were also substrates for sulfation catalyzed by hSULT2A1. PMID:21913674

  6. HLA-A3 supermotif defined by quantitative structure-activity relationship analysis.

    PubMed

    Guan, Pingping; Doytchinova, Irini A; Flower, Darren R

    2003-01-01

    Activation of a cytotoxic T cell requires specific binding of antigenic peptides to major histocompatibility complex (MHC) molecules. This paper reports a study of peptides binding to members of the HLA-A3 superfamily using a recently developed 2D-QSAR method, called the additive method. Four alleles with high phenotype frequency were included in the study: A*0301, A*1101, A*3101 and A*6801. The influence of each of the 20 amino acids at each position of the peptide on binding was studied. A refined A3 supertype motif was defined in the study. PMID:12646688

  7. In silico evaluation, molecular docking and QSAR analysis of quinazoline-based EGFR-T790M inhibitors.

    PubMed

    Asadollahi-Baboli, M

    2016-08-01

    Mutated epidermal growth factor receptor (EGFR-T790M) inhibitors hold promise as new agents against cancer. Molecular docking and QSAR analysis were performed based on a series of fifty-three quinazoline derivatives to elucidate key structural and physicochemical properties affecting inhibitory activity. Molecular docking analysis identified the true conformations of ligands in the receptor's active pocket. The structural features of the ligands, expressed as molecular descriptors, were derived from the obtained docked conformations. Non-linear and spline QSAR models were developed through novel genetic algorithm and artificial neural network (GA-ANN) and multivariate adaptive regression spline techniques, respectively. The former technique was employed to consider non-linear relation between molecular descriptors and inhibitory activity of quinazoline derivatives. The later technique was also used to describe the non-linearity using basis functions and sub-region equations for each descriptor. Our QSAR model gave a high predictive performance [Formula: see text] and [Formula: see text]) using diverse validation techniques. Eight new compounds were designed using our QSAR model as potent EGFR-T790M inhibitors. Overall, the proposed in silico strategy based on docked derived descriptor and non-linear descriptor subset selection may help design novel quinazoline derivatives with improved EGFR-T790M inhibitory activity. PMID:27209475

  8. 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. PMID:23597248

  9. QSAR studies on chalcones and flavonoids as anti-tuberculosis agents using genetic function approximation (GFA) method.

    PubMed

    Sivakumar, Ponnurengam Malliappan; Geetha Babu, Sethu Kailasam; Mukesh, Doble

    2007-01-01

    Design of compounds having good anti-tubercular activity is gaining much importance in the field of tuberculosis research due to reemergence of antibiotic resistance strains. In this paper quantitative structure activity relationships (QSAR) were developed on chalcones, chalcone-like compounds, flavones and flavanones to understand the relationship between biological activity and structural features. Genetic function approximation (GFA) method was used to identify the descriptors that would lead to good regression equations. The best molecular descriptors identified were Jurs descriptors (Jurs charged partial surface area), hydrogen bond donor, principal moment of inertia, molecular energy, dipole magnetic, molecular area, absorption, distribution, metabolism and excretion (ADME) properties and Chi indices (Kier & Hall chi connectivity indices). Excellent statistically significant models were developed by this approach (r(2)=0.8-0.97) for the four groups of compounds. The cross validated r(2) (XV r(2)) which is an indication of the predictive capability of the model for all the cases was also very good (=0.79-0.94). PMID:17202700

  10. Editorial: Current status and perspective on drug targets in tubercle bacilli and drug design of antituberculous agents based on structure-activity relationship.

    PubMed

    Tomioka, Haruaki

    2014-01-01

    Worldwide, tuberculosis (TB) remains the most frequent and important infectious disease causing morbidity and death. However, the development of new drugs for the treatment and prophylaxis of TB, particularly those truly active against dormant and persistent types of tubercle bacilli, has been slow, although some promising drugs, such as diarylquinoline TMC207, nitroimidazopyran PA-824, nitroimidazo-oxazole Delamanid (OPC-67683), oxazolidinone PNU-100480, ethylene diamine SQ-109, and pyrrole derivative LL3858, are currently under phase 1 to 3 clinical trials. Therefore, novel types of antituberculous drug, which act on unique drug targets in Mycobacterium tuberculosis (MTB) pathogens, particularly drug targets related to the establishment of mycobacterial dormancy in the host's macrophages, are urgently needed. In this context, it should be noted that current anti-TB drugs mostly target the metabolic reactions and proteins which are essential for the growth of MTB in extracellular milieus. It may also be promising to develop another type of drug that exerts an inhibitory action against bacterial virulence factors which cross-talk and interfere with signaling pathways of MTB-infected immunocompetent host cells, such as lymphocytes, macrophages, and NK cells, thereby changing the intracellular milieus that are favorable to intramacrophage survival and the growth of infected bacilli. This special issue contains ten review articles, dealing with recent approaches to identify and establish novel drug targets in MTB for the development of new and unique antitubercular drugs, including those related to mycobacterial dormancy and crosstalk with cellular signaling pathways. In addition, this special issue contains some review papers with special reference to the drug design based on quantitative structure-activity relationship (QSAR) analysis, especially three-dimensional (3D)-QSAR. New, critical information on the entire genome of MTB and mycobacterial virulence genes is

  11. Synthesis, Biological Evaluation and 2D-QSAR Study of Halophenyl Bis-Hydrazones as Antimicrobial and Antitubercular Agents

    PubMed Central

    Abdel-Aziz, Hatem A.; Eldehna, Wagdy M.; Fares, Mohamed; Al-Rashood, Sara T. A.; Al-Rashood, Khalid A.; Abdel-Aziz, Marwa M.; Soliman, Dalia H.

    2015-01-01

    In continuation of our endeavor towards the development of potent and effective antimicrobial agents, three series of halophenyl bis-hydrazones (14a–n, 16a–d, 17a and 17b) were synthesized and evaluated for their potential antibacterial, antifungal and antimycobacterial activities. These efforts led to the identification of five molecules 14c, 14g, 16b, 17a and 17b (MIC range from 0.12 to 7.81 μg/mL) with broad antimicrobial activity against Mycobacterium tuberculosis; Aspergillus fumigates; Gram positive bacteria, Staphylococcus aureus, Streptococcus pneumonia, and Bacillis subtilis; and Gram negative bacteria, Salmonella typhimurium, Klebsiella pneumonia, and Escherichia coli. Three of the most active compounds, 16b, 17a and 17b, were also devoid of apparent cytotoxicity to lung cancer cell line A549. Amphotericin B and ciprofloxacin were used as references for antifungal and antibacterial screening, while isoniazid and pyrazinamide were used as references for antimycobacterial activity. Furthermore, three Quantitative Structure Activity Relationship (QSAR) models were built to explore the structural requirements controlling the different activities of the prepared bis-hydrazones. PMID:25903147

  12. QSAR Modeling Using Large-Scale Databases: Case Study for HIV-1 Reverse Transcriptase Inhibitors.

    PubMed

    Tarasova, Olga A; Urusova, Aleksandra F; Filimonov, Dmitry A; Nicklaus, Marc C; Zakharov, Alexey V; Poroikov, Vladimir V

    2015-07-27

    Large-scale databases are important sources of training sets for various QSAR modeling approaches. Generally, these databases contain information extracted from different sources. This variety of sources can produce inconsistency in the data, defined as sometimes widely diverging activity results for the same compound against the same target. Because such inconsistency can reduce the accuracy of predictive models built from these data, we are addressing the question of how best to use data from publicly and commercially accessible databases to create accurate and predictive QSAR models. We investigate the suitability of commercially and publicly available databases to QSAR modeling of antiviral activity (HIV-1 reverse transcriptase (RT) inhibition). We present several methods for the creation of modeling (i.e., training and test) sets from two, either commercially or freely available, databases: Thomson Reuters Integrity and ChEMBL. We found that the typical predictivities of QSAR models obtained using these different modeling set compilation methods differ significantly from each other. The best results were obtained using training sets compiled for compounds tested using only one method and material (i.e., a specific type of biological assay). Compound sets aggregated by target only typically yielded poorly predictive models. We discuss the possibility of "mix-and-matching" assay data across aggregating databases such as ChEMBL and Integrity and their current severe limitations for this purpose. One of them is the general lack of complete and semantic/computer-parsable descriptions of assay methodology carried by these databases that would allow one to determine mix-and-matchability of result sets at the assay level. PMID:26046311

  13. Molecular Dynamics Guided Receptor Independent 4D QSAR Studies of Substituted Coumarins as Anticancer Agents.

    PubMed

    Patil, Rajesh; Sawant, Sanjay

    2015-01-01

    The search for newer cytotoxic agents has taken many paths in the recent years and in fact some of these efforts led to the discovery of some potent cytotoxic agents. Though the vast number of targets of tumor progression has been identified recently, kinases remained key targets in drug design. It is well established that inhibition of JNK1, a serine/threonine protein kinase delays tumor formation. Poly hydroxylated chromenone analog, quescetagetin, inhibits JNK1. As a part of design of coumarin based JNK1 inhibitors, docking studies and 4D QSAR studies were carried out. 3- pyrazolyl substituted coumarin derivatives were chosen for these studies. Docking studies revealed that 3-pyrazolyl substituted coumarins make key interactions with residues at active site of JNK1. In order to investigate the structural features required in these inhibitors, 4D QSAR studies using LQTAgrid module were carried out. The 4D QSAR model built with PLS regression on the matrix of variables specific for interaction energies at each grid point around the molecular dynamics generated conformations of individual compounds shows good predictive abilities. The squared correlation coefficient, R(2) for the model is 0.785, R(2) cross-validated (Q(2)) is 0.698, R(2) predicted is 0.701. Most of the descriptors contributing to 4D QSAR model are Coulombic potential energy based descriptors which highlight the importance of specific atoms in coumarin derivatives in generating these electrostatic potential at specific grid points with the -NH3 probe. We rationalize that solvent accessible van der Waals surface area around such compounds is good measure of this Coulombic potential energy and can be exploited in designing more active compounds. PMID:26081557

  14. Support vector regression-guided unravelling: antioxidant capacity and quantitative structure-activity relationship predict reduction and promotion effects of flavonoids on acrylamide formation.

    PubMed

    Huang, Mengmeng; Wei, Yan; Wang, Jun; Zhang, Yu

    2016-01-01

    We used the support vector regression (SVR) approach to predict and unravel reduction/promotion effect of characteristic flavonoids on the acrylamide formation under a low-moisture Maillard reaction system. Results demonstrated the reduction/promotion effects by flavonoids at addition levels of 1-10000 μmol/L. The maximal inhibition rates (51.7%, 68.8% and 26.1%) and promote rates (57.7%, 178.8% and 27.5%) caused by flavones, flavonols and isoflavones were observed at addition levels of 100 μmol/L and 10000 μmol/L, respectively. The reduction/promotion effects were closely related to the change of trolox equivalent antioxidant capacity (ΔTEAC) and well predicted by triple ΔTEAC measurements via SVR models (R: 0.633-0.900). Flavonols exhibit stronger effects on the acrylamide formation than flavones and isoflavones as well as their O-glycosides derivatives, which may be attributed to the number and position of phenolic and 3-enolic hydroxyls. The reduction/promotion effects were well predicted by using optimized quantitative structure-activity relationship (QSAR) descriptors and SVR models (R: 0.926-0.994). Compared to artificial neural network and multi-linear regression models, SVR models exhibited better fitting performance for both TEAC-dependent and QSAR descriptor-dependent predicting work. These observations demonstrated that the SVR models are competent for predicting our understanding on the future use of natural antioxidants for decreasing the acrylamide formation. PMID:27586851

  15. Support vector regression-guided unravelling: antioxidant capacity and quantitative structure-activity relationship predict reduction and promotion effects of flavonoids on acrylamide formation

    PubMed Central

    Huang, Mengmeng; Wei, Yan; Wang, Jun; Zhang, Yu

    2016-01-01

    We used the support vector regression (SVR) approach to predict and unravel reduction/promotion effect of characteristic flavonoids on the acrylamide formation under a low-moisture Maillard reaction system. Results demonstrated the reduction/promotion effects by flavonoids at addition levels of 1–10000 μmol/L. The maximal inhibition rates (51.7%, 68.8% and 26.1%) and promote rates (57.7%, 178.8% and 27.5%) caused by flavones, flavonols and isoflavones were observed at addition levels of 100 μmol/L and 10000 μmol/L, respectively. The reduction/promotion effects were closely related to the change of trolox equivalent antioxidant capacity (ΔTEAC) and well predicted by triple ΔTEAC measurements via SVR models (R: 0.633–0.900). Flavonols exhibit stronger effects on the acrylamide formation than flavones and isoflavones as well as their O-glycosides derivatives, which may be attributed to the number and position of phenolic and 3-enolic hydroxyls. The reduction/promotion effects were well predicted by using optimized quantitative structure-activity relationship (QSAR) descriptors and SVR models (R: 0.926–0.994). Compared to artificial neural network and multi-linear regression models, SVR models exhibited better fitting performance for both TEAC-dependent and QSAR descriptor-dependent predicting work. These observations demonstrated that the SVR models are competent for predicting our understanding on the future use of natural antioxidants for decreasing the acrylamide formation. PMID:27586851

  16. 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

    SciTech Connect

    Huang, X.D.; Krylov, S.N.; Ren, L.; McConkey, B.J.; Dixon, D.G.; Greenberg, B.M.

    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 that 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.

  17. Pharmacophore modeling, 3D-QSAR and docking study of 2-phenylpyrimidine analogues as selective PDE4B inhibitors.

    PubMed

    Tripuraneni, Naga Srinivas; Azam, Mohammed Afzal

    2016-04-01

    Pharmacophore modeling, molecular docking, and molecular dynamics (MD) simulation studies have been performed, to explore the putative binding modes of 2-phenylpyrimidine series as PDE4B selective inhibitors. A five point pharmacophore model was developed using 87 molecules having pIC50 ranging from 8.52 to 5.07. The pharmacophore hypothesis yielded a statistically significant 3D-QSAR model, with a high correlation coefficient (R(2)=0.918), cross validation coefficient (Q(2)=0.852), and F value (175) at 4 component PLS factor. The external validation indicated that our QSAR model possessed high predictive power (R(2)=0.70). The generated model was further validated by enrichment studies using the decoy test. To evaluate the effectiveness of docking protocol in flexible docking, we have selected crystallographic bound compound to validate our docking procedure as evident from root mean square deviation. A 10ns molecular dynamics simulation confirmed the docking results of both stability of the 1XMU-ligand complex and the presumed active conformation. Further, similar orientation was observed between the superposition of the conformations of 85 after MD simulation and best XP-docking pose; MD simulation and 3D-QSAR pose; best XP-docking and 3D-QSAR poses. Outcomes of the present study provide insight in designing novel molecules with better PDE4B selective inhibitory activity. PMID:26804643

  18. Rational Quantitative Structure-Activity Relationship (RQSAR) Screen for PXR and CAR Isoform-Specific Nuclear Receptor Ligands

    PubMed Central

    Dring, Ann M.; Anderson, Linnea E.; Qamar, Saima; Stoner, Matthew A.

    2010-01-01

    Constitutive androstane receptor (CAR) and pregnane X receptor (PXR) are closely related orphan nuclear receptor proteins that share several ligands and target overlapping sets of genes involved in homeostasis and all phases of drug metabolism. CAR and PXR are involved in the development of certain diseases, including diabetes, metabolic syndrome and obesity. Ligand screens for these receptors so far have typically focused on steroid hormone analogs with pharmacophore-based approaches, only to find relatively few new hits. Multiple CAR isoforms have been detected in human liver, with the most abundant being the constitutively active reference, CAR1, and the ligand-dependent isoform CAR3. It has been assumed that any compound that binds CAR1 should also activate CAR3, and so CAR3 can be used as a ligand-activated surrogate for CAR1 studies. The possibility of CAR3-specific ligands has not, so far, been addressed. To investigate the differences between CAR1, CAR3 and PXR, and to look for more CAR ligands that may be of use in quantitative structure-activity relationship (QSAR) studies, we performed a luciferase transactivation assay screen of 60 mostly non-steroid compounds. Known active compounds with different core chemistries were chosen as starting points and structural variants were rationally selected for screening. Distinct differences in agonist versus inverse agonist/antagonist effects were seen in 49 compounds that had some ligand effect on at least one receptor and 18 that had effects on all three receptors; eight were CAR1 ligands only, three were CAR3 only ligands and four affected PXR only. This work provides evidence for new CAR ligands, some of which have CAR3-specific effects, and provides observational data on CAR and PXR ligands with which to inform in silico strategies. Compounds that demonstrated unique activity on any one receptor are potentially valuable diagnostic tools for the investigation of in vivo molecular targets. PMID:20869355

  19. 3D-QSAR and molecular fragment replacement study on diaminopyrimidine and pyrrolotriazine ALK inhibitors

    NASA Astrophysics Data System (ADS)

    Ke, Zhipeng; Lu, Tao; Liu, Haichun; Yuan, Haoliang; Ran, Ting; Zhang, Yanmin; Yao, Sihui; Xiong, Xiao; Xu, Jinxing; Xu, Anyang; Chen, Yadong

    2014-06-01

    Over expression of anaplastic lymphoma kinase (ALK) has been found in many types of cancer, and ALK is a promising therapeutic target for the treatment of cancer. To obtain new potent inhibitors of ALK, we conducted lead optimization using 3D-QSAR modeling and molecular docking investigation of 2,4-diaminopyrimidines and 2,7-disubstituted-pyrrolo[2,1-f][1,2,4]triazine-based compounds. Three favorable 3D-QSAR models (CoMFA with q2, 0.555; r2, 0.939; CoMSIA with q2, 0.625; r2, 0.974; Topomer CoMFA with q2, 0.557; r2 0.756) have been developed to predict the biological activity of novel compounds. Topomer Search was utilized for virtual screening to obtain suitable fragments. The novel compounds generated by molecular fragment replacement (MFR) were evaluated by Topomer CoMFA prediction, Glide (docking) and further evaluated with CoMFA and CoMSIA prediction. 25 novel 2,7-disubstituted-pyrrolo[2,1-f][1,2,4]triazine derivatives as potential ALK inhibitors were finally obtained. In this paper, a combination of CoMFA, CoMSIA and Topomer CoMFA could obtain favorable 3D-QSAR models and suitable fragments for ALK inhibitors optimization. The work flow which comprised 3D-QSAR modeling, Topomer Search, MFR, molecular docking and evaluating criteria could be applied to de novo drug design and the resulted compounds initiate us to further optimize and design new potential ALK inhibitors.

  20. Brief Report: Activities in Heterosexual Romantic Relationships--Grade Differences and Associations with Relationship Satisfaction

    ERIC Educational Resources Information Center

    Carlson, Wendy; Rose, Amanda J.

    2012-01-01

    Whereas much research addresses relations of youths' heterosexual romantic relationships with sexual and/or delinquent activities, less attention has been paid to youths' more normative, day-to-day activities with romantic partners. This gap in the literature is problematic given that these activities define the substance of the relationships and…

  1. 3D QSAR studies of hydroxylated polychlorinated biphenyls as potential xenoestrogens.

    PubMed

    Ruiz, Patricia; Ingale, Kundan; Wheeler, John S; Mumtaz, Moiz

    2016-02-01

    Mono-hydroxylated polychlorinated biphenyls (OH-PCBs) are found in human biological samples and lack of data on their potential estrogenic activity has been a source of concern. We have extended our previous in silico 2D QSAR study through the application of advance techniques such as docking and 3D QSAR to gain insights into their estrogen receptor (ERα) binding. The results support our earlier findings that the hydroxyl group is the most important feature on the compounds; its position, orientation and surroundings in the structure are influential for the binding of OH-PCBs to ERα. This study has also revealed the following additional interactions that influence estrogenicity of these chemicals (a) the aromatic interactions of the biphenyl moieties with the receptor, (b) hydrogen bonding interactions of the p-hydroxyl group with key amino acids ARG394 and GLU353, (c) low or no electronegative substitution at para-positions of the p-hydroxyl group, (d) enhanced electrostatic interactions at the meta position on the B ring, and (e) co-planarity of the hydroxyl group on the A ring. In combination the 2D and 3D QSAR approaches have led us to the support conclusion that the hydroxyl group is the most important feature on the OH-PCB influencing the binding to estrogen receptors, and have enhanced our understanding of the mechanistic details of estrogenicity of this class of chemicals. Such in silico computational methods could serve as useful tools in risk assessment of chemicals. PMID:26598992

  2. More effective dithiocarbamate derivatives inhibiting carbonic anhydrases, generated by QSAR and computational design.

    PubMed

    Avram, Speranta; Milac, Adina Luminita; Carta, Fabrizio; Supuran, Claudiu T

    2013-04-01

    Dithiocarbamates (DTC) are promising compounds with potential applications in antitumoral and glaucoma therapy. Our aim is to understand molecular features affecting DTC interaction with carbonic anhydrases (CAs), zinc-containing enzymes maintaining acid-base balance in blood and other tissues. To this end, we generate QSAR models based on a compound series containing 25 DTC, inhibitors of four human (h) CAs isoforms: hCA I, II, IX and XII. We establish that critical physicochemical parameters for DTC inhibitory activity are: hydrophobic, electronic, steric, topological and shape. The predictive power of our QSAR models is indicated by significant values of statistical coefficients: cross-validated correlation q(2) (0.55-0.73), fitted correlation r(2) (0.75-0.84) and standard error of prediction (0.47-0.23). Based on the established QSAR equations, we analyse 22 new DTC derivatives and identify DTC dicarboxilic acids derivatives and their esters as potentially improved inhibitors of CA I, II, IX and XII. PMID:23116520

  3. Friend Flips: A Story Activity about Relationships

    ERIC Educational Resources Information Center

    Szucs, Leigh; Reyes, Jovanni V.; Farmer, Jennifer; Wilson, Kelly L.; McNeill, Elisa Beth

    2015-01-01

    Adolescents are influenced by the type, length and quality of the connections shared with different people throughout their lifespan. Relationships with peers, friends, and adults help to shape knowledge, attitudes, and beliefs related to health. Recognizing healthy or unhealthy characteristics allow youth to strengthen relationships and…

  4. Using Toxicological Evidence from QSAR Models in Practice

    EPA Science Inventory

    The new generation of QSAR models provides supporting documentation in addition to the predicted toxicological value. Such information enables the toxicologist to explore the properties of chemical substances and to review and increase the reliability of toxicity predictions. Thi...

  5. Molecular modeling studies of [6,6,5] Tricyclic Fused Oxazolidinones as FXa inhibitors using 3D-QSAR, Topomer CoMFA, molecular docking and molecular dynamics simulations.

    PubMed

    Xu, Cheng; Ren, Yujie

    2015-10-15

    Coagulation factor Xa (Factor Xa, FXa) is a particularly promising target for novel anticoagulant therapy. The first oral factor Xa inhibitor has been approved in the EU and Canada in 2008. In this work, 38 [6,6,5] Tricyclic Fused Oxazolidinones were studied using a combination of molecular modeling techniques including three-dimensional quantitative structure-activity relationship (3D-QSAR), molecular docking, molecular dynamics and Topomer CoMFA (comparative molecular field analysis) were used to build 3D-QSAR models. The results show that the best CoMFA model has q(2)=0.511 and r(2)=0.984, the best CoMSIA (comparative molecular similarity indices analysis) model has q(2)=0.700 and r(2)=0.993 and the Topomer CoMFA analysis has q(2)=0.377 and r(2)=0.886. The results indicated the steric, hydrophobic, H-acceptor and electrostatic fields play key roles in models. Molecular docking and molecular dynamics explored the binding relationship of the ligand and the receptor protein. PMID:26343829

  6. QSAR study of HCV NS5B polymerase inhibitors using the genetic algorithm-multiple linear regression (GA-MLR)

    PubMed Central

    Rafiei, Hamid; Khanzadeh, Marziyeh; Mozaffari, Shahla; Bostanifar, Mohammad Hassan; Avval, Zhila Mohajeri; Aalizadeh, Reza; Pourbasheer, Eslam

    2016-01-01

    Quantitative structure-activity relationship (QSAR) study has been employed for predicting the inhibitory activities of the Hepatitis C virus (HCV) NS5B polymerase inhibitors. A data set consisted of 72 compounds was selected, and then different types of molecular descriptors were calculated. The whole data set was split into a training set (80 % of the dataset) and a test set (20 % of the dataset) using principle component analysis. The stepwise (SW) and the genetic algorithm (GA) techniques were used as variable selection tools. Multiple linear regression method was then used to linearly correlate the selected descriptors with inhibitory activities. Several validation technique including leave-one-out and leave-group-out cross-validation, Y-randomization method were used to evaluate the internal capability of the derived models. The external prediction ability of the derived models was further analyzed using modified r2, concordance correlation coefficient values and Golbraikh and Tropsha acceptable model criteria's. Based on the derived results (GA-MLR), some new insights toward molecular structural requirements for obtaining better inhibitory activity were obtained. PMID:27065774

  7. Structure-biological function relationship extended to mitotic arrest-deficient 2-like protein Mad2 native and mutants-new opportunity for genetic disorder control.

    PubMed

    Avram, Speranta; Milac, Adina; Mernea, Maria; Mihailescu, Dan; Putz, Mihai V; Buiu, Catalin

    2014-01-01

    Overexpression of mitotic arrest-deficient proteins Mad1 and Mad2, two components of spindle assembly checkpoint, is a risk factor for chromosomal instability (CIN) and a trigger of many genetic disorders. Mad2 transition from inactive open (O-Mad2) to active closed (C-Mad2) conformations or Mad2 binding to specific partners (cell-division cycle protein 20 (Cdc20) or Mad1) were targets of previous pharmacogenomics studies. Here, Mad2 binding to Cdc20 and the interconversion rate from open to closed Mad2 were predicted and the molecular features with a critical contribution to these processes were determined by extending the quantitative structure-activity relationship (QSAR) method to large-size proteins such as Mad2. QSAR models were built based on available published data on 23 Mad2 mutants inducing CIN-related functional changes. The most relevant descriptors identified for predicting Mad2 native and mutants action mechanism and their involvement in genetic disorders are the steric (van der Waals area and solvent accessible area and their subdivided) and energetic van der Waals energy descriptors. The reliability of our QSAR models is indicated by significant values of statistical coefficients: Cross-validated correlation q2 (0.53-0.65) and fitted correlation r2 (0.82-0.90). Moreover, based on established QSAR equations, we rationally design and analyze nine de novo Mad2 mutants as possible promoters of CIN. PMID:25411801

  8. 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

  9. Binding studies and quantitative structure-activity relationship of 3-amino-1H-indazoles as inhibitors of GSK3β.

    PubMed

    Caballero, Julio; Zilocchi, Szymon; Tiznado, William; Collina, Simona; Rossi, Daniela

    2011-10-01

    Docking of 3-amino-1H-indazoles complexed with glycogen synthase kinase 3 beta (GSK3β) was performed to gain insight into the structural requirements and preferred conformations of these inhibitors. The study was conducted on a selected set of 57 compounds with variation in structure and activity. We found that the most active compounds established three hydrogen bonds with the residues of the hinge region of GSK3β, but some of the less active compounds have other binding modes. In addition, models able to predict GSK3β inhibitory activities (IC(50) ) of the studied compounds were obtained by 3D-QSAR methods CoMFA and CoMSIA. Ligand-based and receptor-guided alignment methods were utilized. Adequate R(2) and Q(2) values were obtained by each method, although some striking differences existed between the obtained contour maps. Each of the predictive models exhibited a similar ability to predict the activity of a test set. The application of docking and quantitative structure-activity relationship together allowed conclusions to be drawn for the choice of suitable GSK3β inhibitors. PMID:21756288

  10. 2-AZETIDINONE DERIVATIVES: SYNTHESIS, ANTIMICROBIAL, ANTICANCER EVALUATION AND QSAR STUDIES.

    PubMed

    Deep, Aakash; Kumar, Pradeep; Narasimhan, Balasubramanian; Lim, Siong Meng; Ramasamy, Kalavathy; Mishra, Rakesh Kumar; Mani, Vasudevan

    2016-01-01

    A series of 2-azetidinone derivatives was synthesized from hippuric acid and evaluated for its in vitro antimicrobial and anticancer activities. Antimicrobial properties of the title compounds were investigated against Gram positive and Gram negative bacterial as well as fungal strains. Anticancer activity was performed against breast cancer (MCF7) cell lines. Antimicrobial activity results revealed that N-{2-[3-chloro-2-(2- chlorophenyl)-4-oxoazetidin-1-ylamino]-2-oxoethyl}benzamide (4) was found to be the most potent antimicrobial agent. Results of anticancer study indicated that the synthesized compounds exhibited average anticancer potential and N-[2-(3-chloro-2-oxo-4-styrylazetidin-1-ylamino)-2-oxoethyl]benzamide (17) was found to be most potent anticancer agent against breast cancer (MCF7) cell lines. QSAR models indicated that the antibacterial, antifungal and the overall antimicrobial activities of the synthesized compounds were governed by topological parameters, Balaban index (J) and valence zero and first order molecular connectivity indices (⁰χv and ¹χv). PMID:27008802

  11. QSAR models for removal rates of organic pollutants adsorbed by in situ formed manganese dioxide under acid condition.

    PubMed

    Su, Pingru; Zhu, Huicen; Shen, Zhemin

    2016-02-01

    Manganese dioxide formed in oxidation process by potassium permanganate exhibits promising adsorptive capacity which can be utilized to remove organic pollutants in wastewater. However, the structure variances of organic molecules lead to wide difference of adsorption efficiency. Therefore, it is of great significance to find a general relationship between removal rate of organic compounds and their quantum parameters. This study focused on building up quantitative structure activity relationship (QSAR) models based on experimental removal rate (r(exp)) of 25 organic compounds and 17 quantum parameters of each organic compounds computed by Gaussian 09 and Material Studio 6.1. The recommended model is rpre = -0.502-7.742 f(+)x + 0.107 E HOMO + 0.959 q(H(+)) + 1.388 BOx. Both internal and external validations of the recommended model are satisfied, suggesting optimum stability and predictive ability. The definition of applicability domain and the Y-randomization test indicate all the prediction is reliable and no possibility of chance correlation. The recommended model contains four variables, which are closely related to adsorption mechanism. f(+)x reveals the degree of affinity for nucleophilic attack. E HOMO represents the difficulty of electron loss. q(H(+)) reflect the distribution of partial charge between carbon and hydrogen atom. BO x shows the stability of a molecule. PMID:26490942

  12. Molecular modelling on small molecular CDK2 inhibitors: an integrated approach using a combination of molecular docking, 3D-QSAR and pharmacophore modelling.

    PubMed

    Yuan, H; Liu, H; Tai, W; Wang, F; Zhang, Y; Yao, S; Ran, T; Lu, S; Ke, Z; Xiong, X; Xu, J; Chen, Y; Lu, T

    2013-10-01

    Cyclin-dependent kinase 2 (CDK2) has been identified as an important target for developing novel anticancer agents. Molecular docking, three-dimensional quantitative structure-activity relationship (3D-QSAR) and pharmacophore modelling were combined with the ultimate goal of studying the structure-activity relationship of CDK2 inhibitors. The comparative molecular similarity indices analysis (CoMSIA) model constructed based on a set of 3-aminopyrazole derivatives as CDK2 inhibitors gave statistically significant results (q (2) = 0.700; r (2) = 0.982). A HypoGen pharmacophore model, constructed using diverse CDK2 inhibitors, also showed significant statistics ([Formula: see text]Cost = 61.483; RMSD = 0.53; Correlation coefficient = 0.98). The small residues and error values between the estimated and experimental activities of the training and test set compounds proved their strong capability of activity prediction. The structural insights obtained from these two models were consistent with each other. The pharmacophore model summarized the important pharmacophoric features required for protein-ligand binding. The 3D contour maps in combination with the comprehensive pharmacophoric features helped to better interpret the structure-activity relationship. The results will be beneficial for the discovery and design of novel CDK2 inhibitors. The simplicity of this approach provides expansion to its applicability in optimizing other classes of small molecular CDK2 inhibitors. PMID:23941641

  13. Synthesis, biological activities and structure-activity relationships for new avermectin analogues.

    PubMed

    Zhang, Jian; Nan, Xiang; Yu, Hai-Tao; Cheng, Pi-Le; Zhang, Yan; Liu, Ying-Qian; Zhang, Shao-Yong; Hu, Guan-Fang; Liu, Huanxiang; Chen, An-Liang

    2016-10-01

    In an effort to discover new molecules with good insecticidal activities, more than 40 new avermectin derivatives were synthesized and evaluated for their biological activities against three species of arachnids, insects and nematodes, namely, Tetranychus Cinnabarinus, Aphis craccivora and Bursaphelenchus xylophilus. All the tested compounds showed potent inhibitory activities against three insect species. Notably, the majority of compounds exhibited high selectivity against T. cinnabarinus, some of which were much better in comparison with avermectin. Especially compounds 9j (LC50: 0.005 μM) and 16d (LC50: 0.002 μM) were 2.5- and 4.7-fold more active than avermectin (LC50: 0.013 μM), respectively, against T. cinnabarinus. Moreover, compounds 9b, 9d-f, 9h, 9j, 9l, 9n, 9p, 9r, 9v and 17d showed superior activities with LC50 values of 2.959-5.013 μM compared to that of 1 (LC50: 6.746 μM) against B. xylophilus. Meanwhile, the insecticidal activities of compounds 9f, 9g, 9h, and 9m against A. craccivora were 7-8 times better than that of avermectin, with LC50 values of 7.744, 5.634, 6.809, 7.939 and 52.234 μM, respectively. Furthermore, QSAR analysis showed that the molecular shape, size, connectivity degree and electronic distribution of avermectin analogues had substantial effects on insecticidal potency. These preliminary results provided useful insight in guiding further modifications of avermectin in the development of potential new insecticides. PMID:27318119

  14. QSAR model for human pregnane X receptor (PXR) binding: Screening of environmental chemicals and correlations with genotoxicity, endocrine disruption and teratogenicity

    SciTech Connect

    Dybdahl, Marianne Nikolov, Nikolai G.; Wedebye, Eva Bay; Jónsdóttir, Svava Ósk; Niemelä, Jay R.

    2012-08-01

    The pregnane X receptor (PXR) has a key role in regulating the metabolism and transport of structurally diverse endogenous and exogenous compounds. Activation of PXR has the potential to initiate adverse effects, causing drug–drug interactions, and perturbing normal physiological functions. Therefore, identification of PXR ligands would be valuable information for pharmaceutical and toxicological research. In the present study, we developed a quantitative structure–activity relationship (QSAR) model for the identification of PXR ligands using data based on a human PXR binding assay. A total of 631 molecules, representing a variety of chemical structures, constituted the training set of the model. Cross-validation of the model showed a sensitivity of 82%, a specificity of 85%, and a concordance of 84%. The developed model provided knowledge about molecular descriptors that may influence the binding of molecules to PXR. The model was used to screen a large inventory of environmental chemicals, of which 47% was found to be within domain of the model. Approximately 35% of the chemicals within domain were predicted to be PXR ligands. The predicted PXR ligands were found to be overrepresented among chemicals predicted to cause adverse effects, such as genotoxicity, teratogenicity, estrogen receptor activation and androgen receptor antagonism compared to chemicals not causing these effects. The developed model may be useful as a tool for predicting potential PXR ligands and for providing mechanistic information of toxic effects of chemicals. -- Highlights: ► Global QSAR model for the identification of PXR ligands was developed. ► Molecular descriptors that may influence PXR binding were identified. ► 35% of a large set of environmental chemicals were predicted to be PXR ligands. ► Predicted PXR binding was associated with various adverse effects.

  15. THE PRACTICE OF STRUCTURE ACTIVITY RELATIONSHIPS (SAR) IN TOXICOLOGY

    EPA Science Inventory

    Both qualitative and quantitative modeling methods relating chemical structure to biological activity, called structure-activity relationship analyses or SAR, are applied to the prediction and characterization of chemical toxicity. This minireview will discuss some generic issue...

  16. Identification of the Structural Features of Guanine Derivatives as MGMT Inhibitors Using 3D-QSAR Modeling Combined with Molecular Docking.

    PubMed

    Sun, Guohui; Fan, Tengjiao; Zhang, Na; Ren, Ting; Zhao, Lijiao; Zhong, Rugang

    2016-01-01

    DNA repair enzyme O⁶-methylguanine-DNA methyltransferase (MGMT), which plays an important role in inducing drug resistance against alkylating agents that modify the O⁶ position of guanine in DNA, is an attractive target for anti-tumor chemotherapy. A series of MGMT inhibitors have been synthesized over the past decades to improve the chemotherapeutic effects of O⁶-alkylating agents. In the present study, we performed a three-dimensional quantitative structure activity relationship (3D-QSAR) study on 97 guanine derivatives as MGMT inhibitors using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methods. Three different alignment methods (ligand-based, DFT optimization-based and docking-based alignment) were employed to develop reliable 3D-QSAR models. Statistical parameters derived from the models using the above three alignment methods showed that the ligand-based CoMFA (Qcv² = 0.672 and Rncv² = 0.997) and CoMSIA (Qcv² = 0.703 and Rncv² = 0.946) models were better than the other two alignment methods-based CoMFA and CoMSIA models. The two ligand-based models were further confirmed by an external test-set validation and a Y-randomization examination. The ligand-based CoMFA model (Qext² = 0.691, Rpred² = 0.738 and slope k = 0.91) was observed with acceptable external test-set validation values rather than the CoMSIA model (Qext² = 0.307, Rpred² = 0.4 and slope k = 0.719). Docking studies were carried out to predict the binding modes of the inhibitors with MGMT. The results indicated that the obtained binding interactions were consistent with the 3D contour maps. Overall, the combined results of the 3D-QSAR and the docking obtained in this study provide an insight into the understanding of the interactions between guanine derivatives and MGMT protein, which will assist in designing novel MGMT inhibitors with desired activity. PMID:27347909

  17. Binary classification of a large collection of environmental chemicals from estrogen receptor assays by quantitative structure-activity relationship and machine learning methods.

    PubMed

    Zang, Qingda; Rotroff, Daniel M; Judson, Richard S

    2013-12-23

    There are thousands of environmental chemicals subject to regulatory decisions for endocrine disrupting potential. The ToxCast and Tox21 programs have tested ∼8200 chemicals in a broad screening panel of in vitro high-throughput screening (HTS) assays for estrogen receptor (ER) agonist and antagonist activity. The present work uses this large data set to develop in silico quantitative structure-activity relationship (QSAR) models using machine learning (ML) methods and a novel approach to manage the imbalanced data distribution. Training compounds from the ToxCast project were categorized as active or inactive (binding or nonbinding) classes based on a composite ER Interaction Score derived from a collection of 13 ER in vitro assays. A total of 1537 chemicals from ToxCast were used to derive and optimize the binary classification models while 5073 additional chemicals from the Tox21 project, evaluated in 2 of the 13 in vitro assays, were used to externally validate the model performance. In order to handle the imbalanced distribution of active and inactive chemicals, we developed a cluster-selection strategy to minimize information loss and increase predictive performance and compared this strategy to three currently popular techniques: cost-sensitive learning, oversampling of the minority class, and undersampling of the majority class. QSAR classification models were built to relate the molecular structures of chemicals to their ER activities using linear discriminant analysis (LDA), classification and regression trees (CART), and support vector machines (SVM) with 51 molecular descriptors from QikProp and 4328 bits of structural fingerprints as explanatory variables. A random forest (RF) feature selection method was employed to extract the structural features most relevant to the ER activity. The best model was obtained using SVM in combination with a subset of descriptors identified from a large set via the RF algorithm, which recognized the active and

  18. Using ToxCast in vitro Assays in the Hierarchical Quantitative Structure-Activity Relationship (QSAR) Modeling for Predicting in vivo Toxicity of Chemicals

    EPA Science Inventory

    The goal of chemical toxicology research is utilizing short term bioassays and/or robust computational methods to predict in vivo toxicity endpoints for chemicals. The ToxCast program established at the US Environmental Protection Agency (EPA) is addressing this goal by using ca....

  19. Quantitative structure-activity relationship of the curcumin-related compounds using various regression methods

    NASA Astrophysics Data System (ADS)

    Khazaei, Ardeshir; Sarmasti, Negin; Seyf, Jaber Yousefi

    2016-03-01

    Quantitative structure activity relationship were used to study a series of curcumin-related compounds with inhibitory effect on prostate cancer PC-3 cells, pancreas cancer Panc-1 cells, and colon cancer HT-29 cells. Sphere exclusion method was used to split data set in two categories of train and test set. Multiple linear regression, principal component regression and partial least squares were used as the regression methods. In other hand, to investigate the effect of feature selection methods, stepwise, Genetic algorithm, and simulated annealing were used. In two cases (PC-3 cells and Panc-1 cells), the best models were generated by a combination of multiple linear regression and stepwise (PC-3 cells: r2 = 0.86, q2 = 0.82, pred_r2 = 0.93, and r2m (test) = 0.43, Panc-1 cells: r2 = 0.85, q2 = 0.80, pred_r2 = 0.71, and r2m (test) = 0.68). For the HT-29 cells, principal component regression with stepwise (r2 = 0.69, q2 = 0.62, pred_r2 = 0.54, and r2m (test) = 0.41) is the best method. The QSAR study reveals descriptors which have crucial role in the inhibitory property of curcumin-like compounds. 6ChainCount, T_C_C_1, and T_O_O_7 are the most important descriptors that have the greatest effect. With a specific end goal to design and optimization of novel efficient curcumin-related compounds it is useful to introduce heteroatoms such as nitrogen, oxygen, and sulfur atoms in the chemical structure (reduce the contribution of T_C_C_1 descriptor) and increase the contribution of 6ChainCount and T_O_O_7 descriptors. Models can be useful in the better design of some novel curcumin-related compounds that can be used in the treatment of prostate, pancreas, and colon cancers.

  20. Three-dimensional quantitative structure–activity relationship and docking studies in a series of anthocyanin derivatives as cytochrome P450 3A4 inhibitors

    PubMed Central

    Shityakov, Sergey; Puskás, István; Roewer, Norbert; Förster, Carola; Broscheit, Jens

    2014-01-01

    The cytochrome P450 (CYP)3A4 enzyme affects the metabolism of most drug-like substances, and its inhibition may influence drug safety. Modulation of CYP3A4 by flavonoids, such as anthocyanins, has been shown to inhibit the mutagenic activity of mammalian cells. Considering the previous investigations addressing CYP3A4 inhibition by these substances, we studied the three-dimensional quantitative structure–activity relationship (3D-QSAR) in a series of anthocyanin derivatives as CYP3A4 inhibitors. For the training dataset (n=12), comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) yielded crossvalidated and non-crossvalidated models with a q2 of 0.795 (0.687) and r2 of 0.962 (0.948), respectively. The models were also validated by an external test set of four compounds with r2 of 0.821 (CoMFA) and r2 of 0.812 (CoMSIA). The binding affinity modes associated with experimentally derived IC50 (half maximal inhibitory concentration) values were confirmed by molecular docking into the CYP3A4 active site with r2 of 0.66. The results obtained from this study are useful for a better understanding of the effects of anthocyanin derivatives on inhibition of carcinogen activation and cellular DNA damage. PMID:24741320

  1. Exploring clotrimazole-based pharmacophore: 3D-QSAR studies and synthesis of novel antiplasmodial agents.

    PubMed

    Brogi, Simone; Brindisi, Margherita; Joshi, Bhupendra P; Sanna Coccone, Salvatore; Parapini, Silvia; Basilico, Nicoletta; Novellino, Ettore; Campiani, Giuseppe; Gemma, Sandra; Butini, Stefania

    2015-11-15

    We report herein the generation and validation of a 3D-QSAR model based on a set of antimalarials previously described by us and characterized by a clotrimazole-based pharmacophore. A novel series of derivatives was synthesized and showed activity against Plasmodium falciparum chloroquine-sensitive (CQ-S) and chloroquine-resistant (CQ-R) strains. Gratifyingly, compounds 35a-c showed interesting activity against P. falciparum CQ-R strains with improved predicted physico-chemical properties. PMID:26428874

  2. Prediction of aromatic amine carcinogenicity: QSAR base on calculated delocalizibility of hypothetical nitrenium ion intermediate

    SciTech Connect

    Purdy, R.

    1995-12-31

    Predictors for the reactivity of primary aromatic amines were hypothesized and tested on a small set of amines. It was found that the delocalizibility on the nitrogen of the previously hypothesized nitrenium ion intermediate was the only good predictor. The strength of this predictor was tested on a larger set of amines and a cut off value for discriminating between carcinogens and noncarcinogens was chosen. This QSAR supports the hypothesis that a nitrenium ion is an intermediate in the activation of primary aromatic amines to active carcinogens.

  3. Some Phthalocyanine and Naphthalocyanine Derivatives as Corrosion Inhibitors for Aluminium in Acidic Medium: Experimental, Quantum Chemical Calculations, QSAR Studies and Synergistic Effect of Iodide Ions.

    PubMed

    Dibetsoe, Masego; Olasunkanmi, Lukman O; Fayemi, Omolola E; Yesudass, Sasikumar; Ramaganthan, Baskar; Bahadur, Indra; Adekunle, Abolanle S; Kabanda, Mwadham M; Ebenso, Eno E

    2015-01-01

    The effects of seven macrocyclic compounds comprising four phthalocyanines (Pcs) namely 1,4,8,11,15,18,22,25-octabutoxy-29H,31H-phthalocyanine (Pc1), 2,3,9,10,16,17,23,24-octakis(octyloxy)-29H,31H-phthalocyanine (Pc2), 2,9,16,23-tetra-tert-butyl-29H,31H-phthalocyanine (Pc3) and 29H,31H-phthalocyanine (Pc4), and three naphthalocyanines namely 5,9,14,18,23,27,32,36-octabutoxy-2,3-naphthalocyanine (nPc1), 2,11,20,29-tetra-tert-butyl-2,3-naphthalocyanine (nPc2) and 2,3-naphthalocyanine (nP3) were investigated on the corrosion of aluminium (Al) in 1 M HCl using a gravimetric method, potentiodynamic polarization technique, quantum chemical calculations and quantitative structure activity relationship (QSAR). Synergistic effects of KI on the corrosion inhibition properties of the compounds were also investigated. All the studied compounds showed appreciable inhibition efficiencies, which decrease with increasing temperature from 30 °C to 70 °C. At each concentration of the inhibitor, addition of 0.1% KI increased the inhibition efficiency compared to the absence of KI indicating the occurrence of synergistic interactions between the studied molecules and I(-) ions. From the potentiodynamic polarization studies, the studied Pcs and nPcs are mixed type corrosion inhibitors both without and with addition of KI. The adsorption of the studied molecules on Al surface obeys the Langmuir adsorption isotherm, while the thermodynamic and kinetic parameters revealed that the adsorption of the studied compounds on Al surface is spontaneous and involves competitive physisorption and chemisorption mechanisms. The experimental results revealed the aggregated interactions between the inhibitor molecules and the results further indicated that the peripheral groups on the compounds affect these interactions. The calculated quantum chemical parameters and the QSAR results revealed the possibility of strong interactions between the studied inhibitors and metal surface. QSAR analysis on the

  4. Predictive Modeling of Antioxidant Coumarin Derivatives Using Multiple Approaches: Descriptor-Based QSAR, 3D-Pharmacophore Mapping, and HQSAR.

    PubMed

    Mitra, Indrani; Saha, Achintya; Roy, Kunal

    2013-03-01

    The inability of the systemic antioxidants to alleviate the exacerbation of free radical formation from metabolic outputs and environmental pollutants claims an urgent demand for the identification and design of new chemical entities with potent antioxidant activity. In the present work, different QSAR approaches have been utilized for identifying the essential structural attributes imparting a potential antioxidant activity profile of the coumarin derivatives. The descriptor-based QSAR model provides a quantitative outline regarding the structural prerequisites of the molecules, while 3D pharmacophore and HQSAR models emphasize the favourable spatial arrangement of the various chemical features and the crucial molecular fragments, respectively. All the models infer that the fused benzene ring and the oxygen atom of the pyran ring constituting the parent coumarin nucleus capture the prime pharmacophoric features, imparting superior antioxidant activity to the molecules. The developed models may serve as indispensable query tools for screening untested molecules belonging to the class of coumarin derivatives. PMID:23641329

  5. Tuning hERG out: Antitarget QSAR Models for Drug Development

    PubMed Central

    Braga, Rodolpho C.; Alves, Vinícius M.; Silva, Meryck F. B.; Muratov, Eugene; Fourches, Denis; Tropsha, Alexander; Andrade, Carolina H.

    2015-01-01

    Several non-cardiovascular drugs have been withdrawn from the market due to their inhibition of hERG K+ channels that can potentially lead to severe heart arrhythmia and death. As hERG safety testing is a mandatory FDA-required procedure, there is a considerable interest for developing predictive computational tools to identify and filter out potential hERG blockers early in the drug discovery process. In this study, we aimed to generate predictive and well-characterized quantitative structure–activity relationship (QSAR) models for hERG blockage using the largest publicly available dataset of 11,958 compounds from the ChEMBL database. The models have been developed and validated according to OECD guidelines using four types of descriptors and four different machine-learning techniques. The classification accuracies discriminating blockers from non-blockers were as high as 0.83–0.93 on external set. Model interpretation revealed several SAR rules, which can guide structural optimization of some hERG blockers into non-blockers. We have also applied the generated models for screening the World Drug Index (WDI) database and identify putative hERG blockers and non-blockers among currently marketed drugs. The developed models can reliably identify blockers and non-blockers, which could be useful for the scientific community. A freely accessible web server has been developed allowing users to identify putative hERG blockers and non-blockers in chemical libraries of their interest (http://labmol.farmacia.ufg.br/predherg). PMID:24805060

  6. The binding of herbicidal halovinyl anilides to the photosystem II Q sub B site and the relationship between affinities and molecular characteristics

    SciTech Connect

    Eilers, R.J.; Crouse, G.D.; Durst, G.L.; Streusand, V.J.; Manly, C.J.; Webster, J.D. )

    1990-05-01

    A new class of herbicidal halovinyl anilides, which inhibit photosynthetic electron transport, have been shown to inhibit {sup 14}C-atrazine binding in spinach thylakoid membranes. A scatchard analysis of the {sup 14}C-atrazine binding inhibition of the lead compound, LY221204, has shown it to be a competitive inhibitor. Preliminary QSAR (quantitative structure activity relationship) studies suggested that 75-80% of the variance in vivo activity could be explained by size and electronic properties and that activity increased with smaller and more electron releasing substituents. To analyze the effects of these properties on intrinsic activity, a larger QSAR study was undertaken. Atrazine binding inhibition data was generated for a group of substituted, non-conjugated vinyl anilides at 1 and 10 {mu}M concentrations and plotted as a function of physicochemical parameters. The results will be presented.

  7. QSAR based docking studies of marine algal anticancer compounds as inhibitors of protein kinase B (PKBβ).

    PubMed

    Davis, G Dicky John; Vasanthi, A Hannah Rachel

    2015-08-30

    Marine algae are prolific source of bioactive secondary metabolites and are found to be active against different cancer cell lines. QSAR studies will explicate the significance of a particular class of descriptor in eliciting anticancer activity against a cancer type. Marine algal compounds showing anticancer activity against six different cancer cell lines namely MCF-7, A431, HeLa, HT-29, P388 and A549 taken from Seaweed metabolite database were subjected to comprehensive QSAR modeling studies. A hybrid-GA (genetic algorithm) optimization technique for descriptor space reduction and multiple linear regression analysis (MLR) approach was used as fitness functions. Cell lines HeLa and MCF-7 showed good statistical quality (R(2)∼0.75, Q(2)∼0.65) followed by A431, HT29 and P388 cell lines with reasonable statistical values (R(2)∼0.70, Q(2)∼0.60). The models developed were interpretable, with good statistical and predictive significance. Molecular descriptor analyses revealed that Baumann's alignment-independent topological descriptors had a major role in variation of activity along with other descriptors. Incidentally, earlier QSAR analysis on a variety of chemically diverse PKBα inhibitors revealed Baumann's alignment-independent topological descriptors that differentiated the molecules binding to Protein kinase B (PKBα) kinase or PH domain, hence a docking study of two crystal structures of PKBβ was performed for identification of novel ATP-competitive inhibitors of PKBβ. Five compounds had a good docking score and Callophycin A showed better ligand efficiency than other PKBβ inhibitors. Furthermore in silico pharmacokinetic and toxicity studies also showed that Callophycin A had a high drug score (0.85) compared to the other inhibitors. These results encourages discovering novel inhibitors for cancer therapeutic targets by screening metabolites from marine algae. PMID:25936945

  8. Antibacterial activity of N-benzylsalicylthioamides.

    PubMed

    Petrlíková, E; Waisser, K; Jílek, P; Dufková, I

    2010-09-01

    The in-vitro biological activity of N-benzylsalicylthioamides against 8 bacterial strains was determined by broth microdilution method; results were compared with those obtained with neomycin, penicillin G, ciprofloxacin and penicillin V. The compounds showed moderate to high activity against G(+) bacteria; especially compounds 4, 6, 13, 16-21 and 24 exhibited comparable or higher activity than reference drugs. The antibacterial activity was analyzed by quantitative structure-activity relationship (QSAR). The antibacterial activity increased with lipophilicity, with the presence of halogens and with increasing value of Hammet substituent constant σ. PMID:20941574

  9. 3D-QSAR and molecular docking studies on designing inhibitors of the hepatitis C virus NS5B polymerase

    NASA Astrophysics Data System (ADS)

    Li, Wenlian; Si, Hongzong; Li, Yang; Ge, Cuizhu; Song, Fucheng; Ma, Xiuting; Duan, Yunbo; Zhai, Honglin

    2016-08-01

    Viral hepatitis C infection is one of the main causes of the hepatitis after blood transfusion and hepatitis C virus (HCV) infection is a global health threat. The HCV NS5B polymerase, an RNA dependent RNA polymerase (RdRp) and an essential role in the replication of the virus, has no functional equivalent in mammalian cells. So the research and development of efficient NS5B polymerase inhibitors provides a great strategy for antiviral therapy against HCV. A combined three-dimensional quantitative structure-activity relationship (QSAR) modeling was accomplished to profoundly understand the structure-activity correlation of a train of indole-based inhibitors of the HCV NS5B polymerase to against HCV. A comparative molecular similarity indices analysis (COMSIA) model as the foundation of the maximum common substructure alignment was developed. The optimum model exhibited statistically significant results: the cross-validated correlation coefficient q2 was 0.627 and non-cross-validated r2 value was 0.943. In addition, the results of internal validations of bootstrapping and Y-randomization confirmed the rationality and good predictive ability of the model, as well as external validation (the external predictive correlation coefficient rext2 = 0.629). The information obtained from the COMSIA contour maps enables the interpretation of their structure-activity relationship. Furthermore, the molecular docking study of the compounds for 3TYV as the protein target revealed important interactions between active compounds and amino acids, and several new potential inhibitors with higher activity predicted were designed basis on our analyses and supported by the simulation of molecular docking. Meanwhile, the OSIRIS Property Explorer was introduced to help select more satisfactory compounds. The satisfactory results from this study may lay a reliable theoretical base for drug development of hepatitis C virus NS5B polymerase inhibitors.

  10. The relationship between chitotriosidase activity and tuberculosis.

    PubMed

    Chen, M; Deng, J; Li, W; Su, C; Xia, Y; Wang, M; Li, X; Abuaku, B K; Tan, H; Wen, S W

    2015-11-01

    Chitotriosidase, secreted by activated macrophages, is a biomarker of activated macrophages. In this study, we explored whether chitotriosidase could be adopted as a biomarker to evaluate the curative effect on tuberculosis (TB). Five counties were randomly selected out of 122 counties/cities/districts in Hunan Province, China. Our cases were all TB patients who were newly diagnosed or had been receiving treatment at the Centers for Disease Control (CDCs) of these five counties between April and August in 2009. Healthy controls were selected from a community health facility in the Kaifu district of Changsha City after frequency-matching of gender and age with the cases. Chitotriosidase activity was evaluated by a fluorometric assay. Categorical variables were analysed with the χ 2 test. Measurement data in multiple groups were tested with analysis of variance and least significant difference (LSD). Correlation between chitotriosidase activity and the degree of radiological extent (DRE) was examined by Spearman's rank correlation test. The average chitotriosidase activity levels of new TB cases, TB cases with different periods of treatment (6 months) and the control group were 54·47, 34·77, 21·54, 12·73 and 10·53 nmol/h.ml, respectively. Chitotriosidase activity in TB patients declined along with the continuity of treatment. The chitotriosidase activity of both smear-positive and the smear-negative pulmonary TB patients decreased after 6 months' treatment to normal levels (P < 0·05). Moreover, chitotriosidase activity was positively correlated with DRE (r = 0·607, P < 0·001). Our results indicate that chitotriosidase might be a marker of TB treatment effects. However, further follow-up study of TB patients is needed in the future. PMID:26418349

  11. Possible relationships between solar activity and meteorological phenomena

    NASA Technical Reports Server (NTRS)

    Bandeen, W. R. (Editor); Maran, S. P. (Editor)

    1975-01-01

    A symposium was conducted in which the following questions were discussed: (1) the evidence concerning possible relationships between solar activity and meteorological phenomena; (2) plausible physical mechanisms to explain these relationships; and (3) kinds of critical measurements needed to determine the nature of solar/meteorological relationships and/or the mechanisms to explain them, and which of these measurements can be accomplished best from space.

  12. QSAR modeling and molecular interaction analysis of natural compounds as potent neuraminidase inhibitors.

    PubMed

    Sun, Jiaying; Mei, Hu

    2016-04-26

    Different QSAR models of 40 natural compounds as neuraminidase inhibitors (NIs) are developed to comprehend chemical-biological interactions and predict activities against neuraminidase (NA) from Clostridium perfringens. Based on the constitutional, topological and conformational descriptors, R(2) and Q(2) values of the obtained SRA model are 0.931 and 0.856. The R(2) and Q(2) values of the constructed HQSAR and almond models are 0.903 and 0.767, 0.904 and 0.511, respectively. Based on the pharmacophore alignment, R(2) and Q(2) values of the optimal CoMSIA model are 0.936 and 0.654. Moreover, Rtest(2) and Qext(2) of values of SRA, HQSAR, almond and CoMSIA models are 0.611 and 0.565, 0.753 and 0.750, 0.612 and 0.582, 0.582 and 0.571, respectively. So, QSAR models have good predictive capability. They can be further used to evaluate and screen new compounds. Moreover, hydrogen bonds and electrostatic factors have high contributions to activities. To understand molecular interactions between natural compounds and NA from Clostridium perfringens, molecular docking is investigated. The docking results elucidate that Arg266, Asp291, Asp328, Tyr485, Glu493, Arg555, Arg615 and Tyr655 are especially the key residues in the active site of 2bf6. Hydrogen bonds and electrostatics are key factors, which impact the interactions between NIs and NA. So, the influential factors of interactions between NIs and NA in the docking results are in agreement with the QSAR results. PMID:27008437

  13. Young Adolescents' Perceptions of Romantic Relationships and Sexual Activity

    ERIC Educational Resources Information Center

    Royer, Heather R.; Keller, Mary L.; Heidrich, Susan M.

    2009-01-01

    The purpose of this article is to describe young adolescents' perceptions of romantic relationships, ratings of important romantic partner characteristics, and acceptability of sexual activity with romantic relationships. Fifty-seven eighth-grade participants (average age = 13.8 years) from one urban US public middle school completed an anonymous…

  14. Development of classification model and QSAR model for predicting binding affinity of endocrine disrupting chemicals to human sex hormone-binding globulin.

    PubMed

    Liu, Huihui; Yang, Xianhai; Lu, Rui

    2016-08-01

    Disturbing the transport process is a crucial pathway for endocrine disrupting chemicals (EDCs) to disrupt endocrine function. However, this mechanism has not gotten enough attention, compared with that of hormone receptors and synthetase up to now, especially for the sex hormone transport process. In this study, we selected sex hormone-binding globulin (SHBG) and EDCs as a model system and the relative competing potency of a chemical with testosterone binding to SHBG (log RBA) as the endpoints, to develop classification models and quantitative structure-activity relationship (QSAR) models. With the classification model, a satisfactory model with nR09, nR10 and RDF155v as the most relevant variables was screened. Statistic results indicated that the model had the sensitivity, specificity, accuracy of 86.4%, 80.0%, 84.4% and 85.7%, 87.5%, 86.2% for the training set and validation set, respectively, highlighting a high classification performance of the model. With the QSAR model, a satisfactory model with statistical parameters, specifically, an adjusted determination coefficient (Radj(2)) of 0.810, a root mean square error (RMSE) of 0.616, a leave-one-out cross-validation squared correlation coefficient (QLOO(2)) of 0.777, a bootstrap method (QBOOT(2)) of 0.756, an external validation coefficient (Qext(2)) of 0.544 and a RMSEext of 0.859, were obtained, which implied satisfactory goodness of fit, robustness and predictive ability. The applicability domain of the current model covers a large number of structurally diverse chemicals, especially a few classes of nonsteroidal compounds. PMID:27156209

  15. 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. PMID:25773984

  16. Social Relationships, Leisure Activity, and Health in Older Adults

    PubMed Central

    Chang, Po-Ju; Wray, Linda; Lin, Yeqiang

    2015-01-01

    Objective Although the link between enhanced social relationships and better health has generally been well established, few studies have examined the role of leisure activity in this link. This study examined how leisure influences the link between social relationships and health in older age. Methods Using data from the 2006 and 2010 waves of the nationally representative U.S. Health and Retirement Study and structural equation modelling analyses, we examined data on 2,965 older participants to determine if leisure activities mediated the link between social relationships and health in 2010, controlling for race, education level, and health in 2006. Results The results demonstrated that leisure activities mediate the link between social relationships and health in these age groups. Perceptions of positive social relationships were associated with greater involvement in leisure activities, and greater involvement in leisure activities was associated with better health in older age. Discussion & Conclusions The contribution of leisure to health in these age groups is receiving increasing attention, and the results of this study add to the literature on this topic, by identifying the mediating effect of leisure activity on the link between social relationships and health. Future studies aimed at increasing leisure activity may contribute to improved health outcomes in older adults. PMID:24884905

  17. Relationships between Interlibrary Loan and Research Activity in Canada

    ERIC Educational Resources Information Center

    Duy, Joanna; Larivière, Vincent

    2014-01-01

    Interlibrary Loan borrowing rates in academic libraries are influenced by an array of factors. This article explores the relationship between interlibrary loan borrowing activity and research activity at 42 Canadian academic institutions. A significant positive correlation was found between interlibrary loan borrowing activity and measures of…

  18. Relationships between Reading Activities and Language Use.

    ERIC Educational Resources Information Center

    Gordon, Sandra L.; Van Dongen, Richard

    1988-01-01

    Noting that the ways children encounter and use print in the classroom can be examined as surface and organizing content of curriculum, this article provides descriptions of innovative uses of print in the kindergarten and elementary school classroom. Curriculum "surface content" includes activities, use of classroom space, display, and materials…

  19. DEVELOPMENT OF STRUCTURE ACTIVITY RELATIONSHIPS FOR ASSESSING ECOLOGICAL RISKS

    EPA Science Inventory

    In the field of environmental toxicology, structure activity relationships (SARs) have developed as scientifically-credible tools for predicting the effects of chemicals when little or no empirical data are available.

  20. In Silico Prediction of Cytochrome P450-Drug Interaction: QSARs for CYP3A4 and CYP2C9

    PubMed Central

    Nembri, Serena; Grisoni, Francesca; Consonni, Viviana; Todeschini, Roberto

    2016-01-01

    Cytochromes P450 (CYP) are the main actors in the oxidation of xenobiotics and play a crucial role in drug safety, persistence, bioactivation, and drug-drug/food-drug interaction. This work aims to develop Quantitative Structure-Activity Relationship (QSAR) models to predict the drug interaction with two of the most important CYP isoforms, namely 2C9 and 3A4. The presented models are calibrated on 9122 drug-like compounds, using three different modelling approaches and two types of molecular description (classical molecular descriptors and binary fingerprints). For each isoform, three classification models are presented, based on a different approach and with different advantages: (1) a very simple and interpretable classification tree; (2) a local (k-Nearest Neighbor) model based classical descriptors and; (3) a model based on a recently proposed local classifier (N-Nearest Neighbor) on binary fingerprints. The salient features of the work are (1) the thorough model validation and the applicability domain assessment; (2) the descriptor interpretation, which highlighted the crucial aspects of P450-drug interaction; and (3) the consensus aggregation of models, which largely increased the prediction accuracy. PMID:27294921

  1. In Silico Prediction of Cytochrome P450-Drug Interaction: QSARs for CYP3A4 and CYP2C9.

    PubMed

    Nembri, Serena; Grisoni, Francesca; Consonni, Viviana; Todeschini, Roberto

    2016-01-01

    Cytochromes P450 (CYP) are the main actors in the oxidation of xenobiotics and play a crucial role in drug safety, persistence, bioactivation, and drug-drug/food-drug interaction. This work aims to develop Quantitative Structure-Activity Relationship (QSAR) models to predict the drug interaction with two of the most important CYP isoforms, namely 2C9 and 3A4. The presented models are calibrated on 9122 drug-like compounds, using three different modelling approaches and two types of molecular description (classical molecular descriptors and binary fingerprints). For each isoform, three classification models are presented, based on a different approach and with different advantages: (1) a very simple and interpretable classification tree; (2) a local (k-Nearest Neighbor) model based classical descriptors and; (3) a model based on a recently proposed local classifier (N-Nearest Neighbor) on binary fingerprints. The salient features of the work are (1) the thorough model validation and the applicability domain assessment; (2) the descriptor interpretation, which highlighted the crucial aspects of P450-drug interaction; and (3) the consensus aggregation of models, which largely increased the prediction accuracy. PMID:27294921

  2. Synthesis, antimalarial properties and 2D-QSAR studies of novel triazole-quinine conjugates.

    PubMed

    Faidallah, Hassan M; Panda, Siva S; Serrano, Juan C; Girgis, Adel S; Khan, Khalid A; Alamry, Khalid A; Therathanakorn, Tanya; Meyers, Marvin J; Sverdrup, Francis M; Eickhoff, Christopher S; Getchell, Stephen G; Katritzky, Alan R

    2016-08-15

    Click chemistry technique led to novel 1,2,3-triazole-quinine conjugates 8a-g, 10a-o, 11a-h and 13 utilizing benzotriazole-mediated synthetic approach with excellent yields. Some of the synthesized analogs (11a, 11d-h) exhibited antimalarial properties against Plasmodium falciparum strain 3D7 with potency higher than that of quinine (standard reference used) through in vitro standard procedure bio-assay. Statistically significant BMLR-QSAR model describes the bio-properties, validates the observed biological observations and identifies the most important parameters governing bio-activity. PMID:27298002

  3. A novel quantitative structure-activity relationship model for prediction of biomagnification factor of some organochlorine pollutants.

    PubMed

    Fatemi, Mohammad Hossein; Baher, Elham

    2009-08-01

    The biomagnification factor (BMF) is an important property for toxicology and environmental chemistry. In this work, quantitative structure-activity relationship (QSAR) models were used for the prediction of BMF for a data set including 30 polychlorinated biphenyls and 12 organochlorine pollutants. This set was divided into training and prediction sets. The result of diversity test reveals that the structure of the training and test sets can represent those of the whole ones. After calculation and screening of a large number of molecular descriptors, the methods of stepwise multiple linear regression and genetic algorithm (GA) were used for the selection of most important and significant descriptors which were related to BMF. Then multiple linear regression and artificial neural network (ANN) techniques were applied as linear and non-linear feature mapping techniques, respectively. By comparison between statistical parameters of these methods it was concluded that an ANN model, which used GA selected descriptors, was superior over constructed models. Descriptors which were used by this model are: topographic electronic index, complementary information content, XY shadow/XY rectangle and difference between partial positively and negatively charge surface area. The standard errors for training and test sets of this model are 0.03 and 0.20, respectively. The degree of importance of each descriptor was evaluated by sensitivity analysis approach for the nonlinear model. A good results (Q (2) = 0.97 and SPRESS = 0.084) is obtained by applying cross-validation test that indicating the validation of descriptors in the obtained model in prediction of BMF for these compounds. PMID:19219557

  4. Quantitative structure-antibacterial activity relationship modeling using a combination of piecewise linear regression-discriminant analysis (I): Quantum chemical, topographic, and topological descriptors

    NASA Astrophysics Data System (ADS)

    Molina, Enrique; Estrada, Ernesto; Nodarse, Delvin; Torres, Luis A.; González, Humberto; Uriarte, Eugenio

    Time-dependent antibacterial activity of 2-furylethylenes using quantum chemical, topographic, and topological indices is described as inhibition of respiration in E. coli. A QSAR strategy based on the combination of the linear piecewise regression and the discriminant analysis is used to predict the biological activity values of strong and moderates antibacterial furylethylenes. The breakpoint in the values of the biological activity was detected. The biological activities of the compounds are described by two linear regression equations. A discriminant analysis is carried out to classify the compounds in one of the biological activity two groups. The results showed using different kind of descriptors were compared. In all cases the piecewise linear regression - discriminant analysis (PLR-DA) method produced significantly better QSAR models than the linear regression analysis. The QSAR models were validated using an external validation previously extracted from the original data. A prediction of reported antibacterial activity analysis was carried out showing dependence between the probability of a good classification and the experimental antibacterial activity. Statistical parameters showed the quality of quantum-chemical descriptors based models prediction in LDA having an accuracy of 0.9 and a C of 0.9. The best PLR-DA model explains more than 92% of the variance of experimental activity. Models with best prediction results were those based on quantum-chemical descriptors. An interpretation of quantum-chemical descriptors entered in models was carried out.

  5. Synthetic retinoids: structure-activity relationships.

    PubMed

    Barnard, Jonathan H; Collings, Jonathan C; Whiting, Andrew; Przyborski, Stefan A; Marder, Todd B

    2009-11-01

    Retinoid signalling pathways are involved in numerous processes in cells, particularly those mediating differentiation and apoptosis. The endogenous ligands that bind to the retinoid receptors, namely all-trans-retinoic acid (ATRA) and 9-cis-retinoic acid, are prone to double-bond isomerisation and to oxidation by metabolic enzymes, which can have significant and deleterious effects on their activities and selectivities. Many of these problems can be overcome through the use of synthetic retinoids, which are often much more stable, as well as being more active. Modification of their molecular structures can result in retinoids that act as antagonists, rather than agonists, or exhibit a large degree of selectivity for particular retinoid-receptor isotypes. Several such selective retinoids are likely to be of value as pharmaceutical agents with reduced toxicities, particularly in cancer therapy, as reagents for controlling cell differentiation, and as tools for elucidating the precise roles that specific retinoid signalling pathways play within cells. PMID:19821467

  6. Analogue-based approaches in anti-cancer compound modelling: the relevance of QSAR models

    PubMed Central

    2011-01-01

    Background QSAR is among the most extensively used computational methodology for analogue-based design. The application of various descriptor classes like quantum chemical, molecular mechanics, conceptual density functional theory (DFT)- and docking-based descriptors for predicting anti-cancer activity is well known. Although in vitro assay for anti-cancer activity is available against many different cell lines, most of the computational studies are carried out targeting insufficient number of cell lines. Hence, statistically robust and extensive QSAR studies against 29 different cancer cell lines and its comparative account, has been carried out. Results The predictive models were built for 266 compounds with experimental data against 29 different cancer cell lines, employing independent and least number of descriptors. Robust statistical analysis shows a high correlation, cross-validation coefficient values, and provides a range of QSAR equations. Comparative performance of each class of descriptors was carried out and the effect of number of descriptors (1-10) on statistical parameters was tested. Charge-based descriptors were found in 20 out of 39 models (approx. 50%), valency-based descriptor in 14 (approx. 36%) and bond order-based descriptor in 11 (approx. 28%) in comparison to other descriptors. The use of conceptual DFT descriptors does not improve the statistical quality of the models in most cases. Conclusion Analysis is done with various models where the number of descriptors is increased from 1 to 10; it is interesting to note that in most cases 3 descriptor-based models are adequate. The study reveals that quantum chemical descriptors are the most important class of descriptors in modelling these series of compounds followed by electrostatic, constitutional, geometrical, topological and conceptual DFT descriptors. Cell lines in nasopharyngeal (2) cancer average R2 = 0.90 followed by cell lines in melanoma cancer (4) with average R2 = 0.81 gave the

  7. A combined 3D-QSAR and docking studies for the In-silico prediction of HIV-protease inhibitors

    PubMed Central

    2013-01-01

    Background Tremendous research from last twenty years has been pursued to cure human life against HIV virus. A large number of HIV protease inhibitors are in clinical trials but still it is an interesting target for researchers due to the viral ability to get mutated. Mutated viral strains led the drug ineffective but still used to increase the life span of HIV patients. Results In the present work, 3D-QSAR and docking studies were performed on a series of Danuravir derivatives, the most potent HIV- protease inhibitor known so far. Combined study of 3D-QSAR was applied for Danuravir derivatives using ligand-based and receptor-based protocols and generated models were compared. The results were in good agreement with the experimental results. Additionally, docking analysis of most active 32 and least active 46 compounds into wild type and mutated protein structures further verified our results. The 3D-QSAR and docking results revealed that compound 32 bind efficiently to the wild and mutated protein whereas, sufficient interactions were lost in compound 46. Conclusion The combination of two computational techniques would helped to make a clear decision that compound 32 with well inhibitory activity bind more efficiently within the binding pocket even in case of mutant virus whereas compound 46 lost its interactions on mutation and marked as least active compound of the series. This is all due to the presence or absence of substituents on core structure, evaluated by 3D-QSAR studies. This set of information could be used to design highly potent drug candidates for both wild and mutated form of viruses. PMID:23683267

  8. Automatic generation of alignments for 3D QSAR analyses.

    PubMed

    Jewell, N E; Turner, D B; Willett, P; Sexton, G J

    2001-01-01

    Many 3D QSAR methods require the alignment of the molecules in a dataset, which can require a fair amount of manual effort in deciding upon a rational basis for the superposition. This paper describes the use of FBSS, a program for field-based similarity searching in chemical databases, for generating such alignments automatically. The CoMFA and CoMSIA experiments with several literature datasets show that the QSAR models resulting from the FBSS alignments are broadly comparable in predictive performance with the models resulting from manual alignments. PMID:11774998

  9. QSAR Classification of ToxCast and Tox21 Chemicals on the Basis of Estrogen Receptor Assays (FutureToxII)

    EPA Science Inventory

    The ToxCast and Tox21 programs have tested ~8,200 chemicals in a broad screening panel of in vitro high-throughput screening (HTS) assays for estrogen receptor (ER) agonist and antagonist activity. The present work uses this large in vitro data set to develop in silico QSAR model...

  10. Structural investigations of T854A mutation in EGFR and identification of novel inhibitors using structure activity relationships

    PubMed Central

    2015-01-01

    Background The epidermal growth factor receptor (EGFR) is a member of the ErbB family that is involved in a number of processes responsible for cancer development and progression such as angiogenesis, apoptosis, cell proliferation and metastatic spread. Malfunction in activation of protein tyrosine kinases has been shown to result in uncontrolled cell growth. The EGFR TK domain has been identified as suitable target in cancer therapy and tyrosine kinase inhibitors such as erlotinib have been used for treatment of cancer. Mutations in the region of the EGFR gene encoding the tyrosine kinase (TK) domain causes altered responses to EGFR TK inhibitors (TKI). In this paper we perform molecular dynamics simulations and PCA analysis on wild-type and mutant (T854A) structures to gain insight into the structural changes observed in the target protein upon mutation. We also report two novel inhibitors identified by combined approach of QSAR model development. Results The wild-type and mutant structure was observed to be stable for 26 ns and 24 ns respectively. In PCA analysis, the mutant structure proved to be more flexible than wild-type. We developed a 3D-QSAR model using 38 thiazolyl-pyrazoline compounds which was later used for prediction of inhibitory activity of natural compounds of ZINC library. The 3D-QSAR model was proved to be robust by the statistical parameters such as r2 (0.9751), q2(0.9491) and pred_r2(0.9525). Conclusion Analysis of molecular dynamics simulations results indicate stability loss and increased flexibility in the mutant structure. This flexibility results in structural changes which render the mutant protein drug resistant against erlotinib. We report two novel compounds having high predicted inhibitory activity to EGFR TK domain with both wild-type and mutant structure. PMID:26041145

  11. Discovery of New Anti-Schistosomal Hits by Integration of QSAR-Based Virtual Screening and High Content Screening.

    PubMed

    Neves, Bruno J; Dantas, Rafael F; Senger, Mario R; Melo-Filho, Cleber C; Valente, Walter C G; de Almeida, Ana C M; Rezende-Neto, João M; Lima, Elid F C; Paveley, Ross; Furnham, Nicholas; Muratov, Eugene; Kamentsky, Lee; Carpenter, Anne E; Braga, Rodolpho C; Silva-Junior, Floriano P; Andrade, Carolina Horta

    2016-08-11

    Schistosomiasis is a debilitating neglected tropical disease, caused by flatworms of Schistosoma genus. The treatment relies on a single drug, praziquantel (PZQ), making the discovery of new compounds extremely urgent. In this work, we integrated QSAR-based virtual screening (VS) of Schistosoma mansoni thioredoxin glutathione reductase (SmTGR) inhibitors and high content screening (HCS) aiming to discover new antischistosomal agents. Initially, binary QSAR models for inhibition of SmTGR were developed and validated using the Organization for Economic Co-operation and Development (OECD) guidance. Using these models, we prioritized 29 compounds for further testing in two HCS platforms based on image analysis of assay plates. Among them, 2-[2-(3-methyl-4-nitro-5-isoxazolyl)vinyl]pyridine and 2-(benzylsulfonyl)-1,3-benzothiazole, two compounds representing new chemical scaffolds have activity against schistosomula and adult worms at low micromolar concentrations and therefore represent promising antischistosomal hits for further hit-to-lead optimization. PMID:27396732

  12. Structure-Activity Relationship of Azaindole-Based Glucokinase Activators.

    PubMed

    Paczal, Attila; Bálint, Balázs; Wéber, Csaba; Szabó, Zoltán B; Ondi, Levente; Theret, Isabelle; De Ceuninck, Frédéric; Bernard, Catherine; Ktorza, Alain; Perron-Sierra, Francoise; Kotschy, András

    2016-01-28

    7-Azaindole has been identified as a novel bidentate anchor point for allosteric glucokinase activators. A systematic investigation around three principal parts of the new small molecule glucokinase activators led to a robust SAR in agreement with structural data that also helped to assess the conformational flexibility of the allosteric activation site. The increase in glucose uptake resulting from glucokinase activation in hepatocytes in vitro translated into the efficient lowering of glucose levels in vivo with the best compounds. PMID:26685731

  13. Development of quantitative structure property relationships for poly(arylene ether)s.

    PubMed

    Hamerton, I; Howlin, B J; Larwood, V

    1995-02-01

    The technique of quantitative structure-activity relationships (QSAR) is well accepted by the drug design community. The analogous technique of quantitative structure-property relationships (QSPR) has applications in the field of polymer chemistry. A variety of molecular modeling and molecular orbital techniques was used to find molecular descriptors that could be used to derive an empirical equation to describe the glass transition temperature of two related classes of poly(arylene ether)s. The derived equation was then used to predict the thermal characteristics of another polymer of the same type. PMID:7794828

  14. Extending (Q)SARs to incorporate proprietary knowledge for regulatory purposes: A case study using aromatic amine mutagenicity.

    PubMed

    Ahlberg, Ernst; Amberg, Alexander; Beilke, Lisa D; Bower, David; Cross, Kevin P; Custer, Laura; Ford, Kevin A; Van Gompel, Jacky; Harvey, James; Honma, Masamitsu; Jolly, Robert; Joossens, Elisabeth; Kemper, Raymond A; Kenyon, Michelle; Kruhlak, Naomi; Kuhnke, Lara; Leavitt, Penny; Naven, Russell; Neilan, Claire; Quigley, Donald P; Shuey, Dana; Spirkl, Hans-Peter; Stavitskaya, Lidiya; Teasdale, Andrew; White, Angela; Wichard, Joerg; Zwickl, Craig; Myatt, Glenn J

    2016-06-01

    Statistical-based and expert rule-based models built using public domain mutagenicity knowledge and data are routinely used for computational (Q)SAR assessments of pharmaceutical impurities in line with the approach recommended in the ICH M7 guideline. Knowledge from proprietary corporate mutagenicity databases could be used to increase the predictive performance for selected chemical classes as well as expand the applicability domain of these (Q)SAR models. This paper outlines a mechanism for sharing knowledge without the release of proprietary data. Primary aromatic amine mutagenicity was selected as a case study because this chemical class is often encountered in pharmaceutical impurity analysis and mutagenicity of aromatic amines is currently difficult to predict. As part of this analysis, a series of aromatic amine substructures were defined and the number of mutagenic and non-mutagenic examples for each chemical substructure calculated across a series of public and proprietary mutagenicity databases. This information was pooled across all sources to identify structural classes that activate or deactivate aromatic amine mutagenicity. This structure activity knowledge, in combination with newly released primary aromatic amine data, was incorporated into Leadscope's expert rule-based and statistical-based (Q)SAR models where increased predictive performance was demonstrated. PMID:26879463

  15. Benchmarking the Predictive Power of Ligand Efficiency Indices in QSAR.

    PubMed

    Cortes-Ciriano, Isidro

    2016-08-22

    Compound physicochemical properties favoring in vitro potency are not always correlated to desirable pharmacokinetic profiles. Therefore, using potency (i.e., IC50) as the main criterion to prioritize candidate drugs at early stage drug discovery campaigns has been questioned. Yet, the vast majority of the virtual screening models reported in the medicinal chemistry literature predict the biological activity of compounds by regressing in vitro potency on topological or physicochemical descriptors. Two studies published in this journal showed that higher predictive power on external molecules can be achieved by using ligand efficiency indices as the dependent variable instead of a metric of potency (IC50) or binding affinity (Ki). The present study aims at filling the shortage of a thorough assessment of the predictive power of ligand efficiency indices in QSAR. To this aim, the predictive power of 11 ligand efficiency indices has been benchmarked across four algorithms (Gradient Boosting Machines, Partial Least Squares, Random Forest, and Support Vector Machines), two descriptor types (Morgan fingerprints, and physicochemical descriptors), and 29 data sets collected from the literature and ChEMBL database. Ligand efficiency metrics led to the highest predictive power on external molecules irrespective of the descriptor type or algorithm used, with an R(2)test difference of ∼0.3 units and a this difference ∼0.4 units when modeling small data sets and a normalized RMSE decrease of >0.1 units in some cases. Polarity indices, such as SEI and NSEI, led to higher predictive power than metrics based on molecular size, i.e., BEI, NBEI, and LE. LELP, which comprises a polarity factor (cLogP) and a size parameter (LE) constantly led to the most predictive models, suggesting that these two properties convey a complementary predictive signal. Overall, this study suggests that using ligand efficiency indices as the dependent variable might be an efficient strategy to model

  16. 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

  17. AQUATIC TOXICITY MODE OF ACTION STUDIES APPLIED TO QSAR DEVELOPMENT

    EPA Science Inventory

    A series of QSAR models for predicting fish acute lethality were developed using systematically collected data on more than 600 chemicals. These models were developed based on the assumption that chemicals producing toxicity through a common mechanism will have commonality in the...

  18. Comparison of a neural net-based QSAR algorithm (PCANN) with Hologram- and multiple linear regression-based QSAR approaches: application to 1,4-dihydropyridine-based calcium channel antagonists.

    PubMed

    Viswanadhan, V N; Mueller, G A; Basak, S C; Weinstein, J N

    2001-01-01

    A QSAR algorithm (PCANN) has been developed and applied to a set of calcium channel blockers which are of special interest because of their role in cardiac disease and also because many of them interact with P-glycoprotein, a membrane protein associated with multidrug resistance to anticancer agents. A database of 46 1,4-dihydropyridines with known Ca2+ channel binding affinities was employed for the present analysis. The QSAR algorithm can be summarized as follows: (1) a set of 90 graph theoretic and information theoretic descriptors representing various structural and topological characteristics was calculated for each of the 1,4-dihydropyridines and (2) principal component analysis (PCA) was used to compress these 90 into the eight best orthogonal composite descriptors for the database. These eight sufficed to explain 96% of the variance in the original descriptor set. (3) Two important empirical descriptors, the Leo-Hansch lipophilic constant and the Hammet electronic parameter, were added to the list of eight. (4) The 10 resulting descriptors were used as inputs to a back-propagation neural network whose output was the predicted binding affinity. (5) The predictive ability of the network was assessed by cross-validation. A comparison of the present approach with two other QSAR approaches (multiple linear regression using the same variables and a Hologram QSAR model) is made and shows that the PCANN approach can yield better predictions, once the right network configuration is identified. The present approach (PCANN) may prove useful for rapid assessment of the potential for biological activity when dealing with large chemical libraries. PMID:11410024

  19. Development of quantitative structure-pharmacokinetic relationships.

    PubMed Central

    Mayer, J M; van de Waterbeemd, H

    1985-01-01

    Quantitative structure-activity relationships (QSAR) relating biological activity to physiochemical descriptors have been successfully used for a number of years. It is also long recognized that pharmacokinetic parameters may play an important and even determinant role in drug action. This prompted several researchers to focus attention to pharmacokinetic parameters as potential descriptors in quantitative drug design. A number of examples of quantitative structure-pharmacokinetic relationships (QSPR) have appeared in the literature. The present contribution reviews some developments in this field. In particular, a number of concepts and problems are critically discussed, rather than compilations of examples already published in recent reviews. Attention will be paid to the main processes of the pharmacokinetic or toxicokinetic phase in drug action, including absorption, distribution and elimination (biotransformation and excretion). It is clear that quantitative approaches are of considerable interest to toxicologists, since these methods may contribute to the development of real predictive toxicology. PMID:3905378

  20. Comparative toxicity and structure-activity in Chlorella and Tetrahymena: Monosubstituted phenols

    SciTech Connect

    Jaworska, J.S.; Schultz, T.W. )

    1991-07-01

    The relative toxicity of selected monosubstituted phenols has been assessed by Kramer and Truemper in the Chlorella vulgaris assay. The authors examined population growth inhibition of this simple green algae under short-term static conditions for 33 derivatives. However, efforts to develop a strong predictive quantitative structure-activity relationship (QSAR) met with limited success because they modeled across modes of toxic action or segregated derivatives such as positional isomers (i.e., ortho-, meta-, para-). In an effort to further their understanding of the relationships of ecotoxic effects of phenols, the authors have evaluated the same derivatives reported by Kramer and Truemper in the Tetrahymena pyriformis population growth assay, compared the responses in both systems and developed QSARs for the Chlorella vulgaris data based on mechanisms of action.

  1. Probing the origins of human acetylcholinesterase inhibition via QSAR modeling and molecular docking.

    PubMed

    Simeon, Saw; Anuwongcharoen, Nuttapat; Shoombuatong, Watshara; Malik, Aijaz Ahmad; Prachayasittikul, Virapong; Wikberg, Jarl E S; Nantasenamat, Chanin

    2016-01-01

    Alzheimer's disease (AD) is a chronic neurodegenerative disease which leads to the gradual loss of neuronal cells. Several hypotheses for AD exists (e.g., cholinergic, amyloid, tau hypotheses, etc.). As per the cholinergic hypothesis, the deficiency of choline is responsible for AD; therefore, the inhibition of AChE is a lucrative therapeutic strategy for the treatment of AD. Acetylcholinesterase (AChE) is an enzyme that catalyzes the breakdown of the neurotransmitter acetylcholine that is essential for cognition and memory. A large non-redundant data set of 2,570 compounds with reported IC50 values against AChE was obtained from ChEMBL and employed in quantitative structure-activity relationship (QSAR) study so as to gain insights on their origin of bioactivity. AChE inhibitors were described by a set of 12 fingerprint descriptors and predictive models were constructed from 100 different data splits using random forest. Generated models afforded R (2), [Formula: see text] and [Formula: see text] values in ranges of 0.66-0.93, 0.55-0.79 and 0.56-0.81 for the training set, 10-fold cross-validated set and external set, respectively. The best model built using the substructure count was selected according to the OECD guidelines and it afforded R (2), [Formula: see text] and [Formula: see text] values of 0.92 ± 0.01, 0.78 ± 0.06 and 0.78 ± 0.05, respectively. Furthermore, Y-scrambling was applied to evaluate the possibility of chance correlation of the predictive model. Subsequently, a thorough analysis of the substructure fingerprint count was conducted to provide informative insights on the inhibitory activity of AChE inhibitors. Moreover, Kennard-Stone sampling of the actives were applied to select 30 diverse compounds for further molecular docking studies in order to gain structural insights on the origin of AChE inhibition. Site-moiety mapping of compounds from the diversity set revealed three binding anchors encompassing both hydrogen bonding and van der Waals

  2. Antioxidant activity of taxifolin: an activity-structure relationship.

    PubMed

    Topal, Fevzi; Nar, Meryem; Gocer, Hulya; Kalin, Pınar; Kocyigit, Umit M; Gülçin, İlhami; Alwasel, Saleh H

    2016-08-01

    Taxifolin is a kind of flavanonol, whose biological ability. The objectives of this study were to investigate the antioxidants and antiradical activities of taxifolin by using different in vitro bioanalytical antioxidant methods including DMPD√(+), ABTS√(+), [Formula: see text], and DPPH√-scavenging effects, the total antioxidant influence, reducing capabilities, and Fe(2+)-chelating activities. Taxifolin demonstrated 81.02% inhibition of linoleic acid emulsion peroxidation at 30 µg/mL concentration. At the same concentration, standard antioxidants including trolox, α-tocopherol, BHT, and BHA exhibited inhibitions of linoleic acid emulsion as 88.57, 73.88, 94.29, and 90.12%, respectively. Also, taxifolin exhibited effective DMPD√(+), ABTS√(+), [Formula: see text], and DPPH√-scavenging effects, reducing capabilities, and Fe(2+)-chelating effects. The results obtained from this study clearly showed that taxifolin had marked antioxidant, reducing ability, radical scavenging and metal-chelating activities. Also, this study exhibits a scientific shore for the significant antioxidant activity of taxifolin and its structure-activity insight. PMID:26147349

  3. Prediction of Inhibitory Activity of Epidermal Growth Factor Receptor Inhibitors Using Grid Search-Projection Pursuit Regression Method

    PubMed Central

    Du, Hongying; Hu, Zhide; Bazzoli, Andrea; Zhang, Yang

    2011-01-01

    The epidermal growth factor receptor (EGFR) protein tyrosine kinase (PTK) is an important protein target for anti-tumor drug discovery. To identify potential EGFR inhibitors, we conducted a quantitative structure–activity relationship (QSAR) study on the inhibitory activity of a series of quinazoline derivatives against EGFR tyrosine kinase. Two 2D-QSAR models were developed based on the best multi-linear regression (BMLR) and grid-search assisted projection pursuit regression (GS-PPR) methods. The results demonstrate that the inhibitory activity of quinazoline derivatives is strongly correlated with their polarizability, activation energy, mass distribution, connectivity, and branching information. Although the present investigation focused on EGFR, the approach provides a general avenue in the structure-based drug development of different protein receptor inhibitors. PMID:21811593

  4. A new series of 2-phenol-4-aryl-6-chlorophenyl pyridine derivatives as dual topoisomerase I/II inhibitors: Synthesis, biological evaluation and 3D-QSAR study.

    PubMed

    Karki, Radha; Jun, Kyu-Yeon; Kadayat, Tara Man; Shin, Somin; Thapa Magar, Til Bahadur; Bist, Ganesh; Shrestha, Aarajana; Na, Younghwa; Kwon, Youngjoo; Lee, Eung-Seok

    2016-05-01

    As a continuous effort to develop novel antitumor agents, a new series of forty-five 2-phenol-4-aryl-6-chlorophenyl pyridine compounds were synthesized and evaluated for cytotoxicity against four different human cancer cell lines (DU145, HCT15, T47D, and HeLa), and topoisomerase I and II inhibitory activity. Several compounds (10-15, 20, 22, 24, 28, 42, and 49) displayed strong to moderate dual topoisomerase I and II inhibitory activity at 100 μM. It was observed that hydroxyl and chlorine moiety at meta or para position of phenyl ring is favorable for dual topoisomerase inhibitory activity and cytotoxicity. Most of the compounds displayed stronger cytotoxicities than those of all positive controls against the HCT15 and T47D cell lines. For investigation of the structure-activity relationships, a 3D-QSAR analysis using the method of comparative molecular field analysis (CoMFA) was performed. The generated 3D contour maps can be used for further rational design of novel terpyridine derivatives as highly selective and potent cytotoxic agents. PMID:26945111

  5. Peptide reactivity associated with skin sensitization: The QSAR Toolbox and TIMES compared to the DPRA.

    PubMed

    Urbisch, D; Honarvar, N; Kolle, S N; Mehling, A; Ramirez, T; Teubner, W; Landsiedel, R

    2016-08-01

    The molecular initiating event (MIE) of skin sensitization is the binding of a hapten to dermal proteins. This can be assessed using the in chemico direct peptide reactivity assay (DPRA) or in silico tools such as the QSAR Toolbox and TIMES SS. In this study, the suitability of these methods was analyzed by comparing their results to in vivo sensitization data of LLNA and human studies. Compared to human data, 84% of non-sensitizers and sensitizers yielded consistent results in the DPRA. In silico tools resulted in 'no alert' for 83%-100% of the non-sensitizers, but alerted only 55%-61% of the sensitizers. The inclusion of biotic and abiotic transformation simulations yielded more alerts for sensitizers, but simultaneously dropped the number of non-alerted non-sensitizers. In contrast to the DPRA, in silico tools were more consistent with results of the LLNA than human data. Interestingly, the new "DPRA profilers" (QSAR Toolbox) provided unsatisfactory results. Additionally, the results were combined in the '2 out of 3' prediction model with in vitro data derived from LuSens and h-CLAT. Using DPRA results, the model identified 90% of human sensitizers and non-sensitizers; using in silico results (including abiotic and biotic activations) instead of DPRA results led to a comparable high predictivity. PMID:27090964

  6. Building on a solid foundation: SAR and QSAR as a fundamental strategy to reduce animal testing.

    PubMed

    Sullivan, K M; Manuppello, J R; Willett, C E

    2014-01-01

    The development of more efficient, ethical, and effective means of assessing the effects of chemicals on human health and the environment was a lifetime goal of Gilman Veith. His work has provided the foundation for the use of chemical structure for informing toxicological assessment by regulatory agencies the world over. Veith's scientific work influenced the early development of the SAR models in use today at the US Environmental Protection Agency. He was the driving force behind the Organisation for Economic Co-operation and Development QSAR Toolbox. Veith was one of a few early pioneers whose vision led to the linkage of chemical structure and biological activity as a means of predicting adverse apical outcomes (known as a mode of action, or an adverse outcome pathway approach), and he understood at an early stage the power that could be harnessed when combining computational and mechanistic biological approaches as a means of avoiding animal testing. Through the International QSAR Foundation he organized like-minded experts to develop non-animal methods and frameworks for the assessment of chemical hazard and risk for the benefit of public and environmental health. Avoiding animal testing was Gil's passion, and his work helped to initiate the paradigm shift in toxicology that is now rendering this feasible. PMID:24773450

  7. The continuous molecular fields approach to building 3D-QSAR models.

    PubMed

    Baskin, Igor I; Zhokhova, Nelly I

    2013-05-01

    The continuous molecular fields (CMF) approach is based on the application of continuous functions for the description of molecular fields instead of finite sets of molecular descriptors (such as interaction energies computed at grid nodes) commonly used for this purpose. These functions can be encapsulated into kernels and combined with kernel-based machine learning algorithms to provide a variety of novel methods for building classification and regression structure-activity models, visualizing chemical datasets and conducting virtual screening. In this article, the CMF approach is applied to building 3D-QSAR models for 8 datasets through the use of five types of molecular fields (the electrostatic, steric, hydrophobic, hydrogen-bond acceptor and donor ones), the linear convolution molecular kernel with the contribution of each atom approximated with a single isotropic Gaussian function, and the kernel ridge regression data analysis technique. It is shown that the CMF approach even in this simplest form provides either comparable or enhanced predictive performance in comparison with state-of-the-art 3D-QSAR methods. PMID:23719959

  8. The Relationship Between Neck Pain and Physical Activity

    PubMed Central

    Cheung, Janice; Kajaks, Tara; MacDermid, Joy C.

    2013-01-01

    Neck pain is a significant societal burden due to its high prevalence and healthcare costs. While physical activity can help to manage other forms of chronic musculoskeletal pain, little data exists on the relationship between physical activity and neck pain. The purpose of this study was to compare physical activity levels between individuals with neck pain and healthy controls, and then to relate disability, fear of movement, and pain sensitivity measures to physical activity levels in each of the two participant groups. 21 participants were recruited for each of the two participant groups (n = 42). Data collection included the use of the Neck Disability Index, the Tampa Scale for Kinesiophobia, electrocutaneous (Neurometer® CPT) and pressure stimulation (JTech algometer) for quantitative sensory testing, and 5 days of subjective (Rapid Assessment of Physical Activity) and objective (BioTrainer II) measurements of physical activity. Analysis of Variance and Pearson’s Correlation were used to determine if differences and relationships exist between dependent variables both within and between groups. The results show that individuals with mild neck pain and healthy controls do not differ in subjectively and objectively measured physical activity. While participants with neck pain reported higher neck disability and fear of movement, these factors did not significantly relate to physical activity levels. Perceived activity level was related to pain threshold and tolerance at local neck muscles sites (C2 paraspinal muscle and upper trapezius muscle), whereas measured activity was related to generalized pain sensitivity, as measured at the tibialis anterior muscle site. PMID:24133553

  9. Structure-activity relationship of indoloquinoline analogs anti-MRSA.

    PubMed

    Zhao, Min; Kamada, Tomonori; Takeuchi, Aya; Nishioka, Hiromi; Kuroda, Teruo; Takeuchi, Yasuo

    2015-12-01

    Indolo[3,2-b]quinoline analogs (3a-3s), 4-(acridin-9-ylamino) phenol hydrochloride (4), benzofuro[3,2-b]quinoline (3t), indeno[1,2-b]quinolines (3u and 3v) have been synthesized. Those compounds were found to exhibit anti-bacterial activity towards Methicillin-resistant Staphylococcus aureus (anti-MRSA activity). Structure-activity relationship studies were conducted that indoloquinoline ring, benzofuroquinoline ring and 4-aminophenol group are essential structure for anti-MRSA activity. PMID:26522949

  10. 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. PMID:26117201

  11. Synthesis, antitumor evaluation and 3D-QSAR studies of [1,2,4]triazolo[4,3-b][1,2,4,5]tetrazine derivatives.

    PubMed

    Xu, Feng; Yang, Zhen-Zhen; Ke, Zhong-Lu; Xi, Li-Min; Yan, Qi-Dong; Yang, Wei-Qiang; Zhu, Li-Qing; Lin, Fei-Lei; Lv, Wei-Ke; Wu, Han-Gui; Wang, John; Li, Hai-Bo

    2016-10-01

    A series of [1,2,4]triazolo[4,3-b][1,2,4,5]tetrazine derivatives have been synthesized and their structures were confirmed by single-crystal X-ray diffraction. Compared to some reported structures of 1,6-dihydro-1,2,4,5-tetrazine, these compounds can't be considered as having homoaromaticity. Their antiproliferative activities were evaluated against MCF-7, Bewo and HL-60 cells in vitro. Two compounds were highly effective against MCF-7, Bewo and HL-60 cells with IC50 values in 0.63-13.12μM. Three-dimensional quantitative structure-activity relationship (3D-QSAR) studies of comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were carried out on 51 [1,2,4]triazolo[4,3-b][1,2,4,5]tetrazine derivatives with antiproliferative activity against MCF-7 cell. Models with good predictive abilities were generated with the cross validated q(2) values for CoMFA and CoMSIA being 0.716 and 0.723, respectively. Conventional r(2) values were 0.985 and 0.976, respectively. The results provide the tool for guiding the design and synthesis of novel and more potent tetrazine derivatives. PMID:27597251

  12. 3D QSAR STUDIES ON A SERIES OF QUINAZOLINE DERRIVATIVES AS TYROSINE KINASE (EGFR) INHIBITOR: THE K-NEAREST NEIGHBOR MOLECULAR FIELD ANALYSIS APPROACH

    PubMed Central

    Noolvi, Malleshappa N.; Patel, Harun M.

    2010-01-01

    Epidermal growth factor receptor (EGFR) protein tyrosine kinases (PTKs) are known for its role in cancer. Quinazoline have been reported to be the molecules of interest, with potent anticancer activity and they act by binding to ATP site of protein kinases. ATP binding site of protein kinases provides an extensive opportunity to design newer analogs. With this background, we report an attempt to discern the structural and physicochemical requirements for inhibition of EGFR tyrosine kinase. The k-Nearest Neighbor Molecular Field Analysis (kNN-MFA), a three dimensional quantitative structure activity relationship (3D- QSAR) method has been used in the present case to study the correlation between the molecular properties and the tyrosine kinase (EGFR) inhibitory activities on a series of quinazoline derivatives. kNNMFA calculations for both electrostatic and steric field were carried out. The master grid maps derived from the best model has been used to display the contribution of electrostatic potential and steric field. The statistical results showed significant correlation coefficient r2 (q2) of 0.846, r2 for external test set (pred_r2) 0.8029, coefficient of correlation of predicted data set (pred_r2se) of 0.6658, degree of freedom 89 and k nearest neighbor of 2. Therefore, this study not only casts light on binding mechanism between EGFR and its inhibitors, but also provides hints for the design of new EGFR inhibitors with observable structural diversity PMID:24825983

  13. Synthesis, biological evaluation and 3D-QSAR studies of imidazolidine-2,4-dione derivatives as novel protein tyrosine phosphatase 1B inhibitors.

    PubMed

    Wang, Mei-Yan; Jin, Yuan-Yuan; Wei, Hui-Yu; Zhang, Li-Song; Sun, Su-Xia; Chen, Xiu-Bo; Dong, Wei-Li; Xu, Wei-Ren; Cheng, Xian-Chao; Wang, Run-Ling

    2015-10-20

    Protein tyrosine phosphatase 1B (PTP1B) plays a vital role in the regulation of insulin sensitivity and dephosphorylation of the insulin receptor, so PTP1B inhibitors may be potential agents to treat type 2 diabetes. In this work, a series of novel imidazolidine-2,4-dione derivatives were designed, synthesized and assayed for their PTP1B inhibitory activities. These compounds exhibited potent activities with IC50 values at 0.57-172 μM. A 3D-QSAR study using CoMFA and CoMSIA techniques was carried out to explore structure activity relationship of these molecules. The CoMSIA model was more predictive with q(2) = 0.777, r(2) = 0.999, SEE = 0.013 and r(2)pred = 0.836, while the CoMFA model gave q(2) = 0.543, r(2) = 0.998, SEE = 0.029 and r(2)pred = 0.754. The contour maps derived from the best CoMFA and CoMSIA models combined with docking analysis provided good insights into the structural features relevant to the bioactivity, and could be used in the molecular design of novel imidazolidine-2,4-dione derivatives. PMID:26342135

  14. Structure-Activity Relationships in Nitro-Aromatic Compounds

    NASA Astrophysics Data System (ADS)

    Vogt, R. A.; Rahman, S.; Crespo-Hernández, C. E.

    Many nitro-aromatic compounds show mutagenic and carcinogenic properties, posing a potential human health risk. Despite this potential health hazard, nitro-aromatic compounds continue to be emitted into ambient air from municipal incinerators, motor vehicles, and industrial power plants. As a result, understanding the structural and electronic factors that influence mutagenicity in nitro-aromatic compounds has been a long standing objective. Progress toward this goal has accelerated over the years, in large part due to the synergistic efforts among toxicology, computational chemistry, and statistical modeling of toxicological data. The concerted influence of several structural and electronic factors in nitro-aromatic compounds makes the development of structure-activity relationships (SARs) a paramount challenge. Mathematical models that include a regression analysis show promise in predicting the mutagenic activity of nitro-aromatic compounds as well as in prioritizing compounds for which experimental data should be pursued. A major challenge of the structure-activity models developed thus far is their failure to apply beyond a subset of nitro-aromatic compounds. Most quantitative structure-activity relationship papers point to statistics as the most important confirmation of the validity of a model. However, the experimental evidence shows the importance of the chemical knowledge in the process of generating models with reasonable applicability. This chapter will concisely summarize the structural and electronic factors that influence the mutagenicity in nitro-aromatic compounds and the recent efforts to use quantitative structure-activity relationships to predict those physicochemical properties.

  15. Analysis of High-Dimensional Structure-Activity Screening Datasets Using the Optimal Bit String Tree.

    PubMed

    Zhang, Ke; Hughes-Oliver, Jacqueline M; Young, S Stanley

    2013-01-01

    A new classification method called the Optimal Bit String Tree is proposed to identify quantitative structure-activity relationships (QSARs). The method introduces the concept of a chromosome to describe the presence/absence context of a combination of descriptors. A descriptor set and its optimal chromosome form the splitting variable. A new stochastic searching scheme that contains a weighted sampling scheme, simulated annealing, and a trimming procedure optimizes the choice of splitting variable. Simulation studies and an application to screening monoamine oxidase (MAO) inhibitors show that OBSTree is advantageous in accurately and effectively identifying QSAR rules and finding different classes of active compounds. Details of the algorithm, SAS code, and simulated and real datasets are available online as supplementary materials. PMID:23878407

  16. Structure-cardiac activity relationship of C19-diterpenoid alkaloids.

    PubMed

    Jian, Xi-Xian; Tang, Pei; Liu, Xiu-Xiu; Chao, Ruo-Bing; Chen, Qiao-Hong; She, Xue-Ke; Chen, Dong-Lin; Wang, Feng-Peng

    2012-06-01

    Thirty three C19-diterpenoid alkaloids, twenty-two prepared from known C19-diterpenoid alkaloids and eleven isolated from Aconitum and Delphinium spp. were evaluated for their cardiac activity in the isolated bullfrog heart assay. Among them, eleven compounds exhibited cardiac activity, with average rate of amplitude increase in the range of 16-118%. Compound 7, mesaconine (17), hypaconine (25), and beiwutinine (26) exhibited strong cardiac activities relative to the reference drug. The structure-activity relationship data acquired indicated that an alpha-hydroxyl group at C-15, a hydroxyl group at C-8, an alpha-methoxyl o