3D-QSAR and molecular docking studies of 2-pyrimidinecarbonitrile derivatives as inhibitors against falcipain-3

The three dimensional-quantitative structure activity relationship (3D-QSAR) studies were performed on a series of falcipain-3 inhibitors using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) techniques. A training set containing 42 molecules served to establish the QSAR models. The optimum CoMFA and CoMSIA models obtained for the training set were statistically significant with cross-validated correlation coefficients r(cv)(2) (q(2)) of 0.549 and 0.608, and conventional correlation coefficients (r(2)) of 0.976 and 0.932, respectively. An independent test set of 12 molecules validated the external predictive power of both models with predicted correlation coefficients (r(pred)(2)) for CoMFA and CoMSIA as 0.697 and 0.509, respectively. The docking of inhibitors into falcipain-3 active site using GOLD software revealed the vital interactions and binding conformation of the inhibitors. The CoMFA and CoMSIA field contour maps agree well with the structural characteristics of the binding pocket of falcipain-3 active site, which suggests that the information rendered by 3D-QSAR models and the docking interactions can provide guidelines for the development of improved falcipain-3 inhibitors.     

3D-QSAR studies on malonyl coenzyme A decarboxylase inhibitors

Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on a series of Malonyl Co-A decarboxylase (MCD) inhibitors (Cheng et al. J. Med. Chem.2006, 49, 1517-1525 and Cheng et al. Bioorg. Med. Chem. Lett.2006, 16, 695-700). These inhibitors have shown protective action on the ischemic heart by inhibiting fatty acid oxidation. The CoMFA model produced statistically significant results, with the cross-validated and conventional correlation coefficients being 0.544 and 0.986, respectively. The best results were obtained by combining steric, electrostatic, hydrophobic, and H-bond acceptor fields in CoMSIA, in which case the respective cross-validated and conventional correlation coefficients were 0.551 and 0.918. The predictive ability of CoMFA and CoMSIA, determined using a test set of 24 compounds, gave predictive correlation coefficients of 0.718 and 0.725, respectively. The information obtained from CoMFA and CoMSIA 3D contour maps may be of utility in the design of more potent MCD inhibitors.     

3D-QSAR studies on tripeptide aldehyde inhibitors of proteasome using CoMFA and CoMSIA methods

The ubiquitin-proteasome pathway plays a crucial role in the regulation of many physiological processes and in the development of a number of major human diseases, such as cancer, Alzheimer's, Parkinson's, diabetes, etc. As a new target, the study on the proteasome inhibitors has received much attention recently. Three-dimensional quantitative structure-activity relationship (3D-QSAR) studies using comparative molecule field analysis (CoMFA) and comparative molecule similarity indices analysis (CoMSIA) techniques were applied to analyze the binding affinity of a set of tripeptide aldehyde inhibitors of 20S proteasome. The optimal CoMFA and CoMSIA models obtained for the training set were all statistically significant with cross-validated coefficients (q(2)) of 0.615, 0.591 and conventional coefficients (r(2)) of 0.901, 0.894, respectively. These models were validated by a test set of eight molecules that were not included in the training set. The predicted correlation coefficients (r(2)) of CoMFA and CoMSIA are 0.944 and 0.861, respectively. The CoMFA and CoMSIA field contour maps agree well with the structural characteristics of the binding pocket of beta5 subunit of 20S proteasome, which suggests that the 3D-QSAR models built in this paper can be used to guide the development of novel inhibitors of 20S proteasome.     

CoMFA and CoMSIA 3D-QSAR analysis of DMDP derivatives as anti-cancer agents

Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) based on three dimensional quantitative structure-activity relationship (3D-QSAR) studies were conducted on a series (78 compounds) of 2, 4-diamino-5-methyl-5-deazapteridine (DMDP) derivatives as potent anticancer agents. The best prediction were obtained with a CoMFA standard model (q(2) = 0.530, r(2) = 0.903) and with CoMSIA combined steric, electrostatic, hydrophobic and hydrogen bond donor fields (q(2) = 0.548, r(2) = 0.909). Both models were validated by a test set of ten compounds producing very good predictive r(2) values of 0.935 and 0.842, respectively. CoMFA and CoMSIA contour maps were then used to analyze the structural features of ligands to account for the activity in terms of positively contributing physiochemical properties such as steric, electrostatic, hydrophobic and hydrogen bond donor fields. The resulting contour maps produced by the best CoMFA and CoMSIA models were used to identify the structural features relevant to the biological activity in this series of analogs. This study suggests that the highly electropositive substituents with low steric tolerance are required at 5 position of the pteridine ring and bulky electronegatve substituents are required at the meta-position of the phenyl ring. The information obtained from CoMFA and CoMSIA 3-D contour maps can be used for the design of deazapteridine-based analogs as anticancer agents.     

CoMFA and CoMSIA 3D-QSAR analysis of diaryloxy-methano-phenanthrene derivatives as anti-tubercular agents

Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) based on three-dimensional quantitative structure-activity relationship (3D-QSAR) studies were conducted on a series (44 compounds) of diaryloxy-methano-phenanthrene derivatives as potent antitubercular agents. The best predictions were obtained with a CoMFA standard model (q (2)=0.625, r (2)=0.994) and with CoMSIA combined steric, electrostatic, and hydrophobic fields (q (2)=0.486, r (2)=0.986). Both models were validated by a test set of seven compounds and gave satisfactory predictive r (2) values of 0.999 and 0.745, respectively. CoMFA and CoMSIA contour maps were used to analyze the structural features of the ligands to account for the activity in terms of positively contributing physicochemical properties: steric, electrostatic, and hydrophobic fields. The information obtained from CoMFA and CoMSIA 3-D contour maps can be used for further design of phenanthrene-based analogs as anti-TB agents. The resulting contour maps, produced by the best CoMFA and CoMSIA models, were used to identify the structural features relevant to the biological activity in this series of analogs. Further analysis of these interaction-field contour maps also showed a high level of internal consistency. This study suggests that introduction of bulky and highly electronegative groups on the basic amino side chain along with decreasing steric bulk and electronegativity on the phenanthrene ring might be suitable for designing better antitubercular agents.     

黄酮类醛糖还原酶抑制剂的三维定量构效关系研究

目的：建立黄酮类化合物抑制剂活性的三维定量构效关系模型,为进一步进行黄酮类醛糖还原酶抑制剂（ARI）的活性与三维结构关系的研究提供重要依据。方法：采用比较分子力场分析法（CoMFA）和比较分子相似性指数分析法（CoMSIA）,系统研究了75个新型ARI的三维定量构效关系。结果：CoMFA和CoMSIA模型的交互验证相关系数q^2值分别为0.603和0．706、非交互验证相关系数r2值分别为0.956和0.900。结论：CoMFA和CoMSIA模型均具有较强的预测能力,CoMFA和CoMSIA模型的三维等值线图直观地解释了化合物的构效关系,阐明了化合物结构中各位置取代基对黄酮类醛糖还原酶抑制剂活性的影响,为进一步结构优化提供了重要理论依据。     

3-D-QSAR CoMFA and CoMSIA studies on tetrahydrofuroyl-L-phenylalanine derivatives as VLA-4 antagonists

Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on tetrahydrofuroyl-L-phenylalanine derivatives as VLA-4 antagonists. The best CoMFA and CoMSIA models that were generated using atom based alignment from a training set of twenty five tetrahydrofuroyl-L-phenylalanine derivatives, are six-component models with good statistics; CoMFA: r(2)(cv)=0.366, r(2)=0.983, s=0.099, F=172.661 and PRESS=4.435; CoMSIA: r(2)(cv)=0.528, r(2)=0.995, s=0.054, F=577.87 and PRESS=3.563. Both of these 3-D-QSAR models were validated using a test set of eleven compounds, whose predicted pIC(50) values fall within one log unit of the actual pIC(50). The contour diagrams obtained for the various CoMFA and CoMSIA field contributions can be mapped back onto structural features to explain the activity trends of the molecules analysed. Based on the spatial arrangement of the various field contributions, novel molecules with improved activity can be designed.     

Comparative molecular field analysis and comparative molecular similarity indices analysis of human thymidine kinase 1 substrates

Thymidine kinase 1 (TK1) is a key target for antiviral and anticancer chemotherapy. Three-dimensional quantitative structure-activity relationship (3D-QSAR) using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) techniques was applied to analyze the phosphorylation capacity of a series of 31 TK1 substrates. The optimal predictive CoMFA model with 26 molecules provided the following values: cross-validated r(2) (q(2))=0.651, non-cross-validated r(2)=0.980, standard error of estimate (s)=0.207, F=129.3. For the optimal CoMSIA model the following values were found: q(2)=0.619, r(2)=0.994, s=0.104, F=372.2. The CoMSIA model includes steric, electrostatic, and hydrogen bond donor fields. The predictive capacity of both models was successfully validated by calculating known phosphorylation rates of five TK1 substrates that were not included in the training set. Contour maps obtained from CoMFA and CoMSIA models correlated with the experimentally developed SAR.     

Structural insights of PA-824 derivatives: ligand-based 3D-QSAR study and design of novel PA824 derivatives as anti-tubercular agents

Abstract

With the purpose of designing novel chemical entities with improved inhibitory potencies against drug-resistant Mycobacterium tuberculosis, the 3D- quantitative structure–activity relationship (QSAR) studies were carried out on biphenyl analogs of the tuberculosis (TB) drug, PA-824. Anti-mycobacterial activity (MABA) was considered for the 3D-QSAR studies using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) approaches. The best CoMFA and CoMSIA models were found statistically significant with cross-validated coefficients (q²) of 0.784 and 0.768, respectively, and conventional coefficients (r²) of 0.823 and 0.981, respectively. The cross-validated and the external validation results revealed that both the CoMFA and CoMSIA models possesses high accommodating capacities and they would be reliable for predicting the pMIC values of new PA-824 derivatives. Based on the models and structural insights, a series of new PA-824 derivatives were designed and the anti-mycobacterial activities of the designed compounds were predicted based on the best 3D-QSAR model. The predicted data results suggest the designed compounds are more potent than existed ones.     

3D-QSAR,molecular docking,and molecular dynamic simulations for prediction of new Hsp90 inhibitors based on isoxazole scaffold

Heat shock protein 90(Hsp90), as a molecular chaperone, play a crucial role in folding and proper function of many proteins. Hsp90 inhibitors containing isoxazole scaffold are currently being used in the treatment of cancer as tumor suppressers. Here in the present studies, new compounds based on isoxazole scaffold were predicted using a combination of molecular modeling techniques including three-dimensional quantitative structure–activity relationship (3D-QSAR), molecular docking and molecular dynamic (MD) simulations. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were also done. The steric and electrostatic contour map of CoMFA and CoMSIA were created. Hydrophobic, hydrogen bond donor and acceptor of CoMSIA model also were generated, and new compounds were predicted by CoMFA and CoMSIA contour maps. To investigate the binding modes of the predicted compounds in the active site of Hsp90, a molecular docking simulation was carried out. MD simulations were also conducted to evaluate the obtained results on the best predicted compound and the best reported Hsp90 inhibitors in the 3D-QSAR model. Findings indicate that the predicted ligands were stable in the active site of Hsp90.     

Synthesis, anti-tuberculosis activity, and 3D-QSAR study of 4-(adamantan-1-yl)-2-substituted quinolines

Structural optimization of the previously identified 4-(adamantan-1-yl)-2-quinolinecarbohydrazide (AQCH, MIC=6.25 microg/mL, 99% inhibition, Mycobacterium tuberculosis H37Rv) has led to two series of 4-(adamantan-1-yl)-2-substituted quinolines (Series 1-2). All new derivatives were evaluated in vitro for antimycobacterial activities against drug-sensitive M. tuberculosis H37Rv strain. Several 4-adamantan-1-yl-quinoline-2-carboxylic acid N'-alkylhydrazides (Series 1) described herein showed promising inhibitory activity. In particular, analogs 7, 9, 20, and 21 displayed MIC of 3.125 microg/mL. Further investigation of AQCH by its reaction with various aliphatic, aromatic, and heteroaromatic aldehydes led to the synthesis of 4-adamantan-1-yl-quinoline-2-carboxylic acid alkylidene hydrazides (Series 2). Analogs 42-44 and 48 have produced promising antimycobacterial activities (99% inhibition) at 3.125 microg/mL against drug-sensitive M. tuberculosis H37Rv strain. The most potent analog 35 of the series produced 99% inhibition at 1.00 microg/mL against drug-sensitive strain, and MIC of 3.125 microg/mL against isoniazid-resistant TB strain. To understand the relationship between structure and activity, a 3D-QSAR analysis has been carried out by three methods-comparative molecular field analysis (CoMFA), CoMFA with inclusion of a hydropathy field (HINT), and comparative molecular similarity indices analysis (CoMSIA). Several statistically significant CoMFA, CoMFA with HINT, and CoMSIA models were generated. Prediction of the activity of a test set of molecules was the best for the CoMFA model generated with database alignment. Based on the CoMFA contours, we have tried to explain the structure-activity relationships of the compounds reported herein.     

3D-QSAR studies of substituted 1-(3, 3-diphenylpropyl)-piperidinyl amides and ureas as CCR5 receptor antagonists

3D-QSAR studies on the derivatives of 1-(3,3-diphenylpropyl)-piperidinyl amide and urea as CCR5 receptor antagonists were performed by comparative molecular field analysis (CoMFA) and comparative molecular similarity indices (CoMSIA) methods to rationalize the structural requirements responsible for the inhibitory activity of these compounds. The global minimum energy conformer of the template molecule, the most active and pharmacokinetically stable molecule of the series, was obtained by systematic search and used to build structures of the molecules in the dataset. The best predictions for the CCR5-receptor were obtained with the CoMFA standard model (q ² = 0.787, r ² = 0.962) and CoMSIA model combined steric, electrostatic and hydrophobic fields (q ² = 0.809, r ² = 0.951). The predictive ability of CoMFA and CoMSIA were determined using a test set of 12 compounds giving predictive correlation coefficients of 0.855 and 0.83, respectively, indicating good predictive power. Further, the robustness of the model was verified by bootstrapping analysis. The contour maps produced by the CoMFA and CoMSIA models were used to identify the structural features relevant to the biological activity in this series. Based on the CoMFA and CoMSIA analysis, we have identified some key features in the series that are responsible for CCR5 antagonistic activity which may be used to design more potent 1-(3,3-diphenylpropyl)-piperidinyl derivatives and predict their activity prior to synthesis. Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Three-dimensional quantitative structure–activity relationship study on paullones as CDK inhibitors using CoMSIA and CoMFA

3D-QSAR studies were conducted on a series of paullones as CDK inhibitors using three-dimensional quantitative structure-activity relationship (3D-QSAR) methods. Two methods were compared: the widely used comparative molecular field analysis (CoMFA) and the recently reported comparative molecular similarity indices analysis (CoMSIA). Systematic variations of some parameters in CoMSIA and CoMFA were performed to search for the best 3D-QSAR model. The computed results showed that the 3D-QSAR models from CoMSIA were clearly superior to those from CoMFA. Using the best model from CoMSIA analysis, a significant cross-validated q² was obtained and the predicted biological activities of the five compounds in the test set were in good agreement with the experimental values. The correlation results obtained from CoMSIA were graphically interpreted in terms of field contribution maps allowing physicochemical properties relevant for binding to be easily mapped back onto molecular structures. The features in the CoMSIA contour maps intuitively suggested where to modify a molecular structure in terms of physicochemical properties and functional groups in order to improve its binding affinity, which is very important for improving our understanding of the ligand-receptor interactions and in helping to design compounds with improved activity.     

Design of novel quinazolinone derivatives as inhibitors for 5HT7 receptor

To study the pharmacophore properties of quinazolinone derivatives as 5HT₇ inhibitors, 3D QSAR methodologies, namely Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) were applied, partial least square (PLS) analysis was performed and QSAR models were generated. The derived model showed good statistical reliability in terms of predicting the 5HT₇ inhibitory activity of the quinazolione derivative, based on molecular property fields like steric, electrostatic, hydrophobic, hydrogen bond donor and hydrogen bond acceptor fields. This is evident from statistical parameters like q² (cross validated correlation coefficient) of 0.642, 0.602 and r² (conventional correlation coefficient) of 0.937, 0.908 for CoMFA and CoMSIA respectively. The predictive ability of the models to determine 5HT₇ antagonistic activity is validated using a test set of 26 molecules that were not included in the training set and the predictive r² obtained for the test set was 0.512 & 0.541. Further, the results of the derived model are illustrated by means of contour maps, which give an insight into the interaction of the drug with the receptor. The molecular fields so obtained served as the basis for the design of twenty new ligands. In addition, ADME (Adsorption, Distribution, Metabolism and Elimination) have been calculated in order to predict the relevant pharmaceutical properties, and the results are in conformity with required drug like properties.     

A combined approach of docking and 3D QSAR study of beta-ketoacyl-acyl carrier protein synthase III (FabH) inhibitors

The enzyme FabH catalyzes the initial step of fatty acid biosynthesis via a type II fatty acid synthase. The pivotal role of this essential enzyme combined with its unique structural features and ubiquitous occurrence in bacteria has made it an attractive new target for the development of antibacterial and antiparasitic compounds. Three-dimensional quantitative structure-activity relationship (3D QSAR) studies such as comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) and docking simulations were conducted on a series of potent benzoylaminobenzoic acids. Docking studies were employed to position the inhibitors into the FabH active site to determine the probable binding conformation. A reasonable correlation between the predicated binding free energy and the inhibitory activity was found. CoMFA and CoMSIA were performed based on the docking conformations, giving q(2) of 0.637 and 0.697 for CoMFA and CoMSIA models, respectively. The predictive ability of the models was validated using a set of compounds that were not included in the training set and progressive scrambling test. Mapping the 3D QSAR models to the active site of FabH related that some important amino acid residues are responsible for protein-inhibitor interaction. These results should be applicable to the prediction of the activities of new FabH inhibitors, as well as providing structural understanding.     

An explorative study on potent Gram-negative specific LpxC inhibitors: CoMFA,CoMSIA, HQSAR and molecular docking

Pathogenic Gram-negative bacteria are responsible for nearly half of the serious human infections. Hologram quantitative structure–activity relationships (HQSAR), comparative molecular field analysis (CoMFA), and comparative molecular similarity index analysis (CoMSIA) were implemented on a group of 32 of potent Gram-negative LpxC inhibitors. The most effective HQSAR model was obtained using atoms, bonds, donor, and acceptor as fragment distinction. The cross-validated correlation coefficient (q²), non-cross-validated correlation coefficient (r²), and predictive correlation coefficient (r²_Pred) for test set of HQSAR model were 0.937, 0.993, and 0.892, respectively. The generated models were found to be statistically significant as the CoMFA model had (r²?=?0.967, q²?=?0.804, r²_Pred?=?0.827); the CoMSIA model had (r²?=?0.963, q²?=?0.752, r²_Pred?=?0.857). Molecular docking was employed to validate the results of the HQSAR, CoMFA, and CoMSIA models. Based on the obtained information, six new LpxC inhibitors have been designed.     

CoMFA and CoMSIA 3D QSAR analysis on N1-arylsulfonylindole compounds as 5-HT6 antagonists

Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) three-dimensional quantitative structure-activity relationship (3D-QSAR) studies were conducted on a series of N(1)-arylsulfonylindole compounds as 5-HT(6) antagonists. Evaluation of 20 compounds served to establish the models. The lowest energy conformer of compound 1 obtained from random search was used as template for alignment. The best predictions were obtained with CoMFA standard model (q2 = 0.643, r2 = 0.939 ) and with CoMSIA combined steric, electrostatic, hydrophobic, and hydrogen bond acceptor fields (q2 = 0.584, r2 = 0.902 ). Both the models were validated by an external test set of eight compounds giving satisfactory predictive r2 values of 0.604 and 0.654, respectively. The information obtained from CoMFA and CoMSIA 3D contour maps can be used for further design of specific 5-HT(6) antagonists.     

The 3D-QSAR study of antitumor arylsulfonylimidazolidinone derivatives by CoMFA and CoMSIA

Three-dimensional quantitative structure-activity relationship (3D-QSAR) studies for a series of arylsulfonylimidazolidinone derivatives having antitumor activity were conducted using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). The in vitro cytotoxicity against human lung carcinoma (A549) exhibited a strong correlation with steric and electrostatic factors of the molecules. Four different types of models have been built using CoMFA and CoMSIA method with AM1 charge or Gasteiger-Huckel charge. By comparison of the statistical results of these models, model I obtained by CoMFA study with AM1 showed the best predictability of the antitumor activities based on the cross-validated value (0.642), conventional r2 (0.981), standard error of estimate (0.106) and PRESS value (0.170).     

Molecular modeling study for the design of novel acetyl-CoA carboxylase inhibitors using 3D QSAR,molecular docking and dynamic simulations

Acetyl-CoA carboxylase (ACC) enzyme plays an important role in the regulation of biosynthesis and oxidation of fatty acids. ACC is a recognized drug target for the treatment of obesity and diabetes. Combination of ligand and structure-based in silico methods along with activity and toxicity prediction provides best lead compounds in the drug discovery process. In this study, a data-set of 100 ACC inhibitors were used for the development of comparative molecular field analysis (CoMFA) and comparative molecular similarity index matrix analysis (CoMSIA) models. The generated contour maps were used for the design of novel ACC inhibitors. CoMFA and CoMSIA models were used for the predication of activity of designed compounds. In silico toxicity risk prediction study was carried out for the designed compounds. Molecular docking and dynamic simulations studies were performed to know the binding mode of designed compounds with the ACC enzyme. The designed compounds showed interactions with key amino acid residues important for catalysis, and good correlation was observed between binding free energy and inhibition of ACC.