Pharmacophore modeling of some novel indole β-diketo acid and coumarin-based derivatives as HIV integrase inhibitors

To design new chemotypes with enhanced potencies against the HIV integrase enzyme, 3D pharmacophore models were generated and QSAR study was carried out on 44 novel indole β-diketo acid derivatives and coumarin-based Inhibitors. A five-point pharmacophore with two hydrogen bond acceptors (A) and three aromatic rings (R) as pharmacophore features was developed by PHASE module of Schrodinger suite. The pharmacophore hypothesis yielded a statistically significant 3D-QSAR model, with a correlation coefficient of R ² = 0.81 for training set compounds. The model generated showed excellent predictive power, with a correlation coefficient of Q ² = 0.69 for a randomly chosen test set of eight compounds. The 3D-QSAR plots illustrated insights into the structure activity relationship of these compounds which may helps in the design and development of novel integrase inhibitors.     

3D-QSAR and docking studies on pyridopyrazinones as BRAF inhibitors

BRAF has become an important and exciting therapeutic target toward human cancer. 3D-QSAR and docking studies were performed to explore the interaction of the BRAF with a series of pyridopyrazinones. The comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methods were carried out in terms of their potential for predictability. The CoMFA and CoMSIA models using 71 compounds in the training set gave r _cv² values of 0.567 and 0.662, r ² values of 0.900 and 0.907, respectively. The 3D contour maps generated by the CoMFA and CoMSIA models were used to identify the key structural requirements responsible for the biological activity. Molecular docking was applied to explore the binding mode between the ligands and the receptor. The information obtained by 3D-QSAR models may be useful to design novel potential BRAF inhibitors.     

Pharmacophore model generation and 3D-QSAR analysis of N-acyl and N-aroylpyrazolines for enzymatic and cellular B-Raf kinase inhibition

A successful 3D-QSAR study has been performed for amino-substituted N-acyl and N-aroylpyrazolines as B-Raf kinase inhibitors by means of a common five-point pharmacophore model. In this study, highly predictive 3D-QSAR models have been developed for B-Raf kinase inhibition and pERK inhibition using AADRR-2 and AADRR-6 hypothesis, respectively. The best models showed statistically outstanding values of 0.97, 0.95 and 0.91, 0.87 for r ² and q ² for AADRR-2 and AADRR-6 hypothesis, respectively. The validation of the PHASE model was done by dividing the dataset into training and test set. From the QSAR model, it can implicated that electron-withdrawing and hydrophobic groups are not advantageous for both enzymatic and cellular activities. However, H-bond donor characteristic is favorable for cellular inhibition and unfavorable for enzymatic inhibition. Based on the findings of the 3D-QSAR study, novel and promising compounds for B-Raf kinase inhibition can be synthesized.     

噻唑-吡唑啉衍生物作为EGFR抑制剂的3D-QSAR和分子对接模型研究

目的 建立EGFR抑制剂结构和活性之间的关系模型,基于对分子活性产生影响的重要结构性因素的信息,设计新的抑制剂分子并预测其活性,为抑制剂分子的设计提供依据。方法 使用Discovery Studio 2019软件进行3D-QSAR的研究以及偏最小二乘的计算;利用Autodock进行分子对接;使用LigPlot研究二维相互作用。结果 模型具有较高的q²(0.521),和r²(r²_training=0.993,r²_test=0.916,r²_blind=0.940),表明模型具有较高的预测能力和拟合能力。结论 预测结果表明,新设计的化合物活性较高,为EGFR抑制剂分子的设计提供了参考。     

3D-QSAR and docking studies of arylmethylamine-based DPP IV inhibitors

The present work was focused on the study of the three-dimensional (3D) structural requirements for the highly potent bioactivity of dipeptidyl peptidase (DPP) IV's inhibitor. At first, molecular dynamic and mechanic (MD/MM) simulations were performed to research the conformations of the potent DPP IV's inhibitor 5-(aminomethyl)-6-(2,4-dichlorophenyl)-2-(3,5-dimethoxy-phenyl)pyrimidin-4-amine. Using the MD/MM-determined molecular conformers as templates, the 3D quantitative structure activity relationship (QSAR) studies were carried out based on a set of arylmethylamine DPP IV inhibitors with the comparative molecular field analysis (CoMFA) approach. The best 3D-QSAR model was constructed with good statistic values of r_cv² and R² using PLS analyses (CoMFA: r_cv²=0.660, R²=0.953). The generated 3D-QSAR model was proved to be reliable by internal and external validations. Docking studies were further performed to analyze the interaction mode between the highly potent or low potent arylmethylamine derivatives and DPP IV. Our flexible docking results also confirmed the possible bioactive conformation obtained from the 3D-QSAR model, of arylmethylamine-based DPP IV inhibitors. The 3D-QSAR model may provide information of pharmacophoric features for further design and optimization of new scaffold compounds with high inhibitory activity to DPP IV.     

Three-dimensional quantitative structure–activity relationship (3D-QSAR) analysis and molecular docking of ATP-competitive triazine analogs of human mTOR inhibitors

The mTOR (mammalian target of rapamycin), a serine/threonine kinase has been identified as an important target for cancer. A 3D-QSAR analysis was carried out on 40 triazine based analogs of ATP-competitive mTOR kinase inhibitors. The study includes molecular field analysis (MFA) with G/PLS to predict the steric and electrostatic molecular field requirement for the activity of inhibitors. The QSAR model was developed using a training set of 33 compounds. The analyzed MFA model revealed a good fit, having r ² value of 0.897 and r _cv ² value of 0.718. The predictive power of the model generated was validated using a test set comprising 7 molecules with r _pred ² value of 0.826. The analysis of the best MFA model provided insights into the structure–activity correlation of mTOR kinase inhibitors. Molecular docking studies revealed that all inhibitors bind in the ATP pocket of the kinase domain. Our QSAR model and molecular docking results corroborate with each other and propose directions for the design of new inhibitors with better activity toward mTOR kinase.     

Pharmacophore-based 3D-QSAR study of fungal chitin synthase inhibitors

To design new compounds with enhanced activity against the fungal chitin synthase enzyme, 3D-pharmacophore models were generated and QSAR study was carried out on 44 novel homoallylamines and related compounds, nikkomycin, maleimide, chalcones, and quinolin-2-one derivatives. A three-point pharmacophore with two hydrophobic (H) and one aromatic ring (R) as pharmacophore features was developed by PHASE module of Schrodinger molecular modeling suite. The pharmacophore hypothesis yielded a statistically significant 3D-QSAR model, with a correlation coefficient of R ² of 0.84 for training set compounds. The model generated showed excellent predictive power, with a correlation coefficient of Q ² of 0.63 and Pearson-R value of 0.82 for a randomly chosen test set of nine compounds. The 3D-QSAR model explains the structure–activity relationship of these compounds which may help in the design and development of novel fungal chitin synthase inhibitors.     

Precise prediction of activators for the human constitutive androstane receptor using structure-based three-dimensional quantitative structure–activity relationship methods

The constitutive androstane receptor (CAR, NR1I3) regulates the expression of numerous drug-metabolizing enzymes and transporters. The upregulation of various enzymes, including CYP2B6, by CAR activators is a critical problem leading to clinically severe drug–drug interactions (DDIs). To date, however, few effective computational approaches for identifying CAR activators exist. In this study, we aimed to develop three-dimensional quantitative structure–activity relationship (3D-QSAR) models to predict the CAR activating potency of compounds emerging in the drug-discovery process. Models were constructed using comparative molecular field analysis (CoMFA) based on the molecular alignments of ligands binding to CAR, which were obtained from ensemble ligand-docking using 28 compounds as a training set. The CoMFA model, modified by adding a lipophilic parameter with calculated logD_7.4 (S+logD_7.4), demonstrated statistically good predictive ability (r² = 0.99, q² = 0.74). We also confirmed the excellent predictability of the 3D-QSAR model for CAR activation (r²_pred = 0.71) using seven compounds as a test set for external validation. Collectively, our results indicate that the 3D-QSAR model developed in this study provides precise prediction of CAR activating potency and, thus, should be useful for selecting drug candidates with minimized DDI risk related to enzyme-induction in the early drug-discovery stage.     

Computation of pharmacophore models for the prediction of mitogen-activated protein kinase activated protein kinase-2 inhibitory activity of pyrrolopyridines

This article is an attempt to formulate the three-dimensional pharmacophore modelling of pyrrolopyridine derivatives inhibiting mitogen-activated protein kinase activated protein kinase-2 (MK₂). To understand the essential structural features for MK₂ inhibitors, pharmacophore hypothesis were built on the basis of a set of known MK₂ inhibitors selected from literature using PHASE program. Three pharmacophore models with one hydrogen-bond acceptor (A), two hydrogen-bond donors (D), one hydrophobic group (H) and one aromatic ring (R) as pharmacophoric features were developed. Amongst them the pharmacophore hypothesis ADDHR1 yielded a statistically significant 3D-QSAR model with 0.926 as R ² value and was considered to be the best pharmacophore hypothesis. The developed pharmacophore model was externally validated by predicting the activity of test set molecules. The squared predictive correlation coefficient of 0.882 was observed between experimental and predicted activity values of test set molecules. The geometry and features of pharmacophore was expected to be useful for the design of selective MK₂ inhibitors.     

Three-dimensional quantitative structure–activity relationship CoMFA/CoMSIA on pyrrolidine-based tartrate diamides as TACE inhibitors

A series of pyrrolidine-based tartrate diamides having selective tumor necrosis factor-α converting enzyme (TACE) inhibitory activity was selected for the three-dimensional quantitative structure–activity relationship (3D-QSAR) studies. Total 76 compounds were selected by considering a high deviation in the biological activity and structural variations. The quality and predictive power of 3D-QSAR, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models for the atom-based, centroid/atom-based, data-based alignments were performed. Various models were developed with the help of these alignments. The best model was developed with data-based alignment. The optimal predictive CoMFA model was obtained with cross-validated r ² = 0.53 with six component, non-cross-validated r ² = 0.94, standard error of estimates 0.23, F-value = 121.98 and optimal CoMSIA model was obtained with cross-validated r ² = 0.53 with five components, non-cross-validated r ² = 0.93, standard error of estimates = 0.24 and F-value = 138.83. These models also showed the best test set prediction with predictive r ² value of 0.65 and 0.73, respectively. Thus, on the basis of predictive power COMSIA model appeared to be the best one. The statistical parameters from these models indicate that the data are being well fitted and also have high predictive ability. Moreover, the resulting 3D-CoMFA/CoMSIA contour maps provide useful guidance for designing of highly active TACE inhibitors.     

A comprehensive structure–activity analysis 2,3,5-trisubstituted 4,5-dihydro-4-oxo-3H-imidazo [4,5-c] pyridine derivatives as angiotensin II receptor antagonists: using 2D- and 3D-QSAR approach

The QSAR studies were performed on a series of 2,3,5-trisubstituted 4,5-dihydro-4-oxo-3H-imidazo [4,5-c] pyridine derivatives as angiotensin II AT₁ receptor antagonists activity to find out the structural features requirements for the antihypertensive activity. The QSAR study was carried out on V-life Molecular Design Suite software and the derived best QSAR model by partial least square principal component regression and multiple linear regression method showed variation in biological activity. The statistically best significant model with high-correlation coefficient (r ² = 0.9425) was selected for further study and the resulted validation parameters of the model, cross-validated correlation coefficient (q ² = 0.7786 and pred_r ² = 0.8562) show the model has good predictive activities. The model showed that the parameters SdssCcount, SssNHcount, and SaaaCcount and H_Donor count are highly correlated with angiotensin II AT₁ receptor antagonists activity of 2,3,5-trisubstituted 4,5-dihydro-4-oxo-3H-imidazo [4,5-c] pyridine derivatives. Partial least square (PLS) methodology coupled with various feature selection methods viz. stepwise, simulated annealing and genetic algorithm were applied to derive 3D-QSAR models which were further validated for statistical significance and predictive ability by internal and external validation. Molecular field analysis was used to construct the best 3D-QSAR model-7 using k-nearest neighbor (kNN) method, showing good correlative and predictive capabilities in terms of q ² = 0.8316 and pred_r ² = 0.8152. Both 2D-and 3D-QSAR study of such derivatives provide guidance for further lead optimization and designing of potent anti-hypertensive agents.     

Design of novel focal adhesion kinase inhibitors using 3D-QSAR and molecular docking

Focal adhesion kinase (FAK) plays a primary role in regulating the activity of many signaling molecules. Increased FAK expression has been implicated in a series of cellular processes, including cell migration and survival. Inhibiting the activity of FAK for cancer therapy is currently under investigation. Hence, FAK and its inhibitors have been the subject of intensive research. To understand the structural factors affecting inhibitory potency, molecular docking and 3D-QSAR modeling were studied in this project. CoMFA and CoMSIA methods were used for deriving 3D-QSAR models, which were trained with 78 compounds and then were evaluated for predictive ability with additional 19 compounds. Two substructure-based 3D-QSAR models, including CoMFA model (r ² = 0.930; q ² = 0.629) and CoMSIA model (r ² = 0.952; q ² = 0.586), had a good quality to predict the biological activities of new compounds. Meanwhile, using the flexible docking strategy, two docking-based 3D-QSAR models (CoMFA with r ² = 0.914; q ² = 0.594; CoMSIA with r ² = 0.914; q ² = 0.524) were also constructed. The structure–activity relationship has been illustrated clearly by the contour maps gained from the 3D-QSAR models in combination with the docked binding structures. All the results indicated that it might be useful in the rational design of novel FAK inhibitors.     

3D-QSAR and 3D-QSSR studies of thieno[2,3-d] pyrimidin-4-yl hydrazone analogues as CDK4 inhibitors by CoMFA analysis

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.     

Design of potent human steroid 5α-reductase inhibitors: 3D-QSAR CoMFA, CoMSIA and docking studies

3D-QSAR CoMFA, CoMSIA and docking studies were performed on a set of 4-azasteroidal human steroid 5α-reductase inhibitors. The models developed using maximal common substructure-based alignment was found to be reliable and significant with good predictive r ² value. CoMSIA model developed using combination of steric, electrostatic, hydrophobic, hydrogen bond donor and hydrogen bond acceptor features has shown r _cv ²  = 0.564 with six optimum components, r _ncv ²  = 0.945, F value = 101.196, r _Pred ²  = 0.693 and SEE = 0.209. The contour plots obtained has shown a favourable effect of bulkier groups at C-17 position. Docking studies indicates the importance of bulkier groups at C-17 position for favourable activity. The study further helps in design of potent inhibitors of the enzyme.     

Molecular modeling study of HIV-1 gp120 attachment inhibitors

The viral glycoprotein 120 (gp120) is a glycoprotein exposed on viral surface. The gp120 is essential for virus entry into cells as it plays a vital role in seeking out specific cell surface receptors for entry. In this article, we performed docking and three-dimensional quantitative structure activity relationship (3D-QSAR) study on a series of 48 indole glyoxamide derivatives as gp120 inhibitors. Docking study revealed that the inhibitor docked deeply into the gp120 cavity rather than Phe43 of cluster of differentiation 4 (CD4). 3D-QSAR methodologies, comparative molecular field analysis (CoMFA), and comparative molecular similarity indices analysis (CoMSIA) were utilized to rationalize the structural variations with their inhibitory activities. The docked pose of the most potent molecule (43) was used to determine the structures of other molecules. The CoMFA yielded a model with cross-validated correlation coefficient of (q ²) 0.73 and non-cross-validated correlation coefficient of (r ²) 0.89 with optimum number of components (N?=?3). The CoMSIA models were obtained with the combination of various parameters. Final model was computed with steric, hydrophobic- and hydrogen-bond acceptor (SHA) parameters with reasonable statistics (q ²?=?0.80, r ²?=?0.94 and N?=?5). The predictive power of developed CoMFA and CoMSIA models were assessed by test set (nine molecules). The predictive r _pred ² for CoMFA and CoMSIA model was found to be 0.93 and 0.74, respectively. The generated contour maps were plotted onto the gp120 active site to correlate structural variations with their biological activity in protein environment. Contour map analyses showed the importance of 4-F substitution of indole ring, which made essential electronic interaction with the crucial residue (Trp427). The 3D models could explain nicely the structure–activity relationships of indole glyoxamide analogs. This would give proper guidelines to further enhance the activity of novel inhibitors.