Pharmacophore elucidation and 3D-QSAR analysis of a new class of highly potent inhibitors of acid ceramidase based on maximum common substructure and field fit alignment methods

Comparative molecular field analysis (CoMFA) and comparative similarity indices analysis (CoMSIA) studies have been carried on a series of 2,4-dioxopyrimidine-1-carboxamides as acid ceramidase inhibitors. Two alignment rules for the compounds were defined using maximum common substructure and field fit. The best orientation was then searched by all-orientation search strategy, to minimize the effect of the initial orientation of the structures. The Kennard Stone algorithm was used to divide the entire set into training (25 compounds) and test (7 compounds) sets. Pharmacophore model identification was also performed using DISCOtech algorithm and refinement was carried out using GASP, to highlight important structural features that could be responsible for the inhibitory activity. All constructed models showed appropriate statistical parameters in terms of q ² and r _pred ² . Based upon the information obtained from CoMFA, CoMSIA, and developed pharmacophore pattern, some key features that may be used to design new inhibitors for acid ceramidase have been identified.     

Comparative QSAR studies using HQSAR,CoMFA, and CoMSIA methods on cyclic sulfone hydroxyethylamines as BACE1 inhibitors

The inhibition of β-secretase (BACE1) is currently the main pharmacological strategy available for Alzheimer’s disease (AD). 2D QSAR and 3D QSAR analysis on some cyclic sulfone hydroxyethylamines inhibitors against β-secretase (IC₅₀: 0.002–2.75 μM) were carried out using hologram QSAR (HQSAR), comparative molecular field analysis (CoMFA), and comparative molecular similarity indices analysis (CoMSIA) methods. The best model based on the training set was generated with a HQSAR q² value of 0.693 and r² value of 0.981; a CoMFA q² value of 0.534 and r² value of 0.913; and a CoMSIA q² value of 0.512 and r² value of 0.973. In order to gain further understand of the vital interactions between cyclic sulfone hydroxyethylamines and the protease, the analysis was performed by combining the CoMFA and CoMSIA field distributions with the active sites of the BACE1. The final QSAR models could be helpful in the design and development of novel active BACE1 inhibitors.     

3D QSAR Studies on Benzyl Phenyl Ether Diamidine Derivatives with Antiprotozoal Activities

Human African trypanosomiasis (HAT) is a neglected tropical disease, and some drugs treating HAT have been used for even more than 60 years. Currently, a series of benzyl phenyl ether diamidine derivatives are discovered, which exhibit high antiprotozoal activities and low cytotoxicity, leading to good development prospects. The comparative molecular field analysis (CoMFA) and the comparative molecular similarity indices analysis (CoMSIA) are used to study the relationship between the structure and antiprotozoal activities. The established 3D QSAR model shows not only significant statistical quality, but also satisfies predictive ability: the best CoMFA model had r² = 0.958 and q² = 0.766, the best CoMSIA model had r² = 0.957 and q² = 0.812, the predictive ability of CoMFA and CoMSIA model were further confirmed by a test set which had 11 compounds, giving the correlation coefficient Q_ext² of 0.792, 0.873, respectively. The contour maps and contribution maps show important features that can improve the antiprotozoal activity: position 3 from substituent R4 should be a low electronegativity group, position 4 from substituent R₄ should have higher electronegativity, substituent R₂ should be selected to a low electronegativity and small bulk group. Together these results may offer some useful theoretical information in designing potential inhibitors.     

CoMFA and CoMSIA studies on C-aryl glucoside SGLT2 inhibitors as potential anti-diabetic agents

SGLT2 has become a target of therapeutic interest in diabetes research. CoMFA and CoMSIA studies were performed on C-aryl glucoside SGLT2 inhibitors (180 analogues) as potential anti-diabetic agents. Three different alignment strategies were used for the compounds. The best CoMFA and CoMSIA models were obtained by means of Distill rigid body alignment of training and test sets, and found statistically significant with cross-validated coefficients (q ²) of 0.602 and 0.618, respectively, and conventional coefficients (r ²) of 0.905 and 0.902, respectively. Both models were validated by a test set of 36 compounds giving satisfactory predicted correlation coefficients (r ² _pred) of 0.622 and 0.584 for CoMFA and CoMSIA models, respectively. A comparison was made with earlier 3D QSAR study on SGLT2 inhibitors, which shows that our 3D QSAR models are better than earlier models to predict good inhibitory activity. CoMFA and CoMSIA models generated in this work can provide useful information to design new compounds and helped in prediction of activity prior to synthesis.     

Structure-based CoMFA and CoMSIA study of indolinone inhibitors of PDK1

Phosphoinositide-dependent protein kinase-1 (PDK1) is a Ser/Thr kinase which phosphorylates and activates members of the AGC kinase group known to control processes such as tumor cell growth, protection from apoptosis, and tumor angiogenesis. In this paper, CoMFA and CoMSIA studies were carried out on a training set of 56 conformationally rigid indolinone inhibitors of PDK1. Predictive 3D QSAR models, established using atom fit alignment rule based on crystallographic-bound conformation, had cross-validated (r _cv²) values of 0.738 and 0.816 and non-cross-validated (r _ncv²) values of 0.912 and 0.949 for CoMFA and CoMSIA models, respectively. The predictive ability of the CoMFA and CoMSIA models was determined using a test set of 14 compounds, which gave predictive correlation coefficients (r _pred²) of 0.865 and 0.837, respectively. Structure-based interpretation of the CoMFA and CoMSIA field properties provided further insights for the rational design of new PDK1 inhibitors. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

3D-QSAR and Docking Studies of Pyrido[2,3-d]pyrimidine Derivatives as Wee1 Inhibitors

In order to investigate the inhibiting mechanism and obtain some helpful information for de-signing functional inhibitors against Wee1, three-dimensional quantitative structure-activity relationship (3D-QSAR) and docking studies have been performed on 45 pyrido[2,3-d] pyrim-idine 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 val-idation 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, essen-tial parameter r²_m values of 0.792 and 0.826, the leave-one-out r²_m(LOO) values of 0.781 and 0.809, r²_m(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 sub-stituents 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.     

五元杂环并嘧啶类胸苷酸合成酶抑制剂的构效关系和分子对接

用比较分子场分析法(CoMFA)和比较分子相似性指数分析法(CoMSIA)研究了38个五元杂环并嘧啶衍生物类胸苷酸合成酶抑制剂的三维定量构效关系(3D-QSAR), 建立了相关预测模型. CoMFA和CoMSIA模型的交互验证相关系数q²分别为0.662和0.672、非交互验证相关系数R²分别为0.921和0.884、外部交互验证相关系数Q_ext²分别为0.85和0.81. 分子对接得到的结合模式与三维定量构效关系得到的结果一致. 结果表明这两种模型都具有良好的预测能力, 可应用于指导化合物的设计和结构修饰, 为进一步设计新型胸苷酸合成酶抑制剂提供了理论依据.     

Docking-based 3D-QSAR study of pyridyl aminothiazole derivatives as checkpoint kinase 1 inhibitors

Checkpoint kinase 1 (Chk1) is a promising target for the design of novel anticancer agents. In the present work, molecular docking simulations and three-dimensional quantitative structure–activity relationship (3D-QSAR) studies were performed on pyridyl aminothiazole derivatives as Chk1 inhibitors. AutoDock was used to determine the probable binding conformations of all the compounds inside the active site of Chk1. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models were developed based on the docking conformations and alignments. The CoMFA model produced statistically significant results with a cross-validated correlation coefficient (q²) of 0.608 and a coefficient of determination (r²) of 0.972. The reliable CoMSIA model with q² of 0.662 and r² of 0.970 was obtained from the combination of steric, electrostatic and hydrogen bond acceptor fields. The predictive power of the models were assessed using an external test set of 14 compounds and showed reasonable external predictabilities (r²_pred) of 0.668 and 0.641 for CoMFA and CoMSIA models, respectively. The models were further evaluated by leave-ten-out cross-validation, bootstrapping and progressive scrambling analyses. The study provides valuable information about the key structural elements that are required in the rational design of potential drug candidates of this class of Chk1 inhibitors.     

Pharmacophore interactions analysis and prediction of inhibitory activity of 1,7-diazacarbazoles as checkpoint kinase 1 inhibitors: application of molecular docking, 3D-QSAR and RBF neural network

In the present study, we mainly focused on new synthesized 1,7-diazacarbazole derivatives (44 active molecules) as Chk1 inhibitors to build 3D-QSAR model. Comparative molecular field analysis (CoMFA) model with three principal components was developed. The relative contributions in building of CoMFA model were 64.41 % for steric field and 35.59 % for electrostatic field. R ² values for training and test sets of CoMFA model were 0.8724 and 0.7818, respectively, and squared correlation coefficient for leave-one-out cross-validation test (q ²) was 0.6753. To improve the predictive power, a new 3D-QSAR model was developed by using radial basis function network (RBFN) and score of CoMFA interactions energy values as input variables. Scores 1, 2 and 3 were used as input variables, and a RBFN model with seven centers and spread value equal to 95 was developed to create a nonlinear 3D-QSAR model. R ² values for training and test sets were 0.9613 and 0.8564, and q ² for leave-one-out cross-validation test was 0.9258. Docking of all molecules to 3DX ligand binding site of Chk1 receptor indicated six interactions as pharmacological interactions between compounds and binding site of receptors. These pharmacological interactions were hydrogen bonding with LEU-15 and GLU-85 in main chain and four van der Waals interactions with LEU-15, VAL-23, TYR-86 and LEU-137 in side chain. CoMFA contour plots were used to design new inhibitors, and inhibitory activity of each compound was predicted by using CoMFA and RBFN models.     

Docking-based CoMFA and CoMSIA study of azaindole carboxylic acid derivatives as promising HIV-1 integrase inhibitors

Three-dimensional quantitative structure–activity relationship (3D-QSAR) studies were performed based on a series of azaindole carboxylic acid derivatives that had previously been reported as promising HIV-1 integrase inhibitors. Docking studies to explore the binding mode were performed based on the highly active molecule 36. The best docked conformation of molecule 36 was used as template for alignment. The comparative molecular field analysis (CoMFA) model (including steric and electrostatic fields) yielded the cross validation q ² = 0.655, non-cross validation r ² = 0.989 and predictive r ² _pred = 0.979. The best comparative molecular similarity indices analysis (CoMSIA) model (including steric, electrostatic, hydrophobic and hydrogen-bond acceptor fields) yielded the cross validation q ² = 0.719, non-cross validation r ² = 0.992 and predictive r ² _pred = 0.953. A series of new azaindole carboxylic acid derivatives were designed and the HIV-1 integrase inhibitory activities of these designed compounds were predicted based on the CoMFA and CoMSIA models.     

CoMFA,CoMSIA, Topomer CoMFA,HQSAR, molecular docking and molecular dynamics simulations study of triazine morpholino derivatives as mTOR inhibitors for the treatment of breast cancer

mTOR has become a promising target for many types of cancer like breast, lung and renal cell carcinoma. CoMFA, CoMSIA, Topomer CoMFA and HQSAR were performed on the series of 39 triazine morpholino derivatives. CoMFA analysis showed q² value of 0.735, r²_cv value of 0.722 and r²_pred value of 0.769. CoMSIA analysis (SEHD) showed q² value of 0.761, r²_cv value of 0.775 and r²_pred value of 0.651. Topomer CoMFA analysis showed q² value of 0.693, r² (conventional correlation coefficient) value of 0.940 and r²_pred value of 0.720. HQSAR analysis showed q²,r²and r²_pred values of 0.694, 0.920 and 0.750, respectively. HQSAR analysis with the combination of atomic number (A), bond type (B) and atomic connections showed q² and r² values of 0.655 and 0.891, respectively. Contour maps from all studies provided significant insights. Molecular docking studies with molecular dynamics simulations were carried out on the highly potent compound 36. Furthermore, four acridine derivatives were designed and docking results of these designed compounds showed the same interactions as that of the standard PI-103 which proved the efficiency of 3D-QSAR and MD/MS study. In future, this study might be useful prior to synthesis for the designing of novel mTOR inhibitors.     

CBP/P300溴结构域联芳基类抑制剂三维定量构效关系研究

CREB结合蛋白(CBP)和与其高度同源的P300蛋白是组蛋白乙酰化酶的两个亚型,两者通过它们的溴结构域(bromodomain,BRD)与染色质结合,目前,CBP/P300已经成为人类在肿瘤靶点领域中的研究热点。本研究基于CBP/P300溴结构域联芳基类抑制剂建立三维定量构效关系,采用比较分子力场分析法(Co MFA)和比较分子相似性指数分析法(Co MSIA)分别建立35个已知活性抑制剂的3D-QSAR模型,以确定CBP/P300溴结构域联芳基类抑制剂分子结构与生物活性之间的定量关系。Co MFA和Co MSIA模型活性数据p IC50的预测值与实验值基本一致,说明这两个模型具有较高的预测能力和统计学意义。根据Co MFA和Co MSIA模型所提供的立体场、静电场、疏水场、氢键给体场、氢键供体场等信息提出了改善此类抑制剂活性的药物设计思路,为指导设计具有更高活性的新分子和预测更加有效的CBP/P300溴结构域抑制剂提供理论依据。     

Design,synthesis and computational approach to study novel pyrrole scaffolds as active inhibitors of enoyl ACP reductase (InhA) and Mycobacterium tuberculosis antagonists

Novel 54 pyrrolyl acetohydrazide analogues were designed, synthesized and screened for antitubercular activity against InhA. Enoyl-ACP reductase/InhA one of the significant enzymes implicated in type II FAS (fatty acid synthase) biosynthetic pathway of bacterial outer cell membrane, in addition Mycobacterium tuberculosis H37Rv inhibition potency has proven to be one of the most promising drug target used for designing and testing against TB. In silico molecular modeling was achieved using Surflex-docking method to recognize important binding sites of the enoyl-ACP reductase. 3D-QSAR studies like CoMFA and CoMSIA approaches were studied to create 3D-QSAR depictions for InhA inhibitors. Based on docking results the synthesized molecules are oriented towards the core of the active site. The pyrolyl acetohydrazides exhibited one or two H-bonding connections with InhA enzyme. Molecule 8l (MIC 0.4 μg/mL, InhA- 70% at 50 μM) showed H-bonding connections with Tyr158 and NAD⁺ in a similar mode to that of ligand pyrrolidine carboxamide. The tested molecules also showed good antibacterial (Escherichia coli, Staphylococcus aureus) activity (MIC 0.4–25 μg/mL), while the study against A549 lung adenocarcinoma cell line confirms nontoxic nature of the reported molecules. The QSAR model from CoMFA and CoMSIA through the database configuration showed the most excellent data. The prognostic ability of CoMFA and CoMSIA representations was developed by a test set of 15 molecules that produced cross validated correlation coefficients (q²) of 0.642 and 0.701, respectively. This study gives insight into structural requirement needed for the development of more active InhA inhibitors through in silico approach.     

Studies on molecular mechanism between SHP2 and pyridine derivatives by 3D-QSAR,molecular docking and MD simulations

BackgroundSrc homology 2 (SH2)-containing protein tyrosine phosphatase 2 (SHP2) as a major phosphatase would affect the development of tumors by regulating several cellular processes, and is a significant potential target for cancer treatment.MethodsIn the present work, a series of pyridine derivatives possessing a wide range of inhibitory activity was employed to investigate the structural requirements by developing three dimensional quantitative structure–activity relationship (3D-QSAR) models using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methods. The results show that CoMFA (R²_cv = 0.646, R²_pred = 0.5587) and CoMSIA (R²_cv = 0.777, R²_pred = 0.7131) have excellent stability and predictability. The relationship between the inhibitory activity and structure of the inhibitors was analyzed by the derived contour maps. Furthermore, the QSAR models were validated by molecular docking and molecular dynamics simulations, which were also applied to reveal the potential molecular mechanism of these inhibitors.FindingsIt was found that Arg110, Asn216, Thr218, Thr252 and Pro490 play a crucial role in stabilizing the inhibitors. Additionally, MM/PBSA calculations provided the binding free energy were also conducted to explain the discrepancy of binding activities. Overall, the outcomes of this work could provide useful information and theoretical guidance for the development of novel and potent SHP2 inhibitors.     

Investigation of indirubin derivatives: a combination of 3D-QSAR,molecular docking,and ADMET towards the design of new DRAK2 inhibitors

Kinase-related apoptosis-inducing kinase 2 (DRAK2) is a serine/threonine kinase and belongs to the death-associated protein kinase DPAK family, which is responsible for induction of apoptosis in many cell types. Thus, DRAK2 is regarded as a promising target for treatment of autoimmune diseases. To investigate the binding between DRAK2 and indirubin inhibitors and design potent inhibitors, a three-dimensional quantitative structure-activity relationship (3D-QSAR) and molecular docking were performed. Comparative Molecular Similarity Indices Analysis (CoMSIA) was developed using 33 molecules having pIC₅₀ ranging from 8.523 to 5.000 (IC₅₀ in nM). The best CoMSIA model gave a significant coefficient of determination (R²?=?0.93), as well as a (leave-one-out cross-validation coefficient Q² of 0.81. The predictive ability of this model was evaluated by external validation using a test set of eight compounds and yielded a predicted coefficient of determination R²_test of 0.94. The contour maps could provide structural features to improve inhibitory activity. Good consistency between contour maps and molecular docking strongly suggests that the molecular modeling is reliable. Based on these satisfactory results, we designed several new DRAK2 inhibitors and their inhibitory activities were predicted using different models, which are developed on different training and test sets. Additionally, these newly designed inhibitors showed promising results in the preliminary in silico ADMET evaluations compared to the best inhibitor from the studied dataset. This study could be useful in lead identification and optimization for early drug discovery of DRAK2 inhibitors.     

3D-QSAR and molecular docking studies of 4-methyl quinazoline derivatives as PI3Kα inhibitors

A new series of 4- methyl quinazoline derivatives was synthesized and its anti-cancer activity was assessed. It was revealed that its compounds have potent inhibition on related phosphoinositide 3-kinases alpha (PI3Kα). In this study, the three-dimensional quantitative structure-activity relationship (3D-QSAR) and molecular docking approaches were performed on a series of 4-methyl quinazoline derivatives with PI3Kα inhibitors. The 3D-QSAR study was applied using Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) methods, which gave the cross-validation coefficient (Q²) values of 0.850 and 0.92, the determination coefficient (R²) values of 0.998 and 0.987, and the standard error of the estimate (SEE) values of 0.017 and 0.105, respectively. The acceptable values of determination coefficient (R² test) to CoMFA and CoMSIA respectively corresponding to values of 0.793 and 0.804 utilizing a test set of seven molecules prove the high predictive ability of this model. Using AutoDock tools, Molecular docking analysis was utilized to validate 3D-QSAR methods and to explain the binding site interactions and energy between the most active ligands and the PI3Kα (PDB ID: 4JPS) receptor. Based on these results, a novel series of 4- methyl quinazoline derivatives was predicted.