In silico QSAR studies of anilinoquinolines as EGFR inhibitors

Members of the epidermal growth factor receptor (EGFR) family of proteins are frequently overactive in solid tumors. A relatively new therapeutic approach to inhibit the kinase activity is the use of ATP-competitive small molecules. In silico techniques were employed to identify the key interactions between inhibitors and their protein receptors. A series of EGFR inhibitory anilinoquinolines was studied within the framework of hologram quantitative structure activity relationship (HQSAR), density functional theory (DFT)-based QSAR, and three-dimensional (3D) QSAR (CoMFA/CoMSIA). The HQSAR analysis implied that substitutions at certain sites on the inhibitors play an important role in EGFR inhibition. DFT-based QSAR results suggested that steric and electronic interactions contributed significantly to the activity. Ligand-based 3D-QSAR and receptor-guided 3D-QSAR analyses such as CoMFA and CoMSIA techniques were carried out, and the results corroborated the previous two approaches. The 3D QSAR models indicated that steric and hydrophobic interactions are dominant, and that substitution patterns are an important factor in determining activity. Molecular docking was helpful in identifying a bioactive conformer as well as a plausible binding mode. The docked geometry-based CoMFA model with steric and electrostatic fields effect gave q ² = 0.66, r ² = = 0.94 with r ² _predictive = 0.72. Similarly, CoMSIA with hydrophobic field gave q ² = 0.59, r ² = 0.85 with r ² _predictive = 0.63. Bulky groups around site 3 of ring “C”, and hydrophilic and bulky groups at position 6 of ring “A” are desirable, with a hydrophobic and electron-donating group at site 7 of ring “A” being helpful. Accordingly, potential EGFR inhibitors may be designed by modification of known inhibitors.     

Comparative QSAR studies on peptide deformylase inhibitors

Comparative quantitative structure–activity relationship (QSAR) analyses of peptide deformylase (PDF) inhibitors were performed with a series of previously published (British Biotech Pharmaceuticals, Oxford, UK) reverse hydroxamate derivatives having antibacterial activity against Escherichia coli PDF, using 2D and 3D QSAR methods, comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), and hologram QSAR (HQSAR). Statistically reliable models with good predictive power were generated from all three methods (CoMFA r ² = 0.957, q ² = 0.569; CoMSIA r ² = 0.924, q ² = 0.520; HQSAR r ² = 0.860, q ² = 0.578). The predictive capability of these models was validated by a set of compounds that were not included in the training set. The models based on CoMFA and CoMSIA gave satisfactory predictive r ² values of 0.687 and 0.505, respectively. The model derived from the HQSAR method showed a low predictability of 0.178 for the test set. In this study, 3D prediction models showed better predictive power than 2D models for the test set. This might be because 3D information is more important in the case of datasets containing compounds with similar skeletons. Superimposition of CoMFA contour maps in the active site of the PDF crystal structure showed a meaningful correlation between receptor–ligand binding and biological activity. The final QSAR models, along with information gathered from 3D contour and 2D contribution maps, could be useful for the design of novel active inhibitors of PDF. Figure Superimposition of comparative molecular field analysis (CoMFA) contour plot in the active site of peptide deformylase (PDF)     

3D-QSAR studies on caspase-mediated apoptosis activity of phenolic analogues

Phenols and its analogues are known to induce caspase-mediated apoptosis activity and cytotoxicity on various cancer cell lines. In the current work, two types of molecular field analysis techniques were used to perform the three dimension quantitative structure activity relationship (3D-QSAR) modeling between structural characters and anticancer activity of two sets of phenolic compounds, which are comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). Then two 3D-QSAR models for two sets of phenolic analogues were obtained with good results. The first QSAR model, which was derived from CoMFA for phenols with caspase-mediated apoptosis activity against L1210 cells, had good predictability (q ² = 0.874, r ² = 0.930), and the other one was derived from CoMSIA for electron-attracting phenols with cytotoxicity in L1210 cell (q ² = 0.836, r ² = 0.950). In addition, the CoMFA and CoMSIA contour maps provide valuable guidance for designing highly active phenolic compounds.     

Identification of novel 5-hydroxy-1<Emphasis Type="Italic">H</Emphasis>-indole-3-carboxylates with anti-HBV activities based on 3D QSAR studies

Infection with hepatitis B virus (HBV) is a major cause of liver diseases such as cirrhosis and hepatocellular carcinoma. In our previous studies, we identified indole derivatives that have anti-HBV activities. In this study, we optimize a series of 5-hydroxy-1H-indole-3-carboxylates, which exhibited potent anti-HBV activities, using three-dimensional quantitative structure-activity relationship (3D QSAR) studies with comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). The lowest energy conformation of compound 3, which exhibited the most potent anti-HBV activity, obtained from systematic search was used as the template for alignment. The best predictions were obtained with the CoMFA standard model (q ² = 0.689, r ² = 0.965, SEE = 0.082, F = 148.751) and with CoMSIA combined steric, electrostatic, hydrophobic and H-bond acceptor fields (q ² = 0.578, r ² = 0.973, SEE = 0.078, F = 100.342). Both models were validated by an external test set of six compounds giving satisfactory prediction. Based on the clues derived from CoMFA and CoMSIA models and their contour maps, another three compounds were designed and synthesized. Pharmacological assay demonstrated that the newly synthesized compounds possessed more potent anti-HBV activities than before (IC₅₀: compound 35a is 3.1 μmol/l, compound 3 is 4.1 μmol/l). Combining the clues derived from the 3D QSAR studies and from further validation of the 3D QSAR models, the activities of the newly synthesized indole derivatives were well accounted for. Furthermore, this showed that the CoMFA and CoMSIA models proved to have good predictive ability.     

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.     

Insight into the Interactions between Novel Isoquinolin-1,3-Dione Derivatives and Cyclin-Dependent Kinase 4 Combining QSAR and Molecular Docking

Several small-molecule CDK inhibitors have been identified, but none have been approved for clinical use in the past few years. A new series of 4-[(3-hydroxybenzylamino)-methylene]-4H-isoquinoline-1,3-diones were reported as highly potent and selective CDK4 inhibitors. In order to find more potent CDK4 inhibitors, the interactions between these novel isoquinoline-1,3-diones and cyclin-dependent kinase 4 was explored via in silico methodologies such as 3D-QSAR and docking on eighty-one compounds displaying potent selective activities against cyclin-dependent kinase 4. Internal and external cross-validation techniques were investigated as well as region focusing, bootstraping and leave-group-out. A training set of 66 compounds gave the satisfactory CoMFA model (q ² = 0.695, r ² = 0.947) and CoMSIA model (q ² = 0.641, r ² = 0.933). The remaining 15 compounds as a test set also gave good external predictive abilities with r ² _pred values of 0.875 and 0.769 for CoMFA and CoMSIA, respectively. The 3D-QSAR models generated here predicted that all five parameters are important for activity toward CDK4. Surflex-dock results, coincident with CoMFA/CoMSIA contour maps, gave the path for binding mode exploration between the inhibitors and CDK4 protein. Based on the QSAR and docking models, twenty new potent molecules have been designed and predicted better than the most active compound 12 in the literatures. The QSAR, docking and interactions analysis expand the structure-activity relationships of constrained isoquinoline-1,3-diones and contribute towards the development of more active CDK4 subtype-selective inhibitors.     

3D–QSAR studies of orvinol analogs as κ-opioid agonists

Orvinols are potent analgesics that target opioid receptors. However, their analgesic mechanism remains unclear and no significant preference for subtype opioid receptor has been achieved. In order to find new orvinols that target the κ-receptor, comparative 3D–QSAR studies were performed on 26 orvinol analogs using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). The best predictions for the κ-receptor were obtained with the CoMFA standard model (q ²=0.686, r ²=0.947) and CoMSIA model combined steric, electrostatic, hydrophobic, and hydrogen bond donor/acceptor fields (q ²=0.678, r ²=0.914). The models built were further validated by a test set made up of seven compounds, leading to predictive r ² values of 0.672 for CoMFA and 0.593 for CoMSIA. The study could be helpful for designing and prepare new category κ-agonists from orvinols.      

Elucidation of binding mode and three dimensional quantitative structure–activity relationship studies of a novel series of protein kinase B/Akt inhibitors

Protein kinase B (PKB; also known as Akt kinase) is located downstream in the PI-3 kinase pathway. Overexpression and constitutive activation of PKB/Akt leads to human prostate, breast and ovarian carcinomas. A series of 69 PKB/Akt inhibitors were examined to explore their binding modes using FlexX, and three-dimensional quantitative structure–activity relationship (3D-QSAR) studies based on comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed to provide structural insights into these compounds. CoMFA produced statistically significant results, with cross-validated q ² and non-cross validated correlation r ² coefficients of 0.53 and 0.95, respectively. For CoMSIA, steric, hydrophobic and hydrogen bond acceptor fields jointly yielded ‘leave one out’ q ²  = 0.51 and r ²  = 0.84. The predictive power of CoMFA and CoMSIA was determined using a test set of 13 molecules, which gave correlation coefficients, of 0.58 and 0.62, respectively. Molecular docking revealed that the binding modes of these molecules in the ATP binding sites of the Akt kinase domain were very similar to those of the co-crystallized ligand. The information obtained from 3D contour maps will allow the design of more potent and selective Akt kinase inhibitors.     

Insights into ET<Subscript>A</Subscript> subtype selectivity of benzodiazepine endothelin receptor antagonists by 3D-QSAR approaches

ET_A subtype selective antagonists constitute a novel and potentially important class of agents for the treatment of pulmonary hypertension, heart failure, and other pathological conditions. In this paper, 60 benzodiazepine derivatives displaying potent activities against ET_A and ET_B subtypes of endothelin receptor were selected to establish the 3D-QSAR models using CoMFA and CoMSIA approaches. These models show excellent internal predictability and consistency, external validation using test-set 19 compounds yields a good predictive power for antagonistic potency. Statistical parameters of models were obtained with CoMFA-ET_A (q ² = 0.787, r ² = 0.935, r ² _pred  = 0.901), CoMFA-ET_B (q ² = 0.842, r ² = 0.984, r ² _pred  = 0.941), CoMSIA-ET_A (q ² = 0.762, r ² = 0.971, r ² _pred  = 0.958) and CoMSIA-ET_B (q ² = 0.771, r ² = 0.974, r ² _pred  = 0.953) respectively. Field contour maps (CoMFA and CoMSIA) corresponding to the ET_A and ET_B subtypes reflects the characteristic similarities and differences between these types. The results of this paper provide valuable information to facilitate structural modifications of the title compounds to increase the inhibitory potency and subtype selectivity of endothelin receptor.     

Docking and 3D QSAR study of thiourea analogs as potent inhibitors of influenza virus neuraminidase

Surflex-Dock was applied to study interactions between 30 thiourea analogs and neuraminidase (NA). The docking results showed that hydrogen bonding and electrostatic interactions were highly correlated with the activities of neuraminidase inhibitors (NIs), followed by hydrophobic and steric factors. Moreover, there was a strong correlation between the predicted binding affinity (total score) and experimental pIC₅₀ (correlation coefficient r = 0.870; P < 0.0001). A three dimensional holographic vector of atomic interaction field (3D-HoVAIF) was employed to construct a QSAR model. The r ², q ² and r ² _test values of the optimal QSAR model were 0.849, 0.724 and 0.689, respectively. From the QSAR model, it could be seen that electrostatic, hydrophobic and steric interactions were closely related to inhibitory activity, which was consistent with the docking results. Based on the docking and QSAR results, five new compounds with high predicted activities were designed.     

Structural determinants of benzodiazepinedione/peptide-based p53-HDM2 inhibitors using 3D-QSAR, docking and molecular dynamics

As a tumor suppressor, p53 protein regulates the cell cycle and is involved in preventing tumorgenesis. The protein level of p53 is under the tight control of its negative regulator human double minute 2 (HDM₂) via ubiquitination. Therefore, the design of inhibitors of HDM₂ has attracted much interest of research on developing novel anticancer drugs. Presently, two classes of molecules, i.e., the 1,4-benzodiazepine-2,5-diones (BDPs) and N-Acylpolyamine (NAPA) derivatives were studied by three-dimensional quantitative structure–activity relationship (3D-QSAR) modeling approaches including the comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) as promising p53-HDM₂ inhibitors. Based on both the ligand-based and receptor-guided (docking) alignments, two optimal 3D-QSAR models were obtained with good predictive power of q ² = 0.41, r ² _pred = 0.60 for BDPs, and q ² = 0.414, r ² _pred = 0.69 for NAPA analogs, respectively. By analysis of the model and its related contour maps, it is revealed that the electrostatic interactions contributed much larger to the compound binding affinity than the steric effects. And the contour maps intuitively suggested where to modify the molecular structures in order to improve the binding affinity. In addition, molecular dynamics simulation (MD) study was also carried out on the dataset with purpose of exploring the detailed binding modes of ligand in the HDM₂ binding pocket. Based on the CoMFA contour maps and MD-based docking analyses, some key structural aspects responsible for inhibitory activity of these two classes of compounds were concluded as follows: For BDPs, the R₁ and R₃ regions should have small electronegativity groups; substituents R₂ and R₄ should be larger, and R₃ substituent mainly involves in H-bonds forming. For NAPA derivatives, bulky and electropositive groups in ring B and ring A, small substituent at region P is favorable for the inhibitory activity. The models and related information, we hope, may provide important insight into the inhibitor-p53-HDM₂ interactions and be helpful for facilitating the design of novel potent inhibitors.     

3D-QSAR study of microsomal prostaglandin E<Subscript>2</Subscript> synthase(mPGES-1) inhibitors

Microsomal prostaglandin E₂ synthase (mPGES-1) has been identified recently as a novel target for treating pain and inflammation. The aim of this study is to understand the binding affinities of reported inhibitors for mPGES-1 and further to design potential new mPGES-1 inhibitors. 3D-QSAR-CoMFA (comparative molecular field analysis) and CoMSIA (comparative molecular similarity indices analysis) - techniques were employed on a series of indole derivatives that act as selective mPGES-1 inhibitors. The lowest energy conformer of the most active compound obtained from systematic conformational search was used as a template for the alignment of 32 compounds. The models obtained were used to predict the activities of the test set of eight compounds, and the predicted values were in good agreement with the experimental results. The 3D-QSAR models derived from the training set of 24 compounds were all statistically significant (CoMFA; q ² = 0.89, r ² = 0.95, , and CoMSIA; q ² = 0.84, r ² = 0.93, , ). Contour plots generated for the CoMFA and CoMSIA models reveal useful clues for improving the activity of mPGES-1 inhibitors. In particular, substitutions of an electronegative fluorine atom or a bulky hydrophilic phenoxy group at the meta or para positions of the biphenyl rings might improve inhibitory activity. A plausible binding mode between the ligands and mPGES-1 is also proposed.     

Use of molecular modeling, docking, and 3D-QSAR studies for the determination of the binding mode of benzofuran-3-yl-(indol-3-yl)maleimides as GSK-3β inhibitors

Molecular modeling and docking studies along with three-dimensional quantitative structure relationships (3D-QSAR) studies have been used to determine the correct binding mode of glycogen synthase kinase 3β (GSK-3β) inhibitors. The approaches of comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) are used for the 3D-QSAR of 51 substituted benzofuran-3-yl-(indol-3-yl)maleimides as GSK-3β inhibitors. Two binding modes of the inhibitors to the binding site of GSK-3β are investigated. The binding mode 1 yielded better 3D-QSAR correlations using both CoMFA and CoMSIA methodologies. The three-component CoMFA model from the steric and electrostatic fields for the experimentally determined pIC₅₀ values has the following statistics: R²(cv) = 0.386 nd SE(cv) = 0.854 for the cross-validation, and R² = 0.811 and SE = 0.474 for the fitted correlation. F (3,47) = 67.034, and probability of R² = 0 (3,47) = 0.000. The binding mode suggested by the results of this study is consistent with the preliminary results of X-ray crystal structures of inhibitor-bound GSK-3β. The 3D-QSAR models were used for the estimation of the inhibitory potency of two additional compounds.     

Receptor based 3D-QSAR to identify putative binders of Mycobacterium tuberculosis Enoyl acyl carrier protein reductase

In the current study, the applicability and scope of 3D-QSAR models (CoMFA and CoMSIA) to complement virtual screening using 3D pharmacophore and molecular docking is examined and applied to identify potential hits against Mycobacterium tuberculosis Enoyl acyl carrier protein reductase (MtENR). Initially CoMFA and CoMSIA models were developed using series of structurally related arylamides as MtENR inhibitors. Docking studies were employed to position the inhibitors into MtENR active site to derive receptor based 3D-QSAR models. Both CoMFA and CoMSIA yielded significant cross validated q² values of 0.663 and 0.639 and r² values of 0.989 and 0.963, respectively. The statistically significant models were validated by a test set of eight compounds with predictive r² value of 0.882 and 0.875 for CoMFA and CoMSIA. The contour maps from 3D-QSAR models in combination with docked binding structures help to better interpret the structure activity relationship. Integrated with CoMFA and CoMSIA predictive models structure based (3D-pharmacophore and molecular docking) virtual screening have been employed to explore potential hits against MtENR. A representative set of 20 compounds with high predicted IC₅₀ values were sorted out in the present study.     

Discovery of FIXa inhibitors by combination of pharmacophore modeling,molecular docking,and 3D-QSAR modeling

Human Coagulation Factor IXa (FIXa), specifically inhibited at the initiation stage of the blood coagulation cascade, is an excellent target for developing selective and safe anticoagulants. To explore this inhibitory mechanism, 86 FIXa inhibitors were selected to generate pharmacophore models and subsequently SAR models. Both best pharmacophore model and ROC curve were built through the Receptor–Ligand Pharmacophore Generation module. CoMFA model based on molecular docking and PLS factor analysis methods were developed. Model propagations values are q²?=?0.709, r²?=?0.949, and r²_pred?=?0.905. The satisfactory q² value of 0.609, r² value of 0.962, and r²_pred value of 0.819 for CoMSIA indicated that the CoMFA and CoMSIA models are both available to predict the inhibitory activity on FIXa. On the basis of pharmacophore modeling, molecular docking, and 3D-QSAR modeling screening, six molecules are screened as potential FIXa inhibitors.     

A combined molecular modeling study on a series of pyrazole/isoxazole based human Hsp90α inhibitors

Inhibition of the protein chaperone Hsp90α is a promising approach for cancer therapy. In this work, a molecular modeling study combining pharmacophore model, molecular docking and three-dimensional quantitative structure-activity relationships (3D-QSAR) was performed to investigate a series of pyrazole/isoxazole scaffold inhibitors of human Hsp90α. The pharmacophore model can provide the essential features required for the biological activities of the inhibitors. The molecular docking study can give insight into the binding mode between Hsp90α and its inhibitors. 3D-QSAR based on CoMFA and CoMSIA models were performed from three different strategies for conformational selection and alignment. The receptor-based models gave the most statistically significant results with cross-validated q ² values of 0.782 and 0.829 and r ² values of 0.909 and 0.968, for CoMFA and CoMSIA respectively. Furthermore, the 3D contour maps superimposed within the binding site of Hsp90α could help to understand the pivotal interaction and the structural requirements for potent Hsp90α inhibitors. The results show 4-position of pyrazole/isoxazole ring requires bulky and hydrophobic groups, and bulky and electron repulsion substituent of 5-amides is favorable for enhancing activity. This study will be helpful for the rational design of new potent Hsp90α inhibitors.