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 (CoMFA,CoMSIA, HQSAR and topomer CoMFA), MD simulations and molecular docking studies on purinylpyridine derivatives as B-Raf inhibitors for the treatment of melanoma cancer

As per the World Health Organization (WHO), cancer is the second most leading cause of death after cardiovascular diseases in worldwide with around 9.88 million total new cases and 1.08 million were observed due to skin cancer in 2018. Amongst two types of skin cancer, progression of melanoma cancer is increasing day by day due to the environmental changes than non-melanoma cancer. Most of B-Raf mutation, specifically B-RafV600E, is responsible for the progression of the melanoma cancer. Here, various 3D-QSAR techniques like comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), molecular hologram QSAR (HQSAR) and topomer CoMFA were used to design novel B-Raf inhibitors by using 28 synthetic B-Raf inhibitors. Except for topomer CoMFA model, remaining models were generated by three different alignment methods in which distil-based alignment method was found best and gave prominent statistical values. After performing N-fold statistical validation, in CoMFA, q², r² and r²_pred values were found to be 0.638, 0.969 and 0.848, respectively. Similarly, q², r² and r²_pred values were found to be 0.796, 0.978 and 0.891 in CoMSIA (SHD) and 0.761, 0.973 and 0.852 in CoMSIA (SH) by N-fold statistical validation. In HQSAR analysis, statistical values were found for q² as 0.984, r² as 0.999 and r²_pred as 0.634 with 97 as best hologram length (BHL). The results of topomer CoMFA showed the q² value of 0.663 and the r² value of 0.967. Important features of purinylpyridine were identified by contour map analysis of all 3D-QSAR techniques, which could be useful to design the novel molecules as B-Raf inhibitors for the treatment of melanoma cancer.

     

3D CoMFA,CoMSIA, topomer CoMFA and HQSAR studies on aromatic acid esters for carbonic anhydrase inhibitory activity

Molecular modelling studies [comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), topomer CoMFA and hologram quantitative structure–activity relationship (HQSAR)] have been performed on the series of 28 molecules belonging to the series of aromatic acid ester derivatives for their carbonic anhydrase inhibitory activity. The model exhibited good correlation coefficient (r²) and cross‐validated correlation coefficient (q²) for CoMFA, CoMSIA and HQSAR methods. On the basis of the findings from all these studies, a structure–activity relationship was established. Copyright © 2013 John Wiley & Sons, Ltd.     

QSAR and Pharmacophore Studies of Thiazolidine-4-carboxylic Acid Derivatives as Novel Influenza Neuraminidase Inhibitors Using HQSAR, Topomer CoMFA and CoMSIA

In order to understand the chemical-biological interactions governing their activities toward neuraminidase (NA), QSAR models of 28 thiazolidine-4-carboxylic acid derivatives with inhibitory influenza A virus were developed. The obtained HQSAR (hologram quantitative structure activity relationship), Topomer CoMFA and CoMSIA (comparative molecular similarity indices analysis) models were robust and had good exterior predictive capabilities. Moreover, QSAR modeling results elucidated that hydrogen bonds highly contributed to the inhibitory activity, then electrostatic and hydrophobic factors. Squared multiple correlation coefficients (R2) of HQSAR, Topomer CoMFA and CoMSIA models were 0.994, 0.978 and 0.996, respectively. Squared cross-validated correlation coefficients (Q2) of HQSAR, Topomer CoMFA and CoMSIA models were in turn 0.951, 919 and 0.820. Furthermore, squared multiple correlation coefficients for the test set (R2test) of HQSAR, CoMFA and CoMSIA models were 0.879, 0.912 and 0.953, respectively. Squared cross-validated correlation coefficients for the test set (Q2ext) of HQSAR, Topomer CoMFA and CoMSIA models were 0.867, 0.884 and 0.899, correspondingly.     

Computational Analysis of CRTh2 receptor antagonist: A Ligand-based CoMFA and CoMSIA approach

CRTh2 receptor is an important mediator of inflammatory effects and has attracted much attention as a therapeutic target for the treatment of conditions such as asthma, COPD, allergic rhinitis and atopic dermatitis. In pursuit of better CRTh2 receptor antagonist agents, 3D-QSAR studies were performed on a series of 2-(2-(benzylthio)-1H-benzo[d]imidazol-1-yl) acetic acids. There is no crystal structure information available on this protein; hence in this work, ligand-based comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed by atom by atom matching alignment using systematic search and simulated annealing methods. The 3D-QSAR models were generated with 10 different combinations of test and training set molecules, since the robustness and predictive ability of the model is very important. We have generated 20 models for CoMFA and 100 models for CoMSIA based on two different alignments. Each model was validated with statistical cut off values such as q² > 0.4, r² > 0.5 and r²_pred > 0.5. Based on better q² and r²_pred values, the best predictions were obtained for the CoMFA (model 5 q² = 0.488, r²_pred = 0.732), and CoMSIA (model 45 q² = 0.525, r²_pred = 0.883) from systematic search conformation alignment. The high correlation between the cross-validated/predicted and experimental activities of a test set revealed that the CoMFA and CoMSIA models were robust. Statistical parameters from the generated QSAR models indicated the data is well fitted and have high predictive ability. The generated models suggest that steric, electrostatic, hydrophobic, H-bond donor and acceptor parameters are important for activity. Our study serves as a guide for further experimental investigations on the synthesis of new CRTh2 antagonist.     

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.     

Combined 2D and 3D-QSAR,molecular modelling and docking studies of pyrazolodiazepinones as novel phosphodiesterase 2 inhibitors

Selective inhibition of phosphodiesterase 2 (PDE2) in cells where it is located elevates cyclic guanosine monophosphate (cGMP) and acts as novel analgesic with antinociceptive activity. Three-dimensional quantitative structure–activity relationship (QSAR) studies for pyrazolodiazepinone inhibitors exhibiting PDE2 inhibition were performed using comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA) and Topomer CoMFA, and two-dimensional QSAR study was performed using a Hologram QSAR (HQSAR) method. QSAR models were generated using training set of 23 compounds and were validated using test set of nine compounds. The optimum partial least squares (PLS) for CoMFA-Focusing, CoMSIA-SDH, Topomer CoMFA and HQSAR models exhibited good ‘leave-one-out’ cross validated correlation coefficient (q²) of 0.790, 0.769, 0.840 and 0.787, coefficient of determination (r²) of 0.999, 0.964, 0.979 and 0.980, and high predictive power (r²_pred) of 0.796, 0.833, 0.820 and 0.803 respectively. Docking studies revealed that those inhibitors able to bind to amino acid Gln859 by cGMP binding orientation called ‘glutamine-switch’, and also bind to the hydrophobic clamp of PDE2 isoform, could possess high selectivity for PDE2. From the results of all the studies, structure–activity relationships and structural requirements for binding to active site of PDE2 were established which provide useful guidance for the design and future synthesis of potent PDE2 inhibitors.     

Studies of febuxostat analogues as xanthine oxidase inhibitors through 3D-QSAR,Topomer CoMFA and molecular modeling

Xanthine oxidase, a complex molybdoflavoprotein, catalyzes the hydroxylation of xanthine to uric acid, which has emerged as an important target for gout and hyperuricemia. In this work, a combination of molecular modeling methods was performed on a series of febuxostat analogues as xanthine oxidase inhibitors to establish molecular models for new drug design, including three-dimensional quantitative structure–activity relationship, topomer comparative molecular field analysis (CoMFA), molecular docking and molecular dynamic simulations. The optimal CoMFA model yielded a leave-one-out correlation coefficient (q²) of 0.841 and a non-validated correlation coefficient (r²) of 0.985. The respective q² and r² of the best comparative molecular similarity indices analysis (CoMSIA) model were 0.794 and 0.972, respectively. The Topomer CoMFA model provided a q² of 0.915 and an r² of 0.977. 3D contour maps generated from CoMFA and CoMSIA have identified several key features responsible for the inhibition activity. Molecular modeling was taken to further elucidate the proposed binding conformations of the inhibitors to the protein. The obtained results can be served as a useful guideline for designing novel febuxostat derivatives with improved activity against xanthine oxidase.

     

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.     

QSAR models of cytochrome P450 enzyme 1A2 inhibitors using CoMFA,CoMSIA and HQSAR

Quantitative structure–activity relationship (QSAR) studies were conducted on an in-house database of cytochrome P450 enzyme 1A2 inhibitors using the comparative molecular field analysis (CoMFA), comparative molecular similarity analysis (CoMSIA) and hologram QSAR (HQSAR) approaches. The database consisted of 36 active molecules featuring varied core structures. The model based on the naphthalene substructure alignment incorporating 19 molecules yielded the best model with a CoMFA cross validation value q² of 0.667 and a Pearson correlation coefficient r² of 0.976; a CoMSIA q² value of 0.616 and r² value of 0.985; and a HQSAR q² value of 0.652 and r² value of 0.917. A second model incorporating 34 molecules aligned using the benzene substructure yielded an acceptable CoMFA model with q² value of 0.5 and r² value of 0.991. Depending on the core structure of the molecule under consideration, new CYP1A2 inhibitors will be designed based on the results from these models.     

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.     

Molecular description of pyrimidine-based inhibitors with activity against FAK combining 3D-QSAR analysis,molecular docking and molecular dynamics

Focal adhesion kinase (FAK) is a promising target for developing more effective anticancer drugs. To better understand the structure-activity relationships and mechanism of actions of FAK inhibitors, a molecular modeling study using 3D-QSAR, molecular docking, molecular dynamics simulations, and binding free energy analysis were conducted. Two types of satisfactory 3D-QSAR models were generated, comprising the CoMFA model (R²_cv = 0.528, R²_pred = 0.7557) and CoMSIA model (R²_cv = 0.757, R²_pred = 0.8362), for predicting the inhibitory activities of novel inhibitors. The derived contour maps indicate structural characteristics for substituents on the template. Molecular docking, molecular dynamic simulations and binding free energy calculations further reveal that the binding of inhibitors to FAK is mainly contributed from hydrophobic, electrostatic and hydrogen bonding interactions. In addition, some key residues (Arg14, Glu88, Cys90, Arg138, Asn139, Leu141, and Leu155) responsible for ligand-receptor binding are highlighted. All structural information obtained from 3D-QSAR models and molecular dynamics is consist with the available experimental activities. All the results will facilitate the optimization of this series of FAK inhibitors with higher inhibitory activities.     

3D-QSAR and molecular docking study of LRRK2 kinase inhibitors by CoMFA and CoMSIA methods

Three-dimensional quantitative structure–activity relationship (3D-QSAR) modelling was conducted on a series of leucine-rich repeat kinase 2 (LRRK2) antagonists using CoMFA and CoMSIA methods. The data set, which consisted of 37 molecules, was divided into training and test subsets by using a hierarchical clustering method. Both CoMFA and CoMSIA models were derived using a training set on the basis of the common substructure-based alignment. The optimum PLS model built by CoMFA and CoMSIA provided satisfactory statistical results (q² = 0.589 and r² = 0.927 and q² = 0.473 and r² = 0.802, respectively). The external predictive ability of the models was evaluated by using seven compounds. Moreover, an external evaluation set with known experimental data was used to evaluate the external predictive ability of the porposed models. The statistical parameters indicated that CoMFA (after region focusing) has high predictive ability in comparison with standard CoMFA and CoMSIA models. Molecular docking was also performed on the most active compound to investigate the existence of interactions between the most active inhibitor and the LRRK2 receptor. Based on the obtained results and CoMFA contour maps, some features were introduced to provide useful insights for designing novel and potent LRRK2 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.     

Drug discovery studies on quinoline-based derivatives as potential antimalarial agents

Molecular modelling studies were performed to identify the essential structural requirements of quinoline-based derivatives for improving their antimalarial activity. The developed CoMFA, CoMSIA and HQSAR models for a training set comprising 37 derivatives showed good statistical significance in terms of internal cross validation (q²) 0.70, 0.69 and 0.80 and non-cross validation (r²) 0.80, 0.79 and 0.80. Also, the predicted r² values (r²_pred) of 0.63, 0.61 and 0.72 for a test set consisting of 12 compounds suggested significant predicting ability of the models. Structural features were correlated in terms of steric, electrostatic, hydrophobic, hydrogen bond donor and hydrogen bond acceptor interactions. Furthermore, the bioactive conformation was explored and explained by docking compounds #28, 32 and 40 into the active binding site of lactate dehydrogenase of Plasmodium falciparum. The QSAR models, contour map and docking binding affinity obtained could be successfully utilized as a guiding tool for the design and discovery of novel quinoline-based derivatives with potent antimalarial activity.     

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.     

Strategies to design pyrazolyl urea derivatives for p38 kinase inhibition: a molecular modeling study

The p38 protein kinase is a serine–threonine mitogen activated protein kinase, which plays an important role in inflammation and arthritis. A combined study of 3D-QSAR and molecular docking has been undertaken to explore the structural insights of pyrazolyl urea p38 kinase inhibitors. The 3D-QSAR studies involved comparative molecular field analysis (CoMFA) and comparative molecular similarity indices (CoMSIA). The best CoMFA model was derived from the atom fit alignment with a cross-validated r ² (q ²) value of 0.516 and conventional r ² of 0.950, while the best CoMSIA model yielded a q ² of 0.455 and r ² of 0.979 (39 molecules in training set, 9 molecules in test set). The CoMFA and CoMSIA contour maps generated from these models provided inklings about the influence of interactive molecular fields in the space on the activity. GOLD, Sybyl (FlexX) and AutoDock docking protocols were exercised to explore the protein–inhibitor interactions. The integration of 3D-QSAR and molecular docking has proffered essential structural features of pyrazolyl urea inhibitors and also strategies to design new potent analogues with enhanced activity.     

Development of docking-based 3D QSAR models for the design of substituted quinoline derivatives as human dihydroorotate dehydrogenase (hDHODH) inhibitors

Abstract

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.