Comparative Molecular Field Analysis Method Research Articles

3D-QSAR Study of the Chalcone Derivatives as Anticancer Agents

For their biological properties and particularly for their anticancer activities, chalcones are widely studied. In this work, we have submitted diverse sets of chalcone derivatives to the 3D-QSAR (3-dimensional quantitative structural-activity relationship) to study their anticancer activities against HTC116 (human colon cancer), relying on the 3-dimensional descriptors: steric and electrostatic descriptors for the CoMFA (comparative molecular field analysis) method and steric, electrostatic, hydrophobic, H-bond donor, and H-bond acceptor descriptors for the CoMSIA method. CoMFA as well as the CoMSIA model have encouraging values of the cross-validation coefficient (Q2) of 0.608 and 0.806 and conventional correlation coefficient (R2) of 0.960 and 0.934, respectively. Furthermore, values of R2test have been obtained as 0.75 and 0.90, respectively. Besides, y-randomization test was also performed to validate our 3D-QSAR models. Based on these satisfactory results, ten new compounds have been designed and predicted by in silico ADMET method. This study could expand the understanding of chalcone derivatives as anticancer agents and would be of great help in lead optimization for early drug discovery of highly potent anticancer activity.

Integrated molecular docking, 3D QSAR and molecular dynamics simulation studies on indole derivatives for designing new Pim-1 inhibitors

Pim-1 is one of the isoforms of pim proteins comprising pim-1, pim-2 and pim-3. It was basically recognized as proviral integration moloney murine leukemia virus which is associated with T-cell lymphomogenesis. Pim-1 is known to play a crucial role in cell cycle progression and acts as downstream target for the JAK/STAT signaling pathway. Recently it has emerged as a hopeful therapeutic target for cancer treatment as deregulation or over expression of pim causes hematologic cancers. In present article molecular docking based three dimensional quantitative structure and activity relationship and molecular dynamics simulation studies have been carried out on indole derivatives reported as pim-1 inhibitors. Initially docking was carried out to obtain the receptor specific orientation of the molecules and later to understand the structural requirements of pim-1 inhibitors robust 3 D QSAR models were built using CoMFA (comparative molecular field analysis) and CoMSIA (comparative molecular similarity indices analysis) methods. The reliability of the models was established from conventional (r2) and cross validated (q2) values of 0.982, 0.524 for CoMFA and 0.974, 0.586 for CoMSIA respectively. Further the predictive ability of the model was evaluated using a test set of 17 molecules. The docking studies revealed that interaction with Glu 121 is vital for binding of inhibitors to pim-1. Based on the outcome of the results new molecules with improved activity were designed. Furthermore, MD simulations were also performed to examine the stability of interactions and investigate the pivotal role of Glu 121.

In vitro antitumor activity, ADME-Tox and 3D-QSAR of synthesized and selected natural styryl lactones

Prostate cancer is a common cause of death in men and a novel treating methods should be developed. In order to find a new drug for prostate cancer, a series of novel conformationally constrained analogues of (+)-goniofufurone and 7-epi-(+)-goniofufurone, as well as the newly synthesized styryl lactones containing the cinnamic acid ester groups were evaluated for in vitro cytotoxicity against prostate cancer cell (PC-3). Furthermore, prediction of physicochemical characteristics and drugability as well as in silico ADME-Tox tests of investigated compounds were performed. The 3D-QSAR model was established using the comparative molecular field analysis method. According to obtained results, the tricyclic compounds 9 and 10 had the highest potency with IC50 < 20 μM. Evaluation of structural features through 3D-QSAR model identified steric field feature on the cinnamic acid ester groups at C-7 as a crucial for the cytotoxic activity. This research suggests that most of the analysed compounds have desirable properties for drug candidates and high potential in drug development, which recommend them for further research in treatment of prostate cancer. Furthermore, obtained 3D-QSAR model is able to successfully identify styryl lactones that have significant cytotoxic activity and provide information for screening and design of novel inhibitors against PC-3 cell line that could be used as drugs in treatment of the prostate cancer.

3D-QSAR, molecular docking, and new compound design of pyrimidine derivatives as Src small molecule inhibitors

Non-receptor tyromethod, The sine kinase Src is one of the important molecular targets for potential triple-negative breast cancer (TNBC). In this study, the structure and acitivity relationship of 51 Src small molecule inhibitors with potential pyrimidine derivatives was explored. On the basis of ligand composite, the relationsConducted a phase hip between molecular structure and inhibitory activity of MDA-MB-231/435 TNBC cell lines was studied by using comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis fields (CoMSIA), and Topomer CoMFA (T-COMFA) methods, and thereby two sets of three-dimensional quantitative structure–activity relationship models were established to analyze the molecular structure and anti-TNBC activity. The results indicated that the q2 of CoMFA, CoMSIA, and Topomer CoMFA models was 0.698/0.719, 0.73/0.684, 0.674/0.683, and r2 was 0.994/0.991, 0.991/0.985, 0.946/0.959, respectively. Moreover, the three-dimensional equipotential map also revealed the relationship between structural characteristics and inhibitory activity, and molecular docking was investigated by Surflex-dock. The data proved that this model had good predictive ability and can further guide the design and modification of 3- (phenylethynyl) −1 hydropyrazolyl [3anilo-d] pyrimidine-4-amine derivatives inhibitors. According to the three-dimensional quantitative structure–activity relationship model of two TNBC cell lines of MDA-MB-231/435, 13 and 11 compounds were designed respectively, and the predicted activity values showed effective inhibition of SRC.

Quantitative Structure Activity Relationships Study of Soluble Epoxide Hydrolase Inhibitors Using MLR, ANN, CoMFA and CoMSIA Methods

AbstractSoluble epoxide hydrolase (sEH), a member of the α/β hydrolase fold family, catalyzes the hydrolysis of epoxy eicosatrienoic acids to vicinal diol which are involved in the regulation of blood pressure and inflammation. In this study, 2D and 3D‐QSAR analysis of novel N,N′‐disubstituted urea derivatives as sEH inhibitors were performed by stepwise multiple linear regressions (SW‐MLR), stepwise artificial neural networks (SW‐ANN), Comparative Molecular Field Analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA). It was found that SW‐MLR model with r2 of 0.954 for the training set and r2 of 0.876 for the test set were more favorable than model established by CoMFA method with r2=0.983, r2pred=0.602 and CoMSIA with r2=0.987, r2pred=0.751 in 3D‐QSAR. In addition, obtaining models were validated by cross validation with cut off value of q2&gt; 0.5. These developed models could be a useful guideline to design and predict the activity of novel compounds with enhanced sEH inhibitory activities.

QSAR Studies on Thiazole Derivatives as HCV NS5A Inhibitors via CoMFA and CoMSIA Methods

Background: Hepatitis C Virus (HCV) infection is the major cause of hepatitis after transfusion. And HCV Nonstructural Protein 5A (NS5A) inhibitors have become a new hotspot in the study of HCV inhibitors due to their strong antiviral activity, rapid speed of viral removing and broad antiviral spectrum. Methods: Forty-five NS5A inhibitors were chosen to process three-dimensional quantitative structure- activity relationship (3D-QSAR) by using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models. A training set consisting of 30 compounds was applied to establish the models and a test set consisting of 15 compounds was applied to do the external validation. Results: The CoMFA model predicted a q2 value of 0.607 and an r2 value of 0.934. And the CoMSIA model predicted a q2 value of 0.516 and an r2 value of 0.960 established on the effects of steric, electrostatic, hydrophobic and hydrogen-bond acceptor. 0.713 and 0.939 were the predictive correlation co-efficients (r2pred) of CoMFA and CoMSIA models, respectively. Conclusion: These conclusions provide a theoretical basis for drug design and screening of HCV NS5A complex inhibitors.

Computation of Structure Activity and Design of Chalcone Derivatives

Based on the antitumor activity of chalcone derivatives and their structure, the structure-activity relationship of chalcone derivatives was analyzed by CoMFA (comparative molecular field analysis) method and then the 3D-QSAR (the three dimensional quantitative structures activity relationship) model was established. The analytical results showed that the model had good stability and prediction ability with cross-validated Q2 and non-cross-validated R2 values of 0.527 and 0.995, respectively. The contour map of the model explained the relationship between the structure of chalcone derivatives and antitumor activity, and could be analyzed to design antitumor chalcone derivatives. We designed some structure of chalcone derivatives and calculated their antitumor activity. In this paper, 24 chalcone derivatives were studied by CoMFA, 3D-QSAR, molecular design, which provided theoretical for designing good activities chalcone derivatives.

Computational insight into crucial binding features for metabolic specificity of cytochrome P450 17A1

Abstract In present study we aimed to explore the possible structural feature of Cytochrome P450 (CYP) 17A1 that contributes to the metabolic specificity. The predicable 3D-QSAR (Quantitative Structure-Activity Relationships) models were first developed with CoMFA (Comparative Molecular Field Analysis) and CoMSIA (Comparative Molecular Similarity Indices Analysis) methods based on a training set of 76 non-steroid inhibitors, then verified by a test set of 20 inhibitors. Our data demonstrates a cross-validation correlation coefficient q2s of 0.534 and 0.545, as well as a non-cross-validation correlation coefficient r2s of 0.904 and 0.889, respectively. The contours were generated to indicate the specific inhibitor feature. Further, molecular docking was used to probe the interacting feature between CYP17A1 and its non-steroid inhibitors or its natural substrates. We found the crucial binding features for the non-steroid inhibitor selection can be described as the suitable molecular length and the ability to fulfill the active pocket of CYP17A1, the hydrophobic parent body and the H-bond formed with special residues. Whereas, the distances between reaction site and the oxidative or the per-oxidative center played an important role in the substrate metabolism of 17α-hydroxylase and 17,20-lyase. This study helps to design and screen potential candidates for therapy of prostate cancer and other androgen-dependent diseases.

3D-QSAR Studies of Isatin Derivatives with Anti-Cancer In Vitro: Advanced CoMFA, CoMSIA and Docking Methods

Three-dimensional quantitative structure–activity relationship (3D-QSAR) and docking methods were performed to study 47 tubulin inhibitors, isatin derivatives with anticancer activity against human monocyte-like histiocytic lymphoma human U937 cells. The established 3D-QSAR model from Comparative Molecular Field Analysis (CoMFA), Comparative Molecular Similarity Index Analysis (CoMSIA) approaches show a significant statistical quality and a satisfying predictive ability, with high correlation coefficient value (R2=0.936, R2=0.970) and cross-validation coefficient value (Q2=0.821, Q2=0.884) of CoMFA and CoMSIA, respectively. The predictive ability of the CoMFA (R2 test=0.607) and CoMSIA (R2 test=0.650) model was confirmed by a test set. The CoMFA, CoMSIA contour maps and docking analyses indicate that the substituent R1 should be oxygen which better than azotes to forms a more potent inhibitor against tubulin enzyme and R2, R3, R4 and R5 substituents should be higher electronegative but R6 should be a bulky aromatic group like a methylnaphthol monosubstituted or disubstitued by OCH3. The interaction information between target and ligand was presented such the useful theoretical references for analyzing to understand the action mechanism, designing new more potent inhibitors and optimizing their activities prior to synthesis.

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: 0.002–2.75μM) 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.

Molecular docking, MM/GBSA and 3D-QSAR studies on EGFR inhibitors

Epidermal growth factor receptor (EGFR) is the first growth factor receptor proposed as a target for cancer therapy. Molecular modeling protocols like molecular docking, molecular mechanics/generalized born surface area (MM/GBSA) calculations and three dimensional-quantitative structure activity relationship (3D-QSAR) studies were performed on 45 molecules to understand the structural requirements for EGFR tyrosine kinase inhibitors. Conformation for all the molecules obtained from molecular docking were used as is for 3D-QSAR analysis. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models were obtained by performing partial least square analysis on 35 training molecules and these models were validated using 10 test moleucles. The models showed good statistical results in terms of r 2, q\(^{2}_{\text {loo}}\) and r\(^{2}_{\text {pred}}\) values. Information rendered from 3D-QSAR model and sitemap analysis was used to optimize lead molecule to design prospective inhibitors. Improvement in EGFR binding affinity can be achieved by substitutional modification on phenyl ring attached to alkynyl group with bulkier hydrogen bond donor and acceptor substituents that can increase favourable interaction with the receptor. Epidermal growth factor receptor (EGFR) is the first growth factor receptor proposed as a target for cancer therapy. An integrated computational protocol using molecular docking and 3DQSAR were performed using the CoMFA and CoMSIA methods. Derived models showed good statistical reliability that is evident from their r2, q2 loo values. Information obtained from 3DQSAR model was applied for optimization of lead molecule and to design novel potential inhibitors.

3D-QSAR Studies on the Biological Activity of Imidazolidinylpiperidinylbenzoic Acids as Chemokine Receptor Antagonists.

Human immunodeficiency virus type 1 (HIV-1) infection ultimately leading to acquired immunodeficiency syndrome (AIDS), remains a significant problem. CCR5 is a member of the chemokine receptor family that is utilized in the early stage of the replication cycle by the most commonly transmitted M-tropic strains of HIV-1. In this study, we developed 3D-QSAR models using CoMFA and CoMSIA methods on a series of 71 imidazolidinylpiperidinylbenzoic acid CCR5 antagonists, in order to better understand the substituent requirements and get more potent antagonists of CCR5. The research of 3D-QSAR modeling of imidazolidinylpiperidinylbenzoic acids as chemokine receptor 5 (CCR5) antagonists was conducted using comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA). For this study, a dataset containing 71 imidazolidinyl-piperidinyl-benzoic acids was divided into a training set of 22 compounds and a test set of 49 compounds. The results obtained from the CoMFA/CoMSIA model exhibited a statistical significance r(2) of 0.996 (0.984) with an estimated standard error of 0.109 (0.209). Both CoMFA and CoMSIA models provided valuable insight into the structural requirements for improving the activity of then CCR5 antagonists.

Ligand Efficiency Outperforms pIC50 on Both 2D MLR and 3D CoMFA Models: A Case Study on AR Antagonists.

The concept of ligand efficiency is defined as biological activity in each molecular size and is widely accepted throughout the drug design community. Among different LE indices, surface efficiency index (SEI) was reported to be the best one in support vector machine modeling, much better than the generally and traditionally used end-point pIC50. In this study, 2D multiple linear regression and 3D comparative molecular field analysis methods are employed to investigate the structure-activity relationships of a series of androgen receptor antagonists, using pIC50 and SEI as dependent variables to verify the influence of using different kinds of end-points. The obtained results suggest that SEI outperforms pIC50 on both MLR and CoMFA models with higher stability and predictive ability. After analyzing the characteristics of the two dependent variables SEI and pIC50, we deduce that the superiority of SEI maybe lie in that SEI could reflect the relationship between molecular structures and corresponding bioactivities, in nature, better than pIC50. This study indicates that SEI could be a more rational parameter to be optimized in the drug discovery process than pIC50.

3D-QSAR and docking studies on adenosine A2A receptor antagonists by the CoMFA method

Parkinson’s disease affects millions of people around the world. Recently, adenosine A2A receptor antagonists have been identified as a drug target for the treatment of Parkinson’s disease. Consequently, there is an immediate need to develop new classes of A2A receptor antagonists. In the present analysis, three-dimensional quantitative structure–activity relationship (3D-QSAR) studies were performed on a series of pyrimidines, using comparative molecular field analysis (CoMFA). The best prediction was obtained with a CoMFA standard model (q2 = 0.475, r2 = 0.977) and a CoMFA region focusing model (q2 = 0.637, r2 = 0.976) combined with steric and electrostatic fields. The structural insights derived from the contour maps helped to better interpret the structure–activity relationships. Also, to understand the structure–activity correlation of A2A receptor antagonists, we have carried out molecular docking analysis. Based on the results obtained from the present 3D-QSAR and docking studies, we have identified some key features for increasing the activity of compounds, which have been used to design new A2A receptor antagonists. The newly designed molecules showed high activity with the obtained models.

Pharmacophore and docking-based 3D-QSAR studies on HIV-1 integrase inhibitors

Integrase(IN) plays an essential role in the process of HIV-1 replication. IN inhibitors of diketo acid derivatives( DKAs) were analysed by the Comparative Molecular Field Analysis(CoMFA) and Comparative Molecular Similarity Induces Analysis(CoMSIA) methods. A set of 42 compounds were randomly selected as the training set(35) and test set(7). Firstly, a good pharmacophore(goodness of hit=0.787) was obtained and used to align ligands. Then, predictive models were constructed with the CoMFA and CoMSIA methods based on the pharmacophore alignment. As a result, the CoMSIA method yielded the best model with an r 2 of 0.955 and a q 2 of 0.665, which can predict the activities of the tested DKAs very well(r 2=0.559). Finally, DKAs were docked into IN, and the predicit modes were superimposed on the contour maps obtained from the best CoMSIA model. The superimposed maps gave a visualized and meaningful insight into the inhibitory behaviors, providing significantly useful information for the rational drug design of anti-IN agents.

3D-QSAR Studies on Chemical Features of 3-(benzo[d]oxazol-2-yl)pyridine-2-amines in the External Region of c-Met Active Site

The three dimensional-quantitative structure activity relationship (3D-QSAR) studies on chemical features of pyridine-2-amines in the external region of c-Met active site (ER chemical features of pyridine-2-amines) were conducted by docking, comparative molecular field analysis (CoMFA), and topomer CoMFA methods. The CoMFA model obtained the partial least-squares (PLS) statistical results, cross-validated correlation coefficient (q 2 ) of 0.703, non cross-validated correlation coefficient (r 2 ) of 0.947 with standard error of estimate (SEE) of 0.23 and the topomer CoMFA obtained q 2 of 0.803, r 2 of 0.940, and SEE of 0.24. Further, the test set was applied to validate predictive abilities of models, where the predictive r 2 (r 2 pred) for CoMFA and topomer CoMFA models were 0.746 and 0.608, respectively. Each contribution of ER chemical features of pyridine-2amines to the inhibitory potency showed correlation coefficients, r 2 of 0.670 and 0.913 for two core parts, 3(benzo[d]oxazol-2-yl)pyridine-2-amine and 3-(1-(2,6-dichloro-3-fluorophenyl)ethoxy) pyridine-2-amine, respectively, with corresponding experimental pIC50.

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 &amp; Sons, Ltd.

Combined 3D-QSAR modeling and molecular docking study on azacycles CCR5 antagonists

The beta chemokine receptor 5 (CCR5) is an attractive target for pharmaceutical industry in the HIV-1, inflammation and cancer therapeutic areas. In this study, we have developed quantitative structure activity relationship (QSAR) models for a series of 41 azacycles CCR5 antagonists using comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), and Topomer CoMFA methods. The cross-validated coefficient q2 values of 3D-QASR (CoMFA, CoMSIA, and Topomer CoMFA) methods were 0.630, 0.758, and 0.852, respectively, the non-cross-validated R2 values were 0.979, 0.978, and 0.990, respectively. Docking studies were also employed to determine the most probable binding mode. 3D contour maps and docking results suggested that bulky groups and electron-withdrawing groups on the core part would decrease antiviral activity. Furthermore, docking results indicated that H-bonds and π bonds were favorable for antiviral activities. Finally, a set of novel derivatives with predicted activities were designed.

3D-QSAR studies of quinoline ring derivatives as HIV-1 integrase inhibitors

In the process of HIV-1 virus replication, integrase plays a quite important role. Integrase inhibitors of quinoline ring derivatives were analysed by the Comparative Molecular Field Analysis (CoMFA), Comparative Molecular Similarity Induces Analysis (CoMSIA) and Topomer CoMFA methods. Firstly, 77 compounds were selected to form the training and test sets. Secondly, predictive models were constructed with the CoMFA, CoMSIA and Topomer CoMFA methods. The CoMFA model yielded the best model with q 2 of 0.76 and of 0.99, the CoMSIA model has q 2 = 0.70 and of 0.99, while the Topomer CoMFA model has q 2 of 0.66 and of 0.97. These results provide a helpful contribution to the design of novel highly active HIV-1 integrase inhibitors.

3D-QSAR studies of thiosemicarbazone and thiocarbamoylpyrazoline antiamebics

Amebiasis is a major threat to the public health that causes tens of thousands of deaths per year. In order to better rationalize the discovery of more potent anti-amebic agents, three-dimensional quantitative structure–activity relationship studies were conducted on a large number (six sets) of anti-amebic thiosemicarbazones and thiocarbamoylpyrazolines using comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis and the topomer CoMFA methods, in addition to the 3D-dependent Eigen value analysis method. The generated models were subject to internal, external, and cross validation statistical tests to judge their respective qualities. Herein, the best alignment approach was described and the models’ profiles were analyzed to give a structural insight of more potent antiamebics.