3D-QSAR Models In Combination Research Articles

Combined 3D-QSAR Modeling and Molecular Docking Study on metronidazole-triazole-styryl hybrids as antiamoebic activity

A series of twenty-two metronidazole-triazole-styryl hybrids as antiamoebic agents were studied based on the combination of 3D-QSAR and surflex-docking. The CoMFA and CoMSIA models were carried out using eighteen compounds in the training set and four compounds in the test set gives Q 2 values of 0.684 and 0.664 respectively, and R 2 values of 0.882 and 0.894 respectively. The adapted alignment method with the suitable parameters resulted in reliable models. Based on contour maps produced by the CoMFA and CoMSIA, we suggested new compounds with high predicted activities, Surflex-docking revealed the important interactions between the ligand and receptor. Therefore, it confirmed the stability of predicted molecules in the receptor with PDB: 4CCQ.

Combined 3D-QSAR modeling and molecular docking study on spiro-derivatives as inhibitors of acetyl-CoA carboxylase

Acetyl-CoA carboxylases (ACC) have long been recognized as an attractive target for metabolic syndrome because of its indispensable role in the regulation of fatty acid metabolism in most living organisms. Based on the lead compound CP-640186, a series of spiro-compounds of benzothiazole derivatives have been identified as ACC inhibitors, which displayed high ACC inhibitor activities and good metabolic stability. Nevertheless, no quantitative structure-activity relationship study has been developed for this kind of ACC inhibitors. In order to evaluate the correlation between changes on structures and variation on biological activity, three-dimensional quantitative structure-activity relationship analysis was employed. Satisfactory results were obtained after performing a leave-one-out cross-validation study with q 2 and r 2 values of 0.625 and 0.927 by the comparative molecular field analysis model, 0.779 and 0.937 by the comparative molecular similarity indices analysis model, respectively. The predictive ability of the models was validated using external validation test set. The contour map analysis of comparative molecular field analysis and comparative molecular similarity indices analysis models indicated that electropositive and bulky groups substituted at the R 1 position of carbamido group, and hydrophilic groups substituted at the phenyl ring were favorable for the inhibitory activity. Furthermore, FlexX docking was employed to investigate the binding mode between human ACC1 and its inhibitors and to validate the three-dimensional quantitative structure-activity relationship models. As a result, based on the results of three-dimensional quantitative structure-activity relationship and FlexX docking, a series of ACC inhibitors with higher predictive activities have been designed. This work might provide valuable information in further optimization of human ACC inhibitors.

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

Focal adhesion kinase (FAK) plays a primary role in regulating the activity of many signaling molecules. Increased FAK expression has been implicated in a series of cellular processes, including cell migration and survival. Inhibiting the activity of FAK for cancer therapy is currently under investigation. Hence, FAK and its inhibitors have been the subject of intensive research. To understand the structural factors affecting inhibitory potency, molecular docking and 3D-QSAR modeling were studied in this project. CoMFA and CoMSIA methods were used for deriving 3D-QSAR models, which were trained with 78 compounds and then were evaluated for predictive ability with additional 19 compounds. Two substructure-based 3D-QSAR models, including CoMFA model (r2 = 0.930; q2 = 0.629) and CoMSIA model (r2 = 0.952; q2 = 0.586), had a good quality to predict the biological activities of new compounds. Meanwhile, using the flexible docking strategy, two docking-based 3D-QSAR models (CoMFA with r2 = 0.914; q2 = 0.594; CoMSIA with r2 = 0.914; q2 = 0.524) were also constructed. The structure–activity relationship has been illustrated clearly by the contour maps gained from the 3D-QSAR models in combination with the docked binding structures. All the results indicated that it might be useful in the rational design of novel FAK inhibitors.

Study on the activity of non-purine xanthine oxidase inhibitor by 3D-QSAR modeling and molecular docking

Non-purine derivatives have been shown to be promising novel drug candidates as xanthine oxidase inhibitors. Based on three-dimensional quantitative structure–activity relationship (3D-QSAR) methods including comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA), two 3D-QSAR models for a series of non-purine xanthine oxidase (XO) inhibitors were established, and their reliability was supported by statistical parameters. Combined 3D-QSAR modeling and the results of molecular docking between non-purine xanthine oxidase inhibitors and XO, the main factors that influenced activity of inhibitors were investigated, and the obtained results could explain known experimental facts. Furthermore, several new potential inhibitors with higher activity predicted were designed, which based on our analyses, and were supported by the simulation of molecular docking. This study provided some useful information for the development of non-purine xanthine oxidase inhibitors with novel structures.

Combined 3D-QSAR modeling and molecular docking study on multi-acting quinazoline derivatives as HER2 kinase inhibitors.

A series of new quinazoline derivatives has been recently reported as potent multi-acting histone deacetylase (HDAC), epidermal growth factor receptor (EGFR), and human epidermal growth factor receptor 2 (HER2) inhibitors. HER2 is one of the major targets for the treatment of breast cancer and other carcinomas. Three-dimensional structure-activity relationship (3D-QSAR) is a well-known technique, which is used to drug design and development. This technique is used for quantitatively predicting the interaction between a molecule and the active site of a specific target. For each 3D-QSAR study, a three-dimensional model is created from a large curve fit to find a fitting between computational descriptors and biological activity. This model could be used as a predictive tool in drug design. The best model has the highest correlation between theoretical and experimental data. Self-Organizing Molecular Field Analysis (SOMFA), a grid-based and alignment-dependent 3D-QSAR method, is employed to study the correlation between the molecular properties and HER2 inhibitory potency of the quinazoline derivatives. Before presentation of inhibitor structures to SOMFA study, conformation of inhibitors was determined by AutoDock4, HyperChem and AutoDock Vina, separately. Overall, six independent models were produced and evaluated by the statistical partial least square (PLS) analysis. Among the several generated 3D-QSARs, the best model was selected on the basis of its statistical significance and predictive potential. The model derived from the superposition of docked conformation with AutoDock Vina with reasonable cross-validated q(2) (0.767), non cross-validated r(2) (0.815) and F-test (97.22) values showed a desirable predictive capability. Analysis of SOMFA model could provide some useful information in the design of novel HER2 kinase inhibitors with better spectrum of activity.

3D-QSAR study of Chk1 kinase inhibitors based on docking

Checkpoint kinase 1 (Chk1), a kind of a serine/threonine protein kinase, plays a significant role in DNA damage-induced checkpoints. Chk1 inhibitors have been demonstrated to abrogate the S and G2 checkpoints and disrupt the DNA repair process, which results in immature mitotic progression, mitotic catastrophe, and cell death. Normal cells remain at the G1 phase via p53 to repair their DNA damages, and are less influenced by the abrogation of S and G2 checkpoint. Therefore, selective inhibitors of Chk1 may be of great therapeutic value in cancer treatment. In this paper, in order to understand the structure-activity relationship of macro-cyclic urea Chk1 inhibitors, a study combined molecular docking and 3D-QSAR modeling was carried out, which resulted in two substructure-based 3D-QSAR models, including the CoMFA model (r(2), 0.873; q(2), 0.572) and CoMSIA model (r(2), 0.897; q(2), 0.599). The detailed microscopic structures of Chk1 binding with inhibitors were performed by molecular docking. Two docking based 3D-QSAR models were developed (CoMFA with r(2), 0.887; q(2), 0.501; CoMSIA with r(2), 0.872; q(2), 0.520). The contour maps obtained from the 3D-QSAR models in combination with the docked binding structures would be helpful to better understand the structure-activity relationship. All the conclusions drawn from both the 3D-QSAR contour maps and molecular docking were in accordance with the experimental activity dates. The results suggested that the developed models and the obtained CHk1 inhibitor binding structures might be reliable to predict the activity of new inhibitors and reasonable for the future drug design.

Combined Pharmacophore Modeling, Docking, and 3D-QSAR Studies of PLK1 Inhibitors

Polo-like kinase 1, an important enzyme with diverse biological actions in cell mitosis, is a promising target for developing novel anticancer drugs. A combined molecular docking, structure-based pharmacophore modeling and three-dimensional quantitative structure-activity relationship (3D-QSAR) study was performed on a set of 4,5-dihydro-1H-pyrazolo[4,3-h]quinazoline derivatives as PLK1 inhibitors. The common substructure, molecular docking and pharmacophore-based alignment were used to develop different 3D-QSAR models. The comparative molecular field analysis (CoMFA) and comparative molecule similarity indices analysis (CoMSIA) models gave statistically significant results. These models showed good q2 and r2 pred values and revealed a good response to test set validation. All of the structural insights obtained from the 3D-QSAR contour maps are consistent with the available crystal structure of PLK1. The contour maps obtained from the 3D-QSAR models in combination with the structure based pharmacophore model help to better interpret the structure-activity relationship. These satisfactory results may aid the design of novel PLK1 inhibitors. This is the first report on 3D-QSAR study of PLK1 inhibitors.

3D-QSAR based pharmacophore modeling and virtual screening for identification of novel pteridine reductase inhibitors

Pteridine reductase is a promising target for development of novel therapeutic agents against Trypanosomatid parasites. A 3D-QSAR pharmacophore hypothesis has been generated for a series of L. major pteridine reductase inhibitors using Catalyst/HypoGen algorithm for identification of the chemical features that are responsible for the inhibitory activity. Four pharmacophore features, namely: two H-bond donors (D), one Hydrophobic aromatic (H) and one Ring aromatic (R) have been identified as key features involved in inhibitor-PTR1 interaction. These features are able to predict the activity of external test set of pteridine reductase inhibitors with a correlation coefficient (r) of 0.80. Based on the analysis of the best hypotheses, some potent Pteridine reductase inhibitors were screened out and predicted with anti-PTR1 activity. It turned out that the newly identified inhibitory molecules are at least 300 fold more potent than the current crop of existing inhibitors. Overall the current SAR study is an effort for elucidating quantitative structure-activity relationship for the PTR1 inhibitors. The results from the combined 3D-QSAR modeling and molecular docking approach have led to the prediction of new potent inhibitory scaffolds.

Microscopic binding of butyrylcholinesterase with quinazolinimine derivatives and the structure–activity correlation

Butyrylcholinesterase (BChE) is not only an important protein for development of anti-cocaine medication but also an established drug target to develop new treatment for Alzheimer’s disease (AD). The molecular basis of interaction of a new series of quinazolinimine derivatives as BChE inhibitors has been studied by molecular docking and molecular dynamics (MD) simulations. The molecular docking and MD simulations revealed that all of these inhibitors bind with BChE in similar binding mode. Based on the similar binding mode, we have carried out three-dimensional quantitative structure–activity relationship (3D-QSAR) studies on these inhibitors using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA), to understand the structure–activity correlation of this series of inhibitors and to develop predictive models that could be used in the design of new inhibitors of BChE. The study has resulted in satisfactory 3D-QSAR models. We have also developed ligand-based 3D-QSAR models. The contour maps obtained from the 3D-QSAR models in combination with the simulated binding structures help to better interpret the structure–activity relationship and is consistent with available experimental activity data. The satisfactory 3D-QSAR models strongly suggest that the determined BChE-inhibitor binding modes are reasonable. The identified binding modes and developed 3D-QSAR models for these BChE inhibitors are expected to be valuable for rational design of new BChE inhibitors that may be valuable in the treatment of Alzheimer’s disease.

Combined 3D-QSAR modeling and molecular docking study on 1,4-dihydroindeno[1,2-c]pyrazoles as VEGFR-2 kinase inhibitors

The vascular endothelial growth factor (VEGF) and its receptor tyrosine kinases VEGFR-2 are attractive targets for the development of novel anticancer agents. To understand the structure–activity correlation of 1,4-dihydroindeno[1,2-c]pyrazole-based VEGFR-2 inhibitors, we have carried out a combined molecular docking and three-dimensional quantitative structure–activity relationship (3D-QSAR) modeling study. The study has resulted in two types of satisfactory substructure-based 3D-QSAR models, including the CoMFA model ( r 2, 0.931; q 2, 0.600) and CoMSIA model ( r 2, 0.928; q 2, 0.569), for predicting the biological activity of new compounds. The detailed microscopic structures of VEGFR-2 binding with inhibitors have been studied by molecular docking. We have also developed docking based 3D-QSAR models (CoMFA with r 2, 0.958; q 2, 0.563; CoMSIA with r 2, 0.965; q 2, 0.567). The contour maps obtained from the 3D-QSAR models in combination with the docked binding structures help to better interpret the structure–activity relationship. All of the structural insights obtained from both the 3D-QSAR contour maps and molecular docking are consistent with the available experimental activity data. The satisfactory results strongly suggest that the developed 3D-QSAR models and the obtained VEGFR-2 inhibitor binding structures are reasonable for the prediction of the activity of new inhibitors and in future drug design.

Combined 3D‐QSAR Modeling and Molecular Docking Study on Quinoline Derivatives as Inhibitors of P‐selectin

P-selectin is a promising target for developing novel atherosclerosis drugs. To understand the structure-activity correlation of quinolines-based P-selectin inhibitors, we have carried out a combined molecular docking and three-dimensional quantitative structure-activity relationship (3D-QSAR) modeling study. The study has resulted in two types of satisfactory 3D-QSAR models, including the CoMFA model (r(2), 0.863; q(2), 0.589) and CoMSIA model (r(2), 0.866; q(2), 0.636), to predict the biological activity of new compounds. The detailed microscopic structures of P-selectin binding with inhibitors have been studied by molecular docking. We have also developed docking based 3D-QSAR models (CoMFA with r(2), 0.934; q(2), 0.591; CoMSIA with r(2), 0.896; q(2), 0.573). The contour maps obtained from the 3D-QSAR models in combination with the docked binding structures help to better interpret the structure-activity relationship. All of the structural insights obtained from both the 3D-QSAR contour maps and molecular docking are consistent with the available experimental activity data. The satisfactory results strongly suggest that the developed 3D-QSAR models and the obtained P-selectin-inhibitor binding structures are reasonable for the prediction of the activity of new inhibitors and in future drug design.

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 q(2) values of 0.663 and 0.639 and r(2) values of 0.989 and 0.963, respectively. The statistically significant models were validated by a test set of eight compounds with predictive r(2) 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 IC(50) values were sorted out in the present study.

Combined 3D-QSAR Modeling and Molecular Docking Study on Indolinone Derivatives as Inhibitors of 3-Phosphoinositide-Dependent Protein Kinase-1

3-Phosphoinositide-dependent protein kinase-1 (PDK1) is a promising target for developing novel anticancer drugs. In order to understand the structure-activity correlation of indolinone-based PDK1 inhibitors, we have carried out a combined molecular docking and three-dimensional quantitative structure-activity relationship (3D-QSAR) modeling study. The study has resulted in two types of satisfactory 3D-QSAR models, including the CoMFA model (r(2)=0.907; q(2)=0.737) and CoMSIA model (r(2)=0.991; q(2)=0.824), for predicting the biological activity of new compounds. The detailed microscopic structures of PDK1 binding with inhibitors have been studied by molecular docking. We have also developed docking-based 3D-QSAR models (CoMFA with q(2)=0.729; CoMSIA with q(2)=0.79). The contour maps obtained from the 3D-QSAR models in combination with the docked binding structures help to better interpret the structure-activity relationship. All of the structural insights obtained from both the 3D-QSAR contour maps and molecular docking are consistent with the available experimental activity data. This is the first report on 3D-QSAR modeling of PDK1 inhibitors. The satisfactory results strongly suggest that the developed 3D-QSAR models and the obtained PDK1-inhibitor binding structures are reasonable for the prediction of the activity of new inhibitors and in future drug design.