Topomer CoMFA, HQSAR Study of Benzimidazole Derivative as NS5B Polymerase Inhibitor

Background: In recent years, the number of people infected with hepatitis C virus (HCV) has continued to grow, this becoming a major threat to global health, and new anti-HCV drugs are urgently needed. HCV NS5B polymerase is an RNA-dependent RNA polymerase (RdRp), which plays an important role in virus replication, and can effectively prevent the replication of HCV sub-genomic RNA in daughter cells. It is considered a very promising HCV therapeutic target for the design of anti-HCV drugs. Methods: In order to explore the relationship between the structure of benzimidazole derivatives and its inhibitory activity on NS5B polymerase, holographic quantitative structure-activity relationship (HQSAR) and Topomer comparative molecular field analysis (CoMFA) were used to establish benzimidazole QSAR model of derivative inhibitors. Results: The results show that for the Topomer CoMFA model, the cross-validation coefficient q2 value is 0.883, and the non-cross-validation coefficient r2 value is 0.975. The model is reasonable, reliable, and has good predictive ability. For the HQSAR model, the cross-validated q2 value is 0.922, and the uncross-validated r2 value is 0.971, indicating that the model data fits well and has high predictive ability. Through the analysis of contour map and color code diagram, 40 new benzimidazole inhibitor molecules were designed, and all of them have higher activity than template molecules, and the new molecules have significant interaction sites with protein 3SKE. Conclusion: The 3D-QSAR model established by Topomer CoMFA and HQSAR has good prediction results and the statistical verification is valid. The newly designed molecules and docking results provide theoretical guidance for the synthesis of new NS5B polymerase inhibitors, and for the identification of key residues that the inhibitor binds to NS5B, which helps to better understand its inhibitory mechanism. These findings are helpful for the development of new anti-HCV drugs.

A QSAR study of peptidyl vinyl sulfone cysteine protease inhibitors using Topomer CoMFA and Molecular Docking

Background: Malaria is one of the most important infectious diseases in the world. The most severe form of malaria in humans is caused by Plasmodium falciparum. Malaria is a worldwide health problem, with 214 million new cases in 2015 and 438,000 deaths, most of which in Africa. Therefore, there is an urgent need for novel, low-toxic, more specific inhibitors to find new antimalarial agents. A promising target for antimalarial drug design is falcipain-2, a cysteine protease from P. falciparum, that has received considerable attention due to its key role in the life cycle of the parasite. Methods: Three-dimensional quantitative structure-activity relationship (3D-QSAR) models of 39 peptidyl vinyl sulfone cysteine protease inhibitors was constructed using Topomer CoMFA. Topomer Search was employed to virtually screen lead-like compounds in the ZINC database. Molecular docking was employed to further explore the binding requirements between the ligands and the receptor protein which included several hydrogen bonds between peptidyl vinyl sulfone cysteine protease inhibitors and active site residues. Results: The non-cross correlation coefficient (r 2 ), the interaction validation coefficient (q2 ) and the external validation (r 2 pred) were 0.902, 0.685 and 0.763, respectively. The results showed that the model not only had good estimation stability but also good prediction capability. 22 new molecules were obtained, whose predicted activity are higher than template molecules. The results showed that the Topomer Search technology can be effectively applied to screen and design new peptidyl vinyl sulfone cysteine protease inhibitors. Molecular docking showed extensive interactions between peptidyl vinyl sulfone cysteine protease inhibitors and residues of LYS24, ASP21, LYS59 and ASP17 in the active site. Conclusion: 39 peptidyl vinyl sulfone cysteine protease inhibitors were used in the 3D-QSAR study. Topomer CoMFA 3DQSAR method was used to build the model, and the model was well predicted and statistically validated. The design of potent new inhibitors of cysteine protease can get useful insights from these results.

QSAR Studies of Sulfonamide Hydroxamates Derivatives as MMP-2 Inhibitors Topomer CoMFA and Molecular Docking

Background: In recent years, cancer has become the main cause of death and it is a serious threat to human health, so the development of new, selective and safe anticancer drugs is still the focus of medical research. Matrix metalloproteinases-2 (MMP-2) has been determined to play an important role in the regulation of tumor angiogenesis, which is closely related to the development of the tumor. Therefore, MMP-2 is considered as a promising target for tumor therapy. In this study, Tomper comparative molecular field analysis (Topomer CoMFA) and molecular docking were used to investigate the important role of sulfonamide hydroxamate derivatives, an inhibitor of MMP-2, in the inhibition of angiogenesis. Methods: Quantitative structure active relationship (QSAR) models of 35 sulfonamide hydroxamate derivatives with inhibitory MMPs were developed. The quantitative structure-activity relationship (QSAR) model was built by using Topomer comparative molecular field analysis (Topomer CoMFA) technique. Results and Conclusion: The results show that the cross-validated q2 value of the Topomer CoMFA model is 0.881 and the non-cross-validated r2 value is 0.967. The results show that the model is reasonable and reliable, and has good prediction ability. Molecular docking studies were used to find the actual conformations of chemicals in active sites of cancer protease, as well as the binding mode pattern to the binding site in MMP-2. The information provided by the 3D-QSAR model and molecular docking may lead to a better understanding of the structural requirements of 35 sulfonamide hydroxamate derivatives and help to design potential anti-cancer protease inhibitor molecules. Conclusion: Thirty-five analogs were used in the 3D-QSAR study. Topomer CoMFA 3D-QSAR method was used to build the model, and the model was well predicted and statistically validated. The results of 3D-QSAR and molecular docking analysis provide theoretical guidance for the synthesis of new MMP-2 inhibitors.

In Silico ADME and QSAR Studies on a Set of Coumarin Derivatives As Acetylcholinesterase Inhibitors Against Alzheimer’s Disease: CoMFA, CoMSIA, Topomer CoMFA, and HQSAR

Background: Alzheimer’s disease (AD) is increasingly being recognized as one of the lethal diseases in older people. Acetylcholinesterase (AChE) has proven to be the most promising target in AD, used for designing drugs against AD. Methods: In silico studies, 2D- or 3D-QSAR like hologram QSAR (HQSAR), Topomer comparative molecular field analysis (Topomer CoMFA), comparative molecular field analysis (CoMFA), and comparative molecular similarity indices analysis (CoMSIA) methods were used to generate QSAR models for acetylcholinesterase inhibitors. Results: Acetylcholinesterase inhibitors used for the present study contain a series of 7- hydroxycoumarin derivatives connected by piperidine, piperazine, tacrine, triazole, or benzyl fragments through alkyl or amide spacer training set compounds were used to generate best model with a HQSAR q2 value of 0.916 and r2 value of 0.940; a Topomer CoMFA q2 value of 0.907 and r2 value of 0.959, CoMFA q2 value of 0.880 and r2 value of 0.960; and a CoMSIA q2 value of 0.865 and r2 value of 0.941. In addition, contour plots obtained from QSAR models suggested the significant regions that influenced the AChE inhibitory activity. Conclusion: In light of these results, this study provides knowledge about the structural requirements for the development of more active acetylcholinesterase inhibitors. In addition, the predicted ADME profile helps us to find CNS active molecules, the obtained prediction compared with well-known AChE inhibitors viz., ensaculin, tacrine, galantamine, rivastigmine, and donepezil. Based on the knowledge obtained from these studies, the hybridization approach is one of the best ways to find lead compounds and these findings can be useful in the treatment of Alzheimer's disease.