Review of the article „Molecular Modeling Studies of Anti-Alzheimer Agents by QSAR, Molecular Docking and Molecular Dynamic Simulations Techniques“, verified by Publons, Web of Science

Abstract

Abstract: Background: Acetylcholinesterase (AChE) is one of the most important targets in the treatment of Alzheimer's disease (AD). It was claimed that novel AChE inhibitors were optimized as potential drug candidates, designed for regional or systematic release, and created as significant inhibitors. Objective: In this work, molecular modeling studies including CoMFA, CoMFA-RF, CoMSIA , HQSAR and molecular docking and molecular dynamic simulations were used to provide a theoretical basis for finding highly potent anti-Alzheimer drugs. Methods: QSAR was used to generate models and predict the anti-Alzheimer activity using the Sybyl program (x1.2 version). pyrimidinylthiourea derivatives as AChE inhibitors were selected as our data set, which was split randomly into training and test sets. Docking and molecular dynamic simulation were carried out using the MOE software and the Sybyl program, respectively. Partial least square was used as QSAR model-generation method. The statistical qualities of generated models were justified by internal and external validation i.e., cross-validated correlation coefficient (q2), non-cross-validated correlation coefficient () and predicted correlation coefficient (), respectively. Results: The CoMFA (q2, 0.775;, 0.901; 0.773), CoMFA-RF (q2, 0.629;, 0.901; 0.824), CoMSIA (q2, 0.754;, 0.919; 0.874) and HQSAR models (q2, 0.622;, 0.949; 0.854) for training and test set yielded significant statistical results. Conclusion: These QSAR models were excellent, robust and had good predictive capability. Contour maps of the QSAR models were generated and validated by molecular dynamic simulation-assisted molecular docking study. The final QSAR models could be useful for design and development of novel potent AChE inhibitors in Alzheimer's treatment.