Design of vilazodone-donepezil chimeric derivatives as acetylcholinesterase inhibitors by QSAR, molecular docking and molecular dynamics simulations.

Alzheimer's disease (AD) is a disease that affects the cognitive abilities of older adults, and it is one of the biggest global medical challenges of the 21st century. Acetylcholinesterase (AChE) can increase acetylcholine concentrations and improve cognitive function in patients, and is a potential target to develop small molecule inhibitors for the treatment of Alzheimer's disease (AD). In this study, 29 vilazodone-donepezil chimeric derivatives are systematically studied using 3D-QSAR modeling, and a robust and reliable Topomer CoMFA model was obtained with: q2 = 0.720, r2 = 0.991, F = 287.234, N = 6, and SEE = 0.098. Based on the established model and combined with the ZINC20 database, 33 new compounds with ideal inhibitory activity are successfully designed. Molecular docking and ADMET property prediction also show that these newly designed compounds have a good binding ability to the target protein and can meet the medicinal conditions. Subsequently, four new compounds with good comprehensive ability are selected for molecular dynamics simulation, and the simulation results confirm that the newly designed compounds have a certain degree of reliability and stability. This study provides guidance for vilazodone-donepezil chimeric derivatives as a potential AChE inhibitor and has certain theoretical value.

QSAR aided design of potent c-Met inhibitors using molecular docking, molecular dynamics simulation and binding free energy calculation.

Mesenchymal-epithelial transition factor (c-Met) is a tyrosine kinase receptor. Under certain disease conditions, the cellular transformation process may be over-activated, resulting in carcinogenesis. Therefore, molecularly targeted therapy targeting the receptor tyrosine kinase c-Met is achieved by inhibiting c-Met activity and thus effectively suppressing cancer propagation. In this paper, 41 compounds were selected from the reported literature as a dataset to build stable Topomer CoMFA and HQSAR models. The feasibility of the constructed models was evaluated by internal and external validation techniques. Based on the Topomer CoMFA model basis the fragments with higher contribution values were screened and the combination yielded 19 compounds with higher than template molecules. Through molecular docking, the ligand complexes formed hydrogen and hydrophobic bonds with strong stable structures. The ligand-protein complexes with better scoring results were selected for MD simulations, and Y14 exhibited a stable and favourable binding pocket. In addition, ADMET results showed that the ligand-complexes have potential medicinal effects on c-Met inhibition. This study provides a reference for molecularly targeted therapy targeting receptor tyrosine-kinetic c-Met.