3D-QSAR-based pharmacophore modelling of quinazoline derivatives for the identification of acetylcholinesterase inhibitors through virtual screening, molecular docking, molecular dynamics and DFT studies

Abstract

Alzheimer’s disease (AD) is a progressive neurological disorder responsible for the cognitive dysfunction and cognitive impairment in the patients. Acetylcholinesterase inhibitors (AChEIs) are used to treat AD however, these only provided symptomatic relief and more efficient drug molecules are desired for the effective treatment of the disease. In this article, ligand-based drug-designing strategy was used to develop and validate a field-based 3D-QSAR pharmacophore model on quinazoline-based AChEIs reported in the literature. The validated pharmacophore model (AAAHR_1) was used as a prefilter to screen an ASINEX database via virtual screening workflow (VSW). The hits generated were subjected to MM-GBSA to identify potential AChEIs and top three scoring molecules (BAS 05264565, LEG 12727144 and SYN 22339886) were evaluated for thermodynamic stability at the target site using molecular dynamic simulations. Additionally, DFT study was performed to predict the reactivity of lead molecules towards acetylcholinesterase (AChE). Thus, by utilising various computational tools, three molecules were identified as potent AChEIs that can be developed as potential drug candidates for the treatment of AD. Communicated by Ramaswamy H. Sarma