α-Amylase play important role in hydrolyses of α-bonds of large α-linked polysaccharides; thus, it is a potential drug target in diabetes mellites (DM) and its inhibition is one of the therapeutic strategies in DM. With the aim to discover novel and safer therapeutic molecules to combat diabetes, a huge dataset of ∼0.69 billion compounds from ZINC20 database were screened against α-amylase using multi-fold structure-based virtual screening protocol. Based on receptor-based pharmacophore model, docking results, pharmacokinetic profile, molecular interactions with α-amylase, several compounds were retrieved as lead candidates to be further scrutinized in the in vitro assay and in vivo animal testing. Among the selected hits, CP26 exhibited the highest binding free energy in MMGB-SA analysis, followed by CP7 and CP9, which is higher than the binding free energy of acarbose. While CP20 and CP21 showed comparative binding free energy to acarbose. All the selected ligands showed acceptable binding energy range, therefore, several molecules with enhanced efficacy can be designed by derivatizing these molecules. The in-silico results indicates that the selected molecules could serve as potential selective α-amylase inhibitors and can be used for the treatment of diabetes.Communicated by Ramaswamy H. Sarma.
Keywords: descriptor analysis; diabetes mellites; molecular dynamics simulation; virtual screening; α-Amylase.