Abstract
Alzheimer's disease (AD) is a growing global health concern that affects 10% of the population aged above 65 years. A growing body of evidence indicates that multi-targeted drugs might be useful therapeutic options owing to the heterogeneity of AD pathology. The current study exploited advanced computational biology tools to identify ligands that might display effective binding to two protein targets in the context of AD. The present study used in silico virtual screening of small molecules library to identify effectiveness against two AD targets viz. acetyl cholinesterase (AChE) and glycogen synthase kinase-3β (GSK-3β). PyRX-Python prescription with AutodockVina was used to generate binding energy profiles. Further screening was accomplished using SwissADME and molecular interaction studies. The present study obtained 48 ligands (absolute binding energy >8 kcal/mol), by virtual screening of 100 ligands. Among those, 13 ligands (BRW, 6VK, 6Z5, SMH, X37, 55E, 65 A, IQ6, 6VL, 6VM, F1B, 6Z2 and GVP) were selected based on blood brain barrier (BBB) permeability, acceptable ADME properties as well as their molecular interaction profiles with the aforementioned AD-targets. The present study has predicted certain molecules that appear worthy to be tested for effectiveness against two AD targets, namely AChE and GSK-3β. However, the results warrant further wet laboratory validation, as computational studies are merely predictive in nature. This approach might be useful for future treatment of AD.
Communicated by Ramaswamy H. Sarma