Generating an efficient BACE1 inhibitor for the treatment of Alzheimer’s disease based on AI-powered ADMET analysis, Molecular docking and Molecular dynamics studies

Abstract

Alzheimer’s disease (AD) is an early stage of dementia due to neurodegenerative disorder that affects the cognitive functions, memory patterns, and learning skills. BACE1 (beta-site amyloid precursor protein cleaving enzyme 1) is a crucial protein involved in the progression of AD. There are many clinical trials being carried out targeting BACE1 for the treatment of AD. However, they critically face limitations to succeed as approved drugs. Hence, this work is aimed to identify novel BACE1 inhibitors using AI-driven drug development processes. Using WADDAICA tool, 300 similar ligands based on the structural features of Atebecestat, AZD3839, LY2811376 are generated and then ADMET analysis was done. The molecular docking studies with BACE1 protein complexes (PDB ID: 7DCZ, 4B05, 4YBI) were helpful to identify 3 ligands as promising BACE1 inhibitors having low-binding energy, and by conducting 100 ns molecular dynamic simulation study, a minimal fluctuations was demonstrated with the considerable duration. Finally, an efficient BACE1 inhibitor M6 {O=C(N(C1CC1)Cc1cccc(c1)c1cccnc1)
c1cccc(c1)n1nncc1} with good binding affinity, potency (-7.83 Kcal/mol, 1.83 μM), and high BBB permeability for the treatment of AD is sub-selected from the huge volume of chemical spaces, which will be helpful to narrow down the time factor and can pave ways for subsequent in-vitro studies.