Ligand Based Pharmacophore Modeling, Virtual Screening, Molecular Docking, Molecular Dynamic simulation and In-silico ADMET Studies for the Discovery of Potential BACE-1 Inhibitors

Publication: Research Square
Software: ADMET Predictor®
Therapeutic Areas: CNS


Pharmacophore modeling is an innovative technology to explore and extract potential interactions between ligand-protein complexes. On the other hand, virtual screening is an in-silico technique that uses pharmacophore models to analyze extensive databases of compounds or approved drugs to evaluate interactions. These techniques enable to discover, establish, and evaluate therapeutics and other biologically active compounds and also allow the optimization of several hundred and thousand compounds to be tested for interaction against the target protein or receptor, which narrows down the potential molecules that can be used for further studies. Drug repurposing can be done by integrating these techniques into the study design, allowing reduced cost associated with conventional hit and trial testing of compounds, running large databases in shorter duration. The study reported the successful generation and validation of pharmacophore model with subsequent virtual screening. Virtual screening of databases produced 6 hits which were further subjected to in-silico analysis and resulted in identification of anileridine as the potential BACE-1 inhibitor. Anileridine showed significant interaction with one of the important amino acids of the catalytic dyad of the enzyme i.e. Asp32. Furthermore, MD simulations supported the molecular docking and MM-GBSA results and revealed to formation of stable interactions between anileridine and BACE-1. After establishing anileridine as the potential BACE-1 inhibitor procured from already approved drugs, it was subjected to extensive in-silico ADMET studies. Furthermore, the model (AHRRR) can be used to rationally design novel inhibitors of BACE-1 and also identify new molecules from databases as potential BACE-1 inhibitors.

By Usman Shareef, Aisha Altaf, Muhammad Kazim Zargaham, Rohail Bhatti, Ahsan Ibrahim, Muhammad Ammar Zahid