DyphAI dynamic pharmacophore modeling with AI: a tool for efficient screening of new acetylcholinesterase inhibitors.

ABSTRACT: Therapeutic strategies for Alzheimer's disease (AD) often involve inhibiting acetylcholinesterase (AChE), underscoring the need for novel inhibitors with high selectivity and minimal side effects. A detailed analysis of the protein-ligand pharmacophore dynamics can facilitate this. In this study, we developed and employed dyphAI, an innovative approach integrating machine learning models, ligand-based pharmacophore models, and complex-based pharmacophore models into a pharmacophore model ensemble. This ensemble captures key protein-ligand interactions, including π-cation interactions with Trp-86 and several π-π interactions with residues Tyr-341, Tyr-337, Tyr-124, and Tyr-72. The protocol identified 18 novel molecules from the ZINC database with binding energy values ranging from -6

SUBMITTER: Hayek-Orduz Y 

PROVIDER: S-EPMC11865752 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature