Integrating multi-structure covalent docking with machine-learning consensus scoring enhances potency ranking of human acetylcholinesterase inhibitors.

ABSTRACT: Acetylcholinesterase (AChE) inhibition is a key mechanism in the treatment of neurodegenerative diseases and in counteracting toxic exposures to pesticides and nerve agents. However, accurately ranking the potency of covalently binding AChE inhibitors remains challenging due to the enzyme's structural flexibility and the chemical diversity of their covalent warheads. In this study, we developed an in silico protocol that integrates multi-structure covalent docking and machine-learning (ML) consensus scoring to improve docking-based potency ranking among covalent AChE inhibitors. We analyzed 65 ligand-bound (holo) human AChE crystal structures using hierarchical clustering to identify four representative conformations, along with one high-resolution apo structure, for multi-structure dockin

SUBMITTER: Jaladanki CK 

PROVIDER: S-EPMC12866926 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature