Integrating Multi-Structure Covalent Docking with Machine Learning Consensus Scoring Enhances Virtual Screening of Human Acetylcholinesterase Inhibitors

Acetylcholinesterase (AChE) inhibition is a key mechanism in the treatment of neurodegenerative diseases and in counteracting toxic exposures to pesticides and nerve agents. However, virtual screening of AChE remains challenging due to the enzyme’s structural flexibility and the chemical diversity of its covalently binding inhibitors. In this study, we developed an in silico protocol that integrates multi-structure covalent docking and machine learning (ML) consensus scoring to improve the prediction of 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 docking. A curated library of 412 organophosphate and carbamate inhibitors was then docked covalently and non-covalently into each receptor conformation. The resulting docking scores were evaluated against inhibitors’ experimental logIC ₅₀ values using Spearman’s rank correlation coefficient (r). Covalent docking outperformed non-covalent docking (r values up to 0.54 vs 0.18), and our ML consensus model trained on the five structures’ covalent docking scores achieved the highest predictive accuracy (r = 0.70), surpassing all single-structure and conventional consensus baselines. Chemical cluster analysis revealed structure–activity trends based on ligand flexibility, polarity, and aromaticity. SHapley Additive exPlanations analysis highlighted the ML consensus model’s ability to flexibly distribute the influence each structure’s scores played on its predictions. It identified and exploited relationships based on its training dataset that would be difficult to anticipate through a manual analysis of individual structures’ docking performance metrics. This framework is broadly applicable to other covalently targeted proteins, offering a generalizable and interpretable strategy for data-driven covalent inhibitor discovery.