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oai:figshare.com:article/34037751 2026-10-01T02:04:44Z category_1 category_4 category_19 category_873 category_734 category_931 category_64 portal_63 item_type_6 month_year_10_2026

Benchmarking Affinity-Based Docking for Irreversible Acetylcholinesterase Inhibitors: A Physics-Informed Reactivity Framework for Stereoselective Potency Prioritization Jeongyun Kim (452920) Jin Yoo (1501492) Doo-Hee Lee (8457927) Ku Kang (10959330) Biophysics Biochemistry Pharmacology Chemical Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified Cancer tpsa alone correlates small panel (< single docked pose permutation support (< informed reactivity framework gnina cnn scores activated serine alkoxide autodock vina affinity irreversible acetylcholinesterase inhibitors bounded prioritization tool reactive geometry rather p </ sub bounded rather affinity scores p </ resulting framework reactive complex p – computational prioritization catalytic serine >< sub poses rather benchmarking affinity r </ n </ test preserves stereochemical results selecting covalent reproducible signal ranked poses pesticide classes often based noncovalent docking mechanistic interpretation measured human kinetic measurement group chemistry downhill within discriminating information descriptor correlations curated set covalent phosphylation bond formation based docking 62 ), 11 ), Irreversible organophosphorus inhibitors of acetylcholinesterase (AChE) act by covalent phosphylation of the catalytic serine, yet their computational prioritization is often based on affinity scores from noncovalent docking. We benchmark that assumption against a curated set of measured human-AChE inhibition rate constants (<i>n</i> = 14; G-, V-, and organophosphate-pesticide classes, including a configuration-resolved VX pair). AutoDock Vina affinity (Spearman ρ = +0.12) and GNINA CNN scores (ρ = −0.40) do not provide statistically reliable potency rankings; explicit covalent docking also remains weak (ρ = +0.23, <i>n</i> = 11), with bootstrap confidence intervals crossing zero. Selecting covalent-compatible (near-attack) poses rather than top-ranked poses does not change this outcome. A quantum-chemical cluster scan supports a mechanistic interpretation: once the activated serine alkoxide/reactive complex is formed, P–O bond formation is downhill within the model, so the discriminating information is expected to reside in electrophilicity, leaving-group chemistry, and transport into reactive geometry rather than in a single docked pose. Consistently, mechanism-oriented molecular descriptors recover a modest but reproducible signal: TPSA alone correlates with potency (ρ = −0.62), and a Gaussian-process model used primarily for calibrated uncertainty yields leave-one-out ρ = +0.55 with permutation support (<i>p</i> ≈ 0.01) and a bootstrap CI whose lower bound lies near zero. A leave-both-VX-enantiomers-out test preserves the experimental <i>S</i>_P > <i>R</i>_P ordering. Given the small panel (<i>n</i> = 14), the descriptor correlations and stereochemical results are reported as exploratory and uncertainty-bounded rather than predictive. The resulting framework is presented as an uncertainty-bounded prioritization tool for data-scarce covalent AChE inhibitors, not as a substitute for kinetic measurement. 2026-09-30T00:00:00Z Text Journal contribution 10.1021/acsomega.6c08355.s001 https://figshare.com/articles/journal_contribution/Benchmarking_Affinity-Based_Docking_for_Irreversible_Acetylcholinesterase_Inhibitors_A_Physics-Informed_Reactivity_Framework_for_Stereoselective_Potency_Prioritization/34037751 CC BY-NC 4.0