Binding interaction of acetylcholinesterase with steroidal pregnanes: insight from machine learning and atomistic simulation

Acetylcholinesterase (AChE) inhibition is a key strategy in the treatment of Alzheimer’s disease and other neurodegenerative disorders. While pregnane-based compounds have been suggested as AChE inhibitors, their mechanism of action remains unclear. This study employed machine learning (ML) and molecular modeling to probe the molecular interaction of AChE with steroidal pregnanes. The ML models were trained and validated on AChE bioactivity datasets to predict pIC50 and pKi values of small-molecule compounds. Among the models tested, the Random Forest Regressor demonstrated superior performance and was used to identify pregnanes with pIC50 ≥ 5 and pKi ≥ 7 as promising inhibitors. Molecular docking revealed strong molecular interactions between AChE and several pregnanes, particularly 21-[(3-Hydroxy-2-naphthyl)oxy]pregnane-2-one. This compound interacted with critical sub-sites within the AChE binding gorge, including the catalytic active site, peripheral anionic site, oxyanion hole, and anionic sub-site, through multiple hydrogen bonds and hydrophobic interactions. Molecular dynamics simulations over 100 ns indicated structural stability and conformational flexibility of representative AChE-pregnane complexes as indicated by the dynamic parameters and cluster patterns. The Molecular Mechanics with Generalized Born Surface Area free energy analysis confirmed strong binding affinities, while residual energy decomposition provided insights into key residue contributions. Additionally, the pregnanes demonstrated favorable blood-brain barrier permeability and other drug-like properties, suggesting their potential as neurotherapeutic agents. Given their predicted bioactivity, strong interactions with AChE, and drug-like properties, the identified pregnanes warrant further optimization and experimental evaluation for the development of safe and effective AChE inhibitors.