Machine learning-based q-RASAR modelling for the in silico identification of novel α7nAChR agonists for anti-Alzheimer's drug discovery.

Abstract

In this study, we employed a quantitative Read-Across Structure-Activity Relationship (q-RASAR) approach to develop a statistically robust machine learning (ML)-based q-RASAR model aimed at predicting the agonistic activity of compounds targeting the α7-nicotinic acetylcholine (α7nACh) receptor, a key therapeutic target in Alzheimer's disease (AD) due to its involvement in cognitive processes and neuroprotection. We developed a rigorously validated univariate q-RASAR linear regression (LR) model using an extensive dataset of 1,727 structurally diverse heterocyclic and aromatic hydrocarbon compounds sourced from the publicly available Binding Database (www.bindingdb.org). Additionally, we explored various other ML-based q-RASAR models to further enhance predictive performance. The established LR q-RASAR model was subsequently applied to predict the Mcule database (https://mcule.com/database/), containing 1,91,94,405 chemical compounds, to identify structurally relevant candidates with potential α7nACh receptor agonistic activity. Additionally, molecular docking analysis and molecular dynamics (MD) simulations for 100 ns were conducted to investigate interactions between the target protein and ligands. Overall, this investigation highlights the critical influence of hydrophobicity, electronic effects, ionization degree, and steric factors as key determinants in the design of potential anti-Alzheimer's agents.