Pharmaceuticals
A Pathway-Driven Computational Drug Discovery Framework Integrating Machine Learning and Virtual Screening for Identification of Anti-Rheumatoid Arthritis Natural Small Molecules
By Sun J, Wang Y, Ye H and 3 others.
Abstract
Objective
This study aimed to establish a pathway-driven computational drug discovery framework integrating machine learning, target prioritization, virtual screening, molecular dynamics (MD) simulations, and in vitro evaluation to identify natural small molecules with potential anti-rheumatoid arthritis (RA) activity.
Methods
Sixteen RA-related Gene Expression Omnibus (GEO) datasets were included, with 15 cohorts used for model development and one reserved for independent external validation. Pathway features quantified by gene set variation analysis (GSVA) were subjected to cohort-aware feature selection using a leave-one-dataset-out framework across seven machine-learning algorithms. Predictive modeling and SHapley Additive exPlanations (SHAP) analysis were subsequently performed, followed by random-effects meta-analysis (REMA) and network analysis for candidate-target prioritization. Natural compounds were screened by molecular docking and further evaluated using ligand efficiency, multiple scoring approaches, explicit-membrane MD simulations, and a TNF-α-induced MH7A inflammatory model.