AI Accelerated Chemical Screening Integrates ChemBERTa to Identify Repositionable CASP4 Inhibitors via MD Simulations and MM/PBSA Analysis

Building on this foundation, the workflow incorporated virtual screening, cheminformatics modeling, PK–PD evaluation, molecular docking, molecular dynamics simulations, and MM/PBSA free-energy analysis to systematically prioritize repurposed DrugBank compounds for CASP4-targeted Alzheimer’s disease therapy.

A Random Forest classifier trained on the hybrid ChemBERTa physicochemical feature set distinguished active from inactive compounds with ∼95% accuracy (ROC–AUC = 0.73) and achieved a ∼3.5-fold enrichment of active compounds among the top-ranked hits, while a companion Random Forest regressor, trained on the experimental pIC₅₀ values of active compounds, ranked candidates by predicted potency. In addition, integrated cheminformatics modeling, PK–PD analysis, and molecular docking further narrowed the selection to the top five candidate compounds: DB00519, DB01068, DB06202, DB08882, and DB05316.