AI-Driven QSAR and SHAP Analysis of a Scaffold-Focused, Novel Triazole–Naphthalene Library Designed to Target Tau Protein Aggregation.

Tau protein aggregation is a hallmark of Alzheimer’s disease, and disrupting this process presents a compelling therapeutic strategy. In this scaffold-focused pilot study, we present an AI-driven cheminformatics workflow to evaluate a curated library of 16 novel triazole–naphthalene derivatives (TNDs) designed for synthetic accessibility and tau-binding pharmacophore features. Molecular descriptors were computed using RDKit and filtered by Pearson correlation (r > 0.95). Three machine learning models—Random Forest, XGBoost, and Support Vector Regression (SVR)—were trained using AutoDock Vina docking scores against phosphorylated tau protein (PDB ID: 6HRF). SHAP analysis revealed rotatable bond count and hydrophobic surface area as key predictors of docking affinity. The SVR model demonstrated consistent performance (CV RMSE = 0.50 kcal/mol), while PCA and t-SNE confirmed chemical space diversity across the TND scaffold. Retrospective docking highlighted three compounds with sub-micromolar predicted affinity, forming key interactions with tau’s microtubule-binding cleft (e.g., Ser285 and Val287). While the dataset is limited in size, this study establishes a reproducible and interpretable machine learning framework for tau-targeted inhibitor design. A follow-up study is underway using an expanded dataset of 40+ known tau aggregation inhibitors to enhance generalizability, support scaffold hopping, and enable de novo molecular design. In addition, future work will incorporate molecular dynamics (MD) simulations and in vitro tau aggregation assays to experimentally validate binding stability and biological activity. All code and data are open-source to ensure full transparency and reproducibility in Alzheimer’s drug discovery.