Machine learning and free energy clustering reveal PAH protein binding linked to AD risk.

This study develops a computational framework integrating bioinformatics, machine learning, and ΔG clustering to prioritize polycyclic aromatic hydrocarbons (PAHs) for Alzheimer's disease (AD)-associated neurotoxicity. PAH targets were predicted from ChEMBL/STITCH databases; AD-related differentially expressed genes (DEGs) were identified via WGCNA and differential expression analysis of GEO datasets. Protein-protein interaction (PPI) networks, GO/KEGG enrichment, and XGBoost feature selection identified PARP1, PTPN1, and ITGA4 as candidate core PAH targets enriched in neuroinflammation, microglial activation, lipid metabolism, and atherosclerosis pathways. Molecular docking produced ΔG heatmaps for clustering 16 PAHs into eight toxicity-similarity categories. Category-average ΔG values correlated linearly with literature LD50/BMDL data (ρ = 1, p = 0.0417), yielding an empirical relationship BMDL = 1.723 × ΔG + 22.602. Zebrafish motility assays provided preliminary support (Spearman ρ = -1.0, p = 0.167; n = 3). This pipeline provides initial insights into PAH mechanisms and potential therapeutic targets, pending experimental validation.