Abstract
Drug-induced hepatotoxicity (DIH), characterized by diverse phenotypes and complex mechanisms, remains a critical challenge in drug discovery. To systematically decode this diversity and complexity, we propose a multi-dimensional computational framework integrating molecular structure analysis with disease pathogenesis exploration, focusing on drug-induced intrahepatic cholestasis (DIIC) as a representative DIH subtype. First, a graph-based modularity maximization algorithm identified DIIC risk genes, forming a DIIC module and eight disease pathogenesis clusters. Network proximity values between drug targets and DIIC clusters were calculated to define drug-disease relationships. Subsequently, a random forest model combining Mordred molecular descriptors, structural alerts (SAs), and network proximity achieved robust DIIC prediction: Accuracy(ACC) = 0.740 ± 0.014 and area under the curve (AUC) = 0.828 ± 0.008 (ntraining = 342, nvalidation = 114, nexternal test = 295, randomly modeling 100 times). Notably, a K-nearest neighbors-graph convolutional network classified drugs into 8 clusters, with the Cluster 3 model demonstrating superior performance (ACC = 0.810 ± 0.024; AUC = 0.890 ± 0.014; ntraining = 186, nvalidation = 63, nexternal test = 172). Mechanistic analysis linked critical SAs to DIIC pathogenesis: (i) Furan (SA3) perturbed cytochrome P450-mediated metabolism and regulation of lipid metabolism by PPARα; (ii) Nitrogen-sulfur heteroatom chains (SA7) disrupted metabolism of steroids; (iii) Phenylthio groups (SA12) and their CYP450 metabolites induced cholestasis. This multi-dimensional framework bridges molecular features and disease mechanisms, offering a generalizable strategy for toxicity prediction and pathway-centric drug safety evaluation, especial for complex disease.