An explainable hypothesis-driven approach to Drug-Induced Liver Injury with HADES
Drug-induced liver injury (DILI) remains a leading cause of late-stage clinical trial attrition. However, existing computational predictors primarily rely on binary classification, a framing that limits generalization and yields no mechanistic insight to guide translational decisions. We argue that DILI prediction is better posed as an explainable hypothesis-generation problem. To support this shift, we introduce the DILER Benchmark, a dataset that extends beyond binary labels by augmenting a curated set of molecules with mechanistic hepatotoxicity hypotheses derived from biomedical literature. We further present HADES, an agentic system designed to generate transparent and auditable reas...
| Authors | Maciej Wisniewski, Bartosz Topolski, Pawel Dabrowski-Tumanski et al. |
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| Primary category | cs.AI |
| Published | 4 May 2026 |
| Last updated at source | 6 May 2026 |
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