An explainable hypothesis-driven approach to Drug-Induced Liver Injury with HADES

• URL: http://arxiv.org/abs/2605.02669v2
• Date: Wed, 06 May 2026 13:52:57 GMT
• Title: An explainable hypothesis-driven approach to Drug-Induced Liver Injury with HADES
• Abstract summary: Drug-induced liver injury (DILI) remains a leading cause of late-stage clinical trial attrition.<n>We argue that DILI prediction is better posed as an explainable hypothesis-generation problem.<n>We present HADES, an agentic system designed to generate transparent and auditable reasoning traces.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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 reasoning traces. By combining molecular-level predictions, metabolite decomposition, structural understanding, and toxicity pathway evidence, HADES mechanistically assesses DILI risk. Evaluated on the DILER Benchmark, HADES outperforms existing models in binary classification, achieving a ROC-AUC of 0.68 on the Test Set and 0.59 on the challenging Post-2021 Set, compared with 0.63 and 0.50 for DILI-Predictor, respectively. More importantly, we establish a baseline for mechanistic hypothesis generation, where HADES achieves a Hypothesis Alignment Fuzzy Jaccard Index of 0.16. This result underscores the inherent complexity of the task while highlighting the need for advanced explainable approaches in predictive toxicology.

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