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

TL;DR

This paper introduces HADES, an explainable system for predicting drug-induced liver injury by generating mechanistic hypotheses, outperforming existing models and providing transparent reasoning.

Contribution

The paper presents HADES, a novel agentic system that combines mechanistic insights with predictions, and introduces the DILER Benchmark dataset for hypothesis-driven DILI assessment.

Findings

HADES achieves ROC-AUC of 0.68 on the Test Set.

HADES outperforms DILI-Predictor in binary classification.

HADES attains a Hypothesis Alignment Fuzzy Jaccard Index of 0.16.

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.