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 cu

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ethics.ai (4 May 2026), “An explainable hypothesis-driven approach to Drug-Induced Liver Injury with HADES,” evidence record 5016, https://ethics.ai/record/5016 (originally published by arXiv).

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