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HADES system uses AI for explainable drug-induced liver injury prediction

By PulseAugur Editorial · [2 sources] ·

Researchers have developed HADES, an agentic system designed to improve the prediction of drug-induced liver injury (DILI) by shifting from binary classification to hypothesis generation. HADES combines molecular predictions, metabolite decomposition, structural understanding, and toxicity pathway evidence to assess DILI risk with transparent reasoning. In evaluations on the new DILER Benchmark, HADES demonstrated superior performance in binary classification and established a baseline for mechanistic hypothesis generation in predictive toxicology. AI

IMPACT Introduces a novel agentic system for hypothesis generation in toxicology, potentially improving drug development pipelines.

RANK_REASON This is a research paper detailing a new methodology and benchmark for predicting drug-induced liver injury.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

HADES system uses AI for explainable drug-induced liver injury prediction

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This is a research paper detailing a new methodology and benchmark for predicting drug-induced liver injury.
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Maciej Wisniewski, Bartosz Topolski, Pawel Dabrowski-Tumanski, Dariusz Plewczynski, Tomasz Jetka ·

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

    arXiv:2605.02669v1 Announce Type: new 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 …

  2. arXiv cs.AI TIER_1 English(EN) · Tomasz Jetka ·

    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 decis…

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