ToxReason: A Benchmark for Mechanistic Chemical Toxicity Reasoning via Adverse Outcome Pathway

TL;DR

ToxReason is a new benchmark based on the Adverse Outcome Pathway framework that evaluates the ability of models to perform mechanistic reasoning for chemical toxicity prediction across multiple organs.

Contribution

It introduces ToxReason, a benchmark that assesses both toxicity prediction and mechanistic reasoning grounded in biological pathways, filling a gap in existing evaluation methods.

Findings

Strong predictive performance does not guarantee reliable mechanistic reasoning.

Reasoning-aware training enhances both mechanistic understanding and toxicity prediction.

ToxReason reveals the need for integrating reasoning into model evaluation and training.

Abstract

Recent advances in large language models (LLMs) have enabled molecular reasoning for property prediction. However, toxicity arises from complex biological mechanisms beyond chemical structure, necessitating mechanistic reasoning for reliable prediction. Despite its importance, current benchmarks fail to systematically evaluate this capability. LLMs can generate fluent but biologically unfaithful explanations, making it difficult to assess whether predicted toxicities are grounded invalid mechanisms. To bridge this gap, we introduce ToxReason, a benchmark grounded in the Adverse Outcome Pathway (AOP) that evaluates organ-level toxicity reasoning across multiple organs. ToxReason integrates experimental drug-target interaction evidence with toxicity labels, requiring models to infer both toxic outcomes and their underlying mechanisms from Molecular Initiating Event (MIE) to Adverse Outcome (AO). Using ToxReason, we evaluate toxicity prediction performance and reasoning quality across diverse LLMs. We find that strong predictive performance does not necessarily imply reliable reasoning. Furthermore, we show that reasoning-aware training improves mechanistic reasoning and, consequently, toxicity prediction performance. Together, these results underscore the necessity of integrating reasoning into both evaluation and training for trustworthy toxicity modeling.