ToxReason: 一个通过不良后果途径进行机理化学毒性推理的基准
ToxReason: A Benchmark for Mechanistic Chemical Toxicity Reasoning via Adverse Outcome Pathway
AI总结：
ToxReason基准通过整合药物-靶点相互作用证据与毒性标签，评估多器官水平的毒性推理能力，发现强预测性能不等于可靠推理，且推理意识训练能提升机理推理和毒性预测性能。
摘要：
近年来，大型语言模型（LLMs）的进步使分子性质预测成为可能。然而，毒性源于复杂的生物机制，超越了化学结构，需要机理推理以实现可靠预测。尽管其重要性，当前基准未能系统评估此能力。LLMs可以生成流畅但生物不忠实的解释，使难以评估预测毒性是否基于无效机制。为弥合这一差距，我们引入ToxReason，一个基于不良后果途径（AOP）的基准，评估多器官水平的毒性推理能力。ToxReason整合实验药物-靶点相互作用证据与毒性标签，要求模型从分子初始事件（MIE）到不良后果（AO）推断毒性结果及其潜在机制。使用ToxReason，我们评估了不同LLMs的毒性预测性能和推理质量。我们发现，强预测性能不必然意味着可靠的推理。此外，我们证明推理意识训练能提升机理推理，从而提升毒性预测性能。这些结果强调了在评估和训练中整合推理对于可信毒性建模的必要性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
