ToxiMol：首个面向 MLLM 分子毒性修复的基准，多模态大模型准备好「改造坏分子」了吗？
Breaking Bad Molecules: Are MLLMs Ready for Structure-Level Molecular Detoxification?
研究团队推出 ToxiMol，首个面向通用多模态大语言模型（MLLM）的分子毒性修复基准，覆盖 11 项主任务和 660 个代表性有毒分子，并配套 ToxiEval 自动化评估框架。
Authors:Fei Lin, Ziyang Gong, Cong Wang, Tengchao Zhang, Yonglin Tian, Yining Jiang, Ji Dai, Chao Guo, Xiaotong Yu, Xue Yang, Gen Luo, Fei-Yue Wang
Abstract:Toxicity remains a leading cause of early-stage drug development failure. Despite advances in molecular design and property prediction, the task of molecular toxicity repair, generating structurally valid molecular alternatives with reduced toxicity, has not yet been systematically defined or benchmarked. To fill this gap, we introduce ToxiMol, the first benchmark task for general-purpose Multimodal Large Language Models (MLLMs) focused on molecular toxicity repair. We construct a standardized dataset covering 11 primary tasks and 660 representative toxic molecules spanning diverse mechanisms and granularities. We design a prompt annotation pipeline with mechanism-aware and task-adaptive capabilities, informed by expert toxicological knowledge. In parallel, we propose an automated evaluation framework, ToxiEval, which integrates toxicity endpoint prediction, synthetic accessibility, drug-likeness, and structural similarity into a high-throughput evaluation chain for repair success. We systematically assess 43 mainstream general-purpose MLLMs and conduct multiple ablation studies to analyze key issues, including evaluation metrics, candidate diversity, and failure attribution. Experimental results show that although current MLLMs still face significant challenges on this task, they begin to demonstrate promising capabilities in toxicity understanding, semantic constraint adherence, and structure-aware editing.
| Comments: | Accepted by KDD 2026 (AI for Sciences Track, Oral) |
| Subjects: | Artificial Intelligence (cs.AI); Computation and Language (cs.CL) |
| Cite as: | arXiv:2506.10912 [cs.AI] |
| (or arXiv:2506.10912v5 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2506.10912 arXiv-issued DOI via DataCite |
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| Journal reference: | Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2 (KDD '26), Jeju Island, Republic of Korea, pp. 11399-11410, 2026 |
| Related DOI: | https://doi.org/10.1145/3770855.3818840
DOI(s) linking to related resources |
Submission history
From: Fei Lin [view email]
[v1]
Thu, 12 Jun 2025 17:25:53 UTC (1,414 KB)
[v2]
Wed, 18 Jun 2025 14:00:37 UTC (1,414 KB)
[v3]
Wed, 28 Jan 2026 17:20:25 UTC (1,937 KB)
[v4]
Tue, 2 Jun 2026 18:21:07 UTC (1,938 KB)
[v5]
Wed, 4 Nov 2026 18:36:42 UTC (1,960 KB)
来源：arXiv cs.AI · arxiv.org