@inproceedings{xu-etal-2026-molsafeeval,
title = "{M}ol{S}afe{E}val: A Benchmark for Uncovering Safety Risks in {AI}-Generated Molecules",
author = "Xu, Tong and
Cao, Xinzhe and
Zhu, Zhihui and
Ding, Keyan and
Chen, Huajun",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1679/",
pages = "33621--33648",
ISBN = "979-8-89176-395-1",
abstract = "Current molecular generation benchmarks emphasize task complexity, molecule novelty, and property alignment; they largely overlook a critical concern: the potential safety risks of AI-generated molecules. In practice, many generative models may produce molecules with toxic, reactive, or otherwise hazardous characteristics{---}posing hidden dangers that remain insufficiently addressed. To address this gap, we introduce MolSafeEval, a benchmark dedicated to evaluating and analyzing the safety risks of molecular generation. Unlike prior approaches that rely on narrow toxicity predictors, MolSafeEval integrates heterogeneous safety knowledge{---}ranging from toxicological databases to hazard rules{---}into a structured molecular safety knowledge graph. This graph serves as a foundation for large language model{--}based reasoning, enabling systematic detection and explanation of unsafe features in generated compounds. We further categorize molecular generative models into four representative task types{---}unconditional generation, property optimization, target protein{--}based design, and text-based generation{---}and provide standardized datasets and safety evaluation protocols for each."
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<abstract>Current molecular generation benchmarks emphasize task complexity, molecule novelty, and property alignment; they largely overlook a critical concern: the potential safety risks of AI-generated molecules. In practice, many generative models may produce molecules with toxic, reactive, or otherwise hazardous characteristics—posing hidden dangers that remain insufficiently addressed. To address this gap, we introduce MolSafeEval, a benchmark dedicated to evaluating and analyzing the safety risks of molecular generation. Unlike prior approaches that rely on narrow toxicity predictors, MolSafeEval integrates heterogeneous safety knowledge—ranging from toxicological databases to hazard rules—into a structured molecular safety knowledge graph. This graph serves as a foundation for large language model–based reasoning, enabling systematic detection and explanation of unsafe features in generated compounds. We further categorize molecular generative models into four representative task types—unconditional generation, property optimization, target protein–based design, and text-based generation—and provide standardized datasets and safety evaluation protocols for each.</abstract>
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%0 Conference Proceedings
%T MolSafeEval: A Benchmark for Uncovering Safety Risks in AI-Generated Molecules
%A Xu, Tong
%A Cao, Xinzhe
%A Zhu, Zhihui
%A Ding, Keyan
%A Chen, Huajun
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F xu-etal-2026-molsafeeval
%X Current molecular generation benchmarks emphasize task complexity, molecule novelty, and property alignment; they largely overlook a critical concern: the potential safety risks of AI-generated molecules. In practice, many generative models may produce molecules with toxic, reactive, or otherwise hazardous characteristics—posing hidden dangers that remain insufficiently addressed. To address this gap, we introduce MolSafeEval, a benchmark dedicated to evaluating and analyzing the safety risks of molecular generation. Unlike prior approaches that rely on narrow toxicity predictors, MolSafeEval integrates heterogeneous safety knowledge—ranging from toxicological databases to hazard rules—into a structured molecular safety knowledge graph. This graph serves as a foundation for large language model–based reasoning, enabling systematic detection and explanation of unsafe features in generated compounds. We further categorize molecular generative models into four representative task types—unconditional generation, property optimization, target protein–based design, and text-based generation—and provide standardized datasets and safety evaluation protocols for each.
%U https://aclanthology.org/2026.findings-acl.1679/
%P 33621-33648
Markdown (Informal)
[MolSafeEval: A Benchmark for Uncovering Safety Risks in AI-Generated Molecules](https://aclanthology.org/2026.findings-acl.1679/) (Xu et al., Findings 2026)
ACL