{"ID":5551959,"CreatedAt":"2026-07-02T01:54:51.863792489Z","UpdatedAt":"2026-07-04T05:21:04.038526805Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.00464","arxiv_id":"2607.00464","title":"MolSafeEval: A Benchmark for Uncovering Safety Risks in AI-Generated Molecules","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. By systematically revealing the safety vulnerabilities of current generative approaches, MolSafeEval offers a new lens for benchmarking molecular models and provides essential guidance toward safer, more trustworthy molecular design.","short_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 characterist...","url_abs":"https://arxiv.org/abs/2607.00464","url_pdf":"https://arxiv.org/pdf/2607.00464v1","authors":"[\"Tong Xu\",\"Xinzhe Cao\",\"Zhihui Zhu\",\"Keyan Ding\",\"Huajun Chen\"]","published":"2026-07-01T05:33:51Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.CL\"]","methods":"[\"Language Model\"]","has_code":false}