MolSafeEval: A Benchmark for Uncovering Safety Risks in AI-Generated Molecules

• URL: http://arxiv.org/abs/2607.00464v1
• Date: Wed, 01 Jul 2026 05:33:51 GMT
• Title: MolSafeEval: A Benchmark for Uncovering Safety Risks in AI-Generated Molecules
• Abstract summary: MolSafeEval is a benchmark dedicated to evaluating and analyzing the safety risks of molecular generation.<n>It integrates heterogeneous safety knowledge - ranging from toxicological databases to hazard rules - into a structured molecular safety knowledge graph.<n>By systematically revealing the safety vulnerabilities of current generative approaches, MolSafeEval offers a new lens for benchmarking molecular models.
• Score: 60.55214893687032
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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