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

2026-07-01

AI Summary

This work addresses the critical gap in existing molecular generation benchmarks, which lack systematic evaluation of safety risks—such as toxicity and reactivity—in AI-generated molecules. To this end, we introduce MolSafeEval, the first comprehensive safety evaluation benchmark specifically designed for AI-generated molecules. MolSafeEval integrates multi-source toxicological databases and hazard rules to construct a molecular safety knowledge graph and leverages large language models to enable interpretable safety reasoning. The benchmark supports standardized safety assessments across four representative molecular generation tasks, systematically uncovering safety vulnerabilities in current models. By providing both a rigorous evaluation framework and actionable insights, MolSafeEval serves as a foundational tool for developing safer and more trustworthy AI-driven molecular design methodologies.

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.

Problem

• molecular generation
• safety risks
• AI-generated molecules
• toxicity
• hazardous characteristics

Innovation

• molecular safety
• knowledge graph
• generative AI
• toxicity prediction
• benchmark