Year: 2026
Type: conference-paper
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.
Authors: Tong Xu, Xinzhe Cao, Zhihui Zhu, Keyan Ding, Huajun Chen
Institutions: Zhejiang University, University of Oxford
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166867816