arXiv Machine Learning By Tong Xu, Xinzhe Cao, Zhihui Zhu, Keyan Ding, Huajun Chen

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

Read the original on arXiv Machine Learning →

arXiv:2607. 00464v1 Announce Type: new 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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning
Jun 5

MolE-RAG: Molecular Structure-Enhanced Retrieval-Augmented Generation for Chemistry

arXiv:2606. 05693v1 Announce Type: new Abstract: Large language models (LLMs) have shown promise for molecular property prediction, but their ability to reason over chemical structures remains limited, as molecular representations such as SMILES differ substantially from the natural language on which LLMs are primarily trained.

By Joey Chan, Wonbin Kweon, Ashley Shin, Niharika Bhattacharjee, Pengcheng Jiang, Yue Guo, Jiawei Han