arXiv Machine Learning September 22, 2026 By Hyuntae Park, Sooyeon Kim, Jiwon Park, SangKeun Lee

MolSC: Leveraging Substituent Contributions to Enhance Fine-grained Molecular Understanding in LLMs

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MolSC is a new dataset of 181,000 substituent-level examples that captures how attaching specific substituents to molecular scaffolds changes properties such as bioactivity and physicochemical descriptors. The authors also provide MolSC-Bench, a held‑out benchmark of 1,541 examples that are disjoint from MolSC at scaffold, substituent, and molecule levels. Experiments show that training molecular large language models on MolSC markedly improves their ability to predict substituent contributions, outperforming existing models on a range of downstream chemistry tasks.

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