Probing Chemical Language Models: Effects of Pre-training and Fine-tuning

Abstract:Chemical language models (CLMs) are trained with linearized representations such as SMILES, yet it remains unclear which chemically meaningful substructures they encode. To foster a better understanding of CLMs, we conduct a systematic study and probe for 78 molecular substructures across eight pre-trained and six randomly initialized models. We furthermore study how fine-tuning on chemical downstream tasks affects the learned representations of molecular substructures. Our results show that pre-training generally improves molecular structure awareness of CLMs, particularly in the upper layers. Moreover, randomly initialized models already encode ring structures well in the first layer. Our analysis on two chemical downstream tasks further reveals that, interestingly, fine-tuning affects task-relevant molecular substructures more than others, indicating that the changes in the representations follow chemical theory.

Machine Learning (cs.LG)

arXiv:2607.02140 [cs.LG]

(or arXiv:2607.02140v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.02140 Focus to learn more arXiv-issued DOI via DataCite

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