Pretraining chemical language models fails to teach chemistry, study finds

A new arXiv paper investigates whether pretraining chemical language models (CLMs) effectively teaches them chemistry. The study found that pretraining gains do not scale and performance saturates below gradient-boosting baselines, with some pretraining methods even resulting in worse performance than randomly initialized models. While CLMs can resolve molecular structure, they do not do so better than random models, suggesting they primarily learn descriptor-like properties and token statistics rather than deep chemical understanding beyond what classical molecular features already capture. AI

IMPACT Suggests current pretraining methods for chemical language models may not be effectively imparting deep chemical knowledge.

RANK_REASON The cluster contains an academic paper published on arXiv detailing research findings. [lever_c_demoted from research: ic=1 ai=1.0]

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