Chemical language models underperform baselines despite pretraining
Source published ·Modelwire updated
Original coverage: arXiv cs.CL ↗·How Modelwire adds context
The development
A systematic evaluation of chemical language models reveals a sobering gap between pretraining hype and practical utility in molecular property prediction. Across 64 datasets, researchers found that unsupervised pretraining on molecular corpora fails to deliver consistent downstream gains, with performance plateauing below traditional gradient-boosting methods. Only supervised pretraining on hand-crafted physicochemical descriptors showed reliable improvement, while some pretraining approaches actually degraded performance relative to random initialization. The finding challenges the assumption that scale and self-supervised learning automatically transfer to chemistry tasks, suggesting domain-specific inductive biases may matter more than raw model capacity for this application.
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The critical qualifier buried in the results: supervised pretraining on hand-crafted descriptors worked, but unsupervised pretraining on raw molecular data did not. This suggests the models didn't learn chemistry from molecules alone, only from explicit chemical features humans already knew to encode. That's a very different finding than 'pretraining doesn't help chemistry' and points to a data representation problem, not a pretraining problem.
This directly challenges the optimism in 'Latent Taxonomies' (EleutherAI/Allenai, Oct 11), which showed that LLMs spontaneously develop structured reasoning during standard pretraining. Here, chemical language models failed to develop domain-specific inductive biases from molecular corpora alone, suggesting that taxonomy emergence in language models may not generalize to specialized scientific domains. The gap between these findings implies that chemistry requires either denser inductive biases baked into the model architecture (like 'Masked Diffusion Language Agents' from Oct 9) or richer supervision than raw text provides.
If the authors release ablations showing which molecular representations (SMILES, graphs, 3D coordinates) were used during pretraining, and whether models pretrained on 3D structure or graph embeddings outperform those trained on text-only SMILES, that would clarify whether the failure was representation-agnostic or specific to how molecules were tokenized. If performance gaps close with better tokenization schemes within the next 6 months, the pretraining hypothesis survives; if not, it suggests chemistry tasks genuinely require supervised feature engineering.
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Coverage behind this analysis
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·arXiv cs.CL
Language models develop human-like property induction without explicit training
Researchers at EleutherAI and Allenai demonstrate that language models spontaneously develop human-like categorical reasoning during standard next-token prediction. By pre-training Pythia and OLMo 2 models on synthetic hierarchical taxonomies, they show that property induction emerges without explicit supervision: learned facts transfer more readily within taxonomic clusters than across them. This finding reshapes understanding of how…
MentionsChemical language models · Molecular property prediction · Gradient-boosting · Physicochemical descriptors
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Modelwire summarizes, we don’t republish. arXiv cs.CL originally reported this story as “Does Pretraining Teach Chemical Language Models Chemistry?”. The full content lives on arxiv.org. If you’re a publisher and want a different summarization policy for your work, see our takedown page.