Does Pretraining Teach Chemical Language Models Chemistry?
Chemical language models (CLMs) are pretrained on large corpora of molecules with the aim of learning representations that transfer to diverse downstream tasks. Here, we systematically study the effects of pretraining using a series of such CLMs across 64 molecular property prediction datasets. We find that the downstream gains of pretraining in CLMs do not scale and performance saturates below gradient-boosting baselines. Across the regimes evaluated, only supervised pretraining on traditional physico-chemical descriptors consistently improves downstream performance for the CLMs tested whereas some forms of pretraining result in models worse than a randomly initialized encoder. While mod...
| Authors | Leif Sieben, Yoel Zimmermann |
|---|---|
| Primary category | cs.LG |
| Published | 13 October 2026 |
| Last updated at source | 14 October 2026 |
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