Papers › ChemBERTa-2: Towards Chemical Foundation Models
ChemBERTa-2: Towards Chemical Foundation Models
Walid Ahmad, Elana Simon, Seyone Chithrananda, Gabriel Grand, Bharath Ramsundar
Large pretrained models such as GPT-3 have had tremendous impact on modern natural language processing by leveraging self-supervised learning to learn salient representations that can be used to readily finetune on a wide variety of downstream tasks. We investigate the possibility of transferring such advances to molecular machine learning by building a chemical foundation model, ChemBERTa-2, using the language of SMILES. While labeled data for molecular prediction tasks is typically scarce, libraries of SMILES strings are readily available. In this work, we build upon ChemBERTa by optimizing the pretraining process. We compare multi-task and self-supervised pretraining by varying hyperparameters and pretraining dataset size, up to 77M compounds from PubChem. To our knowledge, the 77M set constitutes one of the largest datasets used for molecular pretraining to date. We find that with these pretraining improvements, we are competitive with existing state-of-the-art architectures on the MoleculeNet benchmark suite. We analyze the degree to which improvements in pretraining translate to improvement on downstream tasks.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Molecular Property Prediction | BACE | ChemBERTa-2 (MTR-77M) | RMSE | 1.363 | #12 of 20 | Archive leaderboard | report |
| Molecular Property Prediction | BACE | ChemBERTa-2 (MTR-77M) | ROC-AUC | 79.9 | #12 of 20 | Archive leaderboard | report |
| Molecular Property Prediction | BBBP | ChemBERTa-2 (MTR-77M) | ROC-AUC | 72.8 | #17 of 29 | Archive leaderboard | report |
| Molecular Property Prediction | Clearance | ChemBERTa-2 (MTR-77M) | RMSE | 48.515 | #2 of 2 | Archive leaderboard | report |
| Molecular Property Prediction | ESOL | ChemBERTa-2 (MTR-77M) | RMSE | 0.889 | #18 of 20 | Archive leaderboard | report |
| Molecular Property Prediction | Lipophilicity | ChemBERTa-2 (MTR-77M) | RMSE | 0.798 | #9 of 13 | Archive leaderboard | report |
| Molecular Property Prediction | clintox | ChemBERTa-2 (MTR-77M) | Molecules (M) | 77 | #19 of 20 | Archive leaderboard | report |
| Molecular Property Prediction | clintox | ChemBERTa-2 (MTR-77M) | ROC-AUC | 56.3 | #19 of 20 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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