Monroe: A Molecular Foundation Model for In-Context Probabilistic Inference
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TL;DR - Monroe is a molecular foundation model pretrained on more than 81 million molecules and paired with TabPFN for in-context bioassay activity prediction. It matches or exceeds prior models on established benchmarks and significantly improves performance on activity-cliff tasks relevant to molecular discovery.
- Introduces richer stereochemistry-aware graph representations, conformer-denoising and embedding-decorrelation losses, and improved multi-task learning.
- Uses a prior-data-fitted network as the downstream predictor, targeting data-scarce drug-discovery settings without conventional task-specific adaptation.
- Applies statistically principled pairwise comparisons on Polaris and activity-cliff benchmarks.
- The PFN strategy also improves MiniMol and CheMeleon, indicating that the downstream approach generalizes beyond Monroe.
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Monroe: A Molecular Foundation Model for In-Context Probabilistic Inference
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Hugging Face upvotes 0 · Semantic Scholar citations 0 · Semantic Scholar influential citations 0
TL;DR - Monroe is a molecular foundation model pretrained on more than 81 million molecules and paired with TabPFN for in-context bioassay activity prediction. It matches or exceeds prior models on established benchmarks and significantly improves performance on activity-cliff tasks relevant to molecular discovery.
- Introduces richer stereochemistry-aware graph representations, conformer-denoising and embedding-decorrelation losses, and improved multi-task learning.
- Uses a prior-data-fitted network as the downstream predictor, targeting data-scarce drug-discovery settings without conventional task-specific adaptation.
- Applies statistically principled pairwise comparisons on Polaris and activity-cliff benchmarks.
- The PFN strategy also improves MiniMol and CheMeleon, indicating that the downstream approach generalizes beyond Monroe.