Monroe: A Molecular Foundation Model for In-Context Probabilistic Inference

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.