Monroe: A Molecular Foundation Model for In-Context Probabilistic Inference
Bioassay activity prediction is often data-limited because drug-discovery datasets rely on time-consuming and expensive wet-lab experiments for data generation and evaluation. This challenge has inspired recent research into molecular foundation models (MFMs), which aim to encode general-purpose chemical knowledge into molecular representations that generalize well in data-constrained scenarios. This paper presents Monroe, a new MFM with several innovations over the existing state of the art: increased scale allowing pre-training on over 81 million molecules from the PM6 quantum chemistry data
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- FuzzySimilar title/name (fuzzy) · 84%xorbitsai/inference →
“Fuzzy title match (0.92): “Monroe: A Molecular Foundation Model for In-Context Probabil” ≈ “xorbitsai/inference””
- LinkedLinked via arxiv author · 85%Blazej Banaszewski →
“Monroe: A Molecular Foundation Model for In-Context Probabilistic Inference”
- LinkedLinked via arxiv author · 85%Andrew W. Fitzgibbon →
“Monroe: A Molecular Foundation Model for In-Context Probabilistic Inference”