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 dataset; improved graph representation of stereochemistry; improved training losses including conform...
| Authors | Blazej Banaszewski, Andrew W. Fitzgibbon |
|---|---|
| Primary category | cs.LG |
| Published | 19 August 2026 |
| Last updated at source | 19 August 2026 |
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