Author

Andrew W. Fitzgibbon

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Preprint Aug 2026

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

Monroe is presented, 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 conformer denoising and embedding de...

Błażej Banaszewski, Andrew W. Fitzgibbon · 1 citation

#artificial intelligence Preprint Aug 2026

PGFS++: Molecular Property Improvement under Synthesis and Diversity Constraints

PGFS++ is introduced, a synthesis-aware reinforcement learning framework for input-specific molecular improvement that improves target properties while preserving high output diversity, and experiments show that PGFS++ improves target properties while preserving high output diversity.

Bo-Qi Zhang, G. James, S. Gottipati et al. · 0 citations