← Back to all news
arXiv Machine Learning By Blazej Banaszewski, Andrew W. Fitzgibbon

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

Read the original on arXiv Machine Learning →

Monroe is a new molecular foundation model that improves upon existing models by pre‑training on over 81 million molecules from the PM6 quantum chemistry dataset, enhancing stereochemistry representation, and introducing novel training losses such as conformer denoising and embedding decorrelation. It also incorporates a prior‑data‑fitted model (TabPFN) for downstream in‑context prediction and demonstrates superior performance on Polaris benchmarks and activity cliff tests. Ablation studies show that the PFN‑based downstream approach can upgrade other models, producing state‑of‑the‑art variants MiniMol_PFN and CheMeleon_PFN.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv Machine Learning
Oct 2

Molecular Property Prediction under Structural Shift with Tabular Foundation Models

The paper introduces MolPAIR, a framework that enhances tabular foundation models (TFMs) for predicting molecular properties under structural shifts by incorporating molecule-level and molecular-pair contexts without task‑specific parameter updates. MolPAIR refines initial predictions from a global TFM by using a second frozen TFM to predict differences in prediction errors between a query molecule and labeled reference molecules. Experiments on 58 MoleculeACE and Polaris tasks show that MolPAIR improves performance on 46 tasks across multiple molecular representations and TFM backbones, demonstrating that explicit molecular comparisons strengthen tabular in‑context learning for structural generalization.

By Jinmo Lee, Dooho Lee, Minho Jeong, Jaemin Yoo
llms
More like this →
arXiv AI
Sep 2

MMAI Gym for Science: Training Liquid Foundation Models for Drug Discovery

arXiv:2603.03517v2 Announce Type: replace-cross Abstract: General-purpose large language models (LLMs) that rely on in-context learning do not reliably deliver the scientific understanding and perfor...

By Maksim Kuznetsov, Zulfat Miftahutdinov, Rim Shayakhmetov, Mikolaj Mizera, Roman Schutski, Bogdan Zagribelnyy, Ivan Ilin, Nikita Bondarev, Thomas MacDougall, Mathieu Reymond, Mihir Bafna, Kaeli Kaymak-Loveless, Eugene Babin, Maxim Malkov, Mathias Lechner, Ramin Hasani, Alexander Amini, Vladimir Aladinskiy, Alex Aliper, Alex Zhavoronkov
llmsbenchmarks
More like this →