arXiv AI September 17, 2026 By Moritz Friedemann, Zachary Shinnick, Philip Torr, Bruno Andreis

Procedural Pretraining for Molecular Property Prediction

The paper proposes a three‑stage training pipeline that begins with procedural pretraining on abstract, procedurally generated data, followed by molecular pretraining on SMILES, and finally downstream fine‑tuning for molecular property prediction. Experiments show that procedural pretraining improves downstream performance—e.g., a 4.8% error reduction on Lipophilicity—especially when labeled data are scarce, and that the benefit peaks at an intermediate procedural training budget. Analysis indicates that transferable knowledge resides mainly in attention layers, while feed‑forward layers may over‑specialize.

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fine-tuning

arXiv Machine Learning

Jul 1

Can Tabular In-Context Learners Generalize to Biomolecular Property Prediction?

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arXiv AI

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By Junwoo Park, Minyoung Shin, Cheol Soon Lee, Sujee Lee

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Hugging Face Trending Papers

Aug 11

Multi-Granular Rationale-Guided Molecular LLM for Property Prediction

Large language models (LLMs) are widely applied across chemical tasks, such as molecular property prediction, which underpins drug discovery. Molecular LLMs represent a molecule through several modalities, notably a 1D SMILES sequence or a 2D molecular graph.

llms fine-tuning

arXiv Machine Learning

Jun 11

Probabilistic Contrastive Pretraining for Multi-task ADME Property Prediction

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By Yifan Xue, Srimukh Prasad Veccham, Saee Paliwal, Tyler Shimko, Micha Livne

llms fine-tuning

arXiv Machine Learning

Aug 20

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

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.

By Blazej Banaszewski, Andrew W. Fitzgibbon

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