ArticleCommunications chemistry2025
An open-source family of large encoder-decoder foundation models for chemistry.
Article in Communications chemistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- High-Conductivity Electrolytes Screened Using Fragment- and Composition-Aware Deep Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Next-Generation Artificial Intelligence for ADME Prediction in Drug Discovery: From Small Molecules to Biologics.Yonago acta medica · 2026Review
- Application and Prospects of Large Language Models in Small-Molecule Drug Discovery.Analytical chemistry · 2025Review
- Physics-Embedded Machine Learning Model for Phase Equilibrium Prediction in Multicomponent Systems.Journal of chemical information and modeling · 2025Article
- An open-source family of large encoder-decoder foundation models for chemistry.Communications chemistry · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The use of foundation models has extended from natural language processing to molecular modeling. In this context, large-scale pre-training strategies have been applied to chemical language models to enable representation learning across diverse tasks. Here we introduce a family of encoder-decoder chemical foundation models pre-trained on a curated dataset of 91 million molecular sequences from PubChem. These models support a range of applications, including property estimation and reaction outcome prediction. We evaluate two model variants across several benchmark datasets and show that they match or exceed existing approaches. We also assess the structure of the learned representations and find that the embedding space supports few-shot learning and separates molecules based on chemically relevant features. This structure appears to result from the decoder-based reconstruction objective used during pre-training. These findings suggest that the proposed models can serve as general-purpose tools for molecular analysis and reasoning with minimal supervision.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.