ArticleChemical science2024
Transfer learning for a foundational chemistry model.
Article in Chemical science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Computational data as the fuel for AI in chemistry.Chemical science · 2026Review
- Transfer learning for reaction optimisation: overview of its applications and limits.Chemical science · 2026Review
- Boosting Computational Catalysis and Chemical Reactivity with Artificial Intelligence.Journal of the American Chemical Society · 2026Review
- Transfer learning enables discovery of sub-micromolar antibacterials for ESKAPE pathogens from ultra-large chemical spaces.Chemical science · 2025Article
- Transfer learning from custom-tailored virtual molecular databases to real-world organic photosensitizers for catalytic activity prediction.Communications chemistry · 2025Article
- Transfer Learning-Enhanced Prediction of Glass Transition Temperature in Bismaleimide-Based Polyimides.Polymers · 2025Article
- Carbohydrate Synthesis is Entering the Data-Driven Digital Era.Chemistry (Weinheim an der Bergstrasse, Germany) · 2025Review
- Enhancing chemical reaction search through contrastive representation learning and human-in-the-loop.Journal of cheminformatics · 2025Article
- Computational tools for the prediction of site- and regioselectivity of organic reactions.Chemical science · 2025Review
- Self-Driving Laboratories for Chemistry and Materials Science.Chemical reviews · 2024Review
- Transfer learning across different chemical domains: virtual screening of organic materials with deep learning models pretrained on small molecule and chemical reaction data.Journal of cheminformatics · 2024Article
- A focus on molecular representation learning for the prediction of chemical properties.Chemical science · 2024Article
Corrections and comments
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Authors and funding
1 author.
Funding
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Abstract
Data-driven chemistry has garnered much interest concurrent with improvements in hardware and the development of new machine learning models. However, obtaining sufficiently large, accurate datasets of a desired chemical outcome for data-driven chemistry remains a challenge. The community has made significant efforts to democratize and curate available information for more facile machine learning applications, but the limiting factor is usually the laborious nature of generating large-scale data. Transfer learning has been noted in certain applications to alleviate some of the data burden, but this protocol is typically carried out on a case-by-case basis, with the transfer learning task expertly chosen to fit the finetuning. Herein, I develop a machine learning framework capable of accurate chemistry-relevant prediction amid general sources of low data. First, a chemical "foundational model" is trained using a dataset of ∼1 million experimental organic crystal structures. A task specific module is then stacked atop this foundational model and subjected to finetuning. This approach achieves state-of-the-art performance on a diverse set of tasks: toxicity prediction, yield prediction, and odor prediction.
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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.