ArticleNature communications2020
Transfer learning enables the molecular transformer to predict regio- and stereoselective reactions on carbohydrates.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers.
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Who cites it
62 citing papers in PubMed.
- Article
- Organic Chemistry as a Catalyst for AI Innovation: Challenges, Methods, and Emerging Paradigms.Chemical reviews · 2026Review
- Assigning the stereochemistry of natural products by machine learning.Journal of cheminformatics · 2026Article
- Data augmentation in a triple transformer loop retrosynthesis model.Digital discovery · 2026Article
- An artificial intelligence-driven synthesis planning platform (PhotoCat) for photocatalysis.Communications chemistry · 2026Article
- Article
- Transfer Learning for Heterocycle Retrosynthesis.Journal of chemical information and modeling · 2025Article
- Carbohydrate Synthesis is Entering the Data-Driven Digital Era.Chemistry (Weinheim an der Bergstrasse, Germany) · 2025Review
- Challenging Reaction Prediction Models to Generalize to Novel Chemistry.ACS central science · 2025Article
- Computational tools for the prediction of site- and regioselectivity of organic reactions.Chemical science · 2025Review
- Microfluidic Nanoparticle Separation for Precision Medicine.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Artificial intelligence in drug development.Nature medicine · 2025Review
- Language models can identify enzymatic binding sites in protein sequences.Computational and structural biotechnology journal · 2024Article
- A systematic review of deep learning chemical language models in recent era.Journal of cheminformatics · 2024Review
- Transfer learning enabled transformer-based generative adversarial networks for modeling and generating terahertz channels.Communications engineering · 2024Article
- An efficient C-glycoside production platform enabled by rationally tuning the chemoselectivity of glycosyltransferases.Nature communications · 2024Article
- 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
- Application of Transformers in Cheminformatics.Journal of chemical information and modeling · 2024Review
- Fast and effective molecular property prediction with transferability map.Communications chemistry · 2024Article
- A general model for predicting enzyme functions based on enzymatic reactions.Journal of cheminformatics · 2024Article
2 more citing papers are in PubMed but not listed here.
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4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Organic synthesis methodology enables the synthesis of complex molecules and materials used in all fields of science and technology and represents a vast body of accumulated knowledge optimally suited for deep learning. While most organic reactions involve distinct functional groups and can readily be learned by deep learning models and chemists alike, regio- and stereoselective transformations are more challenging because their outcome also depends on functional group surroundings. Here, we challenge the Molecular Transformer model to predict reactions on carbohydrates where regio- and stereoselectivity are notoriously difficult to predict. We show that transfer learning of the general patent reaction model with a small set of carbohydrate reactions produces a specialized model returning predictions for carbohydrate reactions with remarkable accuracy. We validate these predictions experimentally with the synthesis of a lipid-linked oligosaccharide involving regioselective protections and stereoselective glycosylations. The transfer learning approach should be applicable to any reaction class of interest.
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