ArticleChemical science2021
Predicting enzymatic reactions with a molecular transformer.
Article in Chemical science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- Enhancing biocatalytic retrosynthesis with a graph-to-graph model.Chemical science · 2026Article
- Mechanism-informed rules tunably balance novelty and feasibility of predicted enzymatic reactions.bioRxiv : the preprint server for biology · 2026Article
- Assigning the stereochemistry of natural products by machine learning.Journal of cheminformatics · 2026Article
- EnzRetro: Enzymatic Retrosynthetic Planning With Site-Specific Reaction Edits Based on Sequence Generative Architecture.Exploration (Beijing, China) · 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
- Transfer Learning for Heterocycle Retrosynthesis.Journal of chemical information and modeling · 2025Article
- A universal foundation model for transfer learning in molecular crystals.Chemical science · 2025Article
- LICEDB: light industrial core enzyme database for industrial applications and AI enzyme design.Database : the journal of biological databases and curation · 2025Article
- Deep learning in template-free de novo biosynthetic pathway design of natural products.Briefings in bioinformatics · 2024Review
- Artificial Intelligence Methods and Models for Retro-Biosynthesis: A Scoping Review.ACS synthetic biology · 2024Article
- Opportunities and Challenges for Machine Learning-Assisted Enzyme Engineering.ACS central science · 2024Review
- SynCluster: Reaction Type Clustering and Recommendation Framework for Synthesis Planning.JACS Au · 2023Article
- An explainability framework for deep learning on chemical reactions exemplified by enzyme-catalysed reaction classification.Journal of cheminformatics · 2023Article
- Fast Customization of Chemical Language Models to Out-of-Distribution Data Sets.Chemistry of materials : a publication of the American Chemical Society · 2023Article
- Applications of artificial intelligence and machine learning in dynamic pathway engineering.Biochemical Society transactions · 2023Review
- Multistep retrosynthesis combining a disconnection aware triple transformer loop with a route penalty score guided tree search.Chemical science · 2023Article
- Open-Source Machine Learning in Computational Chemistry.Journal of chemical information and modeling · 2023Review
- Unbiasing Retrosynthesis Language Models with Disconnection Prompts.ACS central science · 2023Article
- Machine Learning Strategies for Reaction Development: Toward the Low-Data Limit.Journal of chemical information and modeling · 2023Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The use of enzymes for organic synthesis allows for simplified, more economical and selective synthetic routes not accessible to conventional reagents. However, predicting whether a particular molecule might undergo a specific enzyme transformation is very difficult. Here we used multi-task transfer learning to train the molecular transformer, a sequence-to-sequence machine learning model, with one million reactions from the US Patent Office (USPTO) database combined with 32 181 enzymatic transformations annotated with a text description of the enzyme. The resulting enzymatic transformer model predicts the structure and stereochemistry of enzyme-catalyzed reaction products with remarkable accuracy. One of the key novelties is that we combined the reaction SMILES language of only 405 atomic tokens with thousands of human language tokens describing the enzymes, such that our enzymatic transformer not only learned to interpret SMILES, but also the natural language as used by human experts to describe enzymes and their mutations.
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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.