ArticleNature communications2022
Biocatalysed synthesis planning using data-driven learning.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers.
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Who cites it
53 citing papers in PubMed.
- Predicting Functional Diversity Within Enzyme Families: A Practical Perspective.Chembiochem : a European journal of chemical biology · 2026Review
- Robots and Minimal, Physics-Informed Features: A Hybrid Framework for Enzyme Catalysis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Enhancing biocatalytic retrosynthesis with a graph-to-graph model.Chemical science · 2026Article
- Intelligent Plasmonic Bio-Holoelectrochemical Systems: A Testable Architecture for Light-Programmed Microbial Electrochemistry.Global challenges (Hoboken, NJ) · 2026Review
- Mechanism-informed rules tunably balance novelty and feasibility of predicted enzymatic reactions.bioRxiv : the preprint server for biology · 2026Article
- Predicting enzyme-compound associations for enzyme-catalysed reactions.Journal of cheminformatics · 2026Article
- Enzyme association for environmental biotransformation reactions through contrastive learning of reaction center-specific fingerprints.Bioinformatics (Oxford, England) · 2026Article
- EnzRetro: Enzymatic Retrosynthetic Planning With Site-Specific Reaction Edits Based on Sequence Generative Architecture.Exploration (Beijing, China) · 2026Article
- Machine Learning Prediction of Laccase-Catalyzed Oxidation of Aromatic Compounds Using Curated Enzyme-Specific Datasets.Journal of computational chemistry · 2026Article
- Enzyformer: a two-stage pretrained model for enzymatic retrosynthesis.Journal of cheminformatics · 2026Article
- Hyperspectral reporters for long-distance and wide-area detection of gene expression in living bacteria.Nature biotechnology · 2026Article
- An artificial intelligence-driven synthesis planning platform (PhotoCat) for photocatalysis.Communications chemistry · 2026Article
- EC-Bench: a benchmark for enzyme commission number prediction.Bioinformatics advances · 2026Article
- AI-Driven Enzyme Engineering: Emerging Models and Next-Generation Biotechnological Applications.Molecules (Basel, Switzerland) · 2025Review
- A virtual platform for automated hybrid organic-enzymatic synthesis planning.Nature communications · 2025Article
- Computational approaches to enzymatic reaction assignment: a review of methods, validations, and future directions.Briefings in bioinformatics · 2025Review
- From reactants to products: computational methods for biosynthetic pathway design.Synthetic and systems biotechnology · 2025Review
- Computational tools for nonnatural pathway design: Algorithms, applications, and challenges.Biodesign research · 2025Review
- Transfer Learning for Heterocycle Retrosynthesis.Journal of chemical information and modeling · 2025Article
- novoStoic2.0: An integrated framework for pathway synthesis, thermodynamic evaluation, and enzyme selection.PLoS computational biology · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Enzyme catalysts are an integral part of green chemistry strategies towards a more sustainable and resource-efficient chemical synthesis. However, the use of biocatalysed reactions in retrosynthetic planning clashes with the difficulties in predicting the enzymatic activity on unreported substrates and enzyme-specific stereo- and regioselectivity. As of now, only rule-based systems support retrosynthetic planning using biocatalysis, while initial data-driven approaches are limited to forward predictions. Here, we extend the data-driven forward reaction as well as retrosynthetic pathway prediction models based on the Molecular Transformer architecture to biocatalysis. The enzymatic knowledge is learned from an extensive data set of publicly available biochemical reactions with the aid of a new class token scheme based on the enzyme commission classification number, which captures catalysis patterns among different enzymes belonging to the same hierarchy. The forward reaction prediction model (top-1 accuracy of 49.6%), the retrosynthetic pathway (top-1 single-step round-trip accuracy of 39.6%) and the curated data set are made publicly available to facilitate the adoption of enzymatic catalysis in the design of greener chemistry processes.
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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.