ArticleNature communications2023
A general model to predict small molecule substrates of enzymes based on machine and deep learning.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 82 papers.
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Who cites it
82 citing papers in PubMed.
- Predicting Enzyme Turnover Numbers and Enabling Rational Enzyme Evolution.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- 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
- A Novel Unsupervised Learning Model with Bayesian Hyperparameter Optimization for Predicting Substrates of Promiscuous Enzymes.ACS omega · 2026Article
- Multiagent Data Mining for Enhanced Machine Learning Prediction of Delayed Fluorescence Materials.ACS omega · 2026Article
- Applications and limitations of AI tools in enzyme design.Protein science : a publication of the Protein Society · 2026Review
- De novo L-(+)-tartaric acid biosynthesis in multi-modular engineered yeasts.Nature communications · 2026Article
- Computational approach for identification and characterization of a glucose-tolerant thermostable β-glucosidase from marine metagenome.Molecular diversity · 2026Article
- A Review of Plant-Derived Diterpenoid Biosynthesis: From Structural Scaffold Diversity and Lineage-Associated Distribution to Enzyme Mining and Discovery Strategies.Molecules (Basel, Switzerland) · 2026Review
- EPIC: A Machine-Learning Framework for Product-Dependent Behavior in β‑Glucosidases.ACS omega · 2026Article
- Ligify 2.0: a web server for predicted small molecule biosensors.Nucleic acids research · 2026Article
- KASSPer: kinase active site structure prediction using protein and ligand language models and its application to virtual screening.Bioinformatics (Oxford, England) · 2026Article
- Accurate and task-agnostic modeling of enzymatic reactions through multimodal relational learning.Acta pharmaceutica Sinica. B · 2026Article
- Reaction-aware molecular representation learning: Toward generalizable artificial intelligence for enzymatic catalysis.Acta pharmaceutica Sinica. B · 2026Article
- Chemical neighborhood exploration for substrate discovery in biocatalysis.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Context-dependent peptide recognition shapes tyrosine kinase substrate specificity beyond consensus motifs.bioRxiv : the preprint server for biology · 2026Article
- pLM-Repeat: Exploiting the sequence representations of protein language models for sensitive repeat detection.Protein science : a publication of the Protein Society · 2026Article
- Computational glycosyltransferases masked deoxynivalenol toxicity and halted FHB spread in wheat grains.Journal of advanced research · 2026Article
- The Extreme Environment Microbiome Catalog (EEMC): a global resource for microbial diversity and antimicrobial discovery.Nature communications · 2026Article
- A survey of downstream applications of evolutionary scale modeling protein language models.Quantitative biology (Beijing, China) · 2026Review
22 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
For most proteins annotated as enzymes, it is unknown which primary and/or secondary reactions they catalyze. Experimental characterizations of potential substrates are time-consuming and costly. Machine learning predictions could provide an efficient alternative, but are hampered by a lack of information regarding enzyme non-substrates, as available training data comprises mainly positive examples. Here, we present ESP, a general machine-learning model for the prediction of enzyme-substrate pairs with an accuracy of over 91% on independent and diverse test data. ESP can be applied successfully across widely different enzymes and a broad range of metabolites included in the training data, outperforming models designed for individual, well-studied enzyme families. ESP represents enzymes through a modified transformer model, and is trained on data augmented with randomly sampled small molecules assigned as non-substrates. By facilitating easy in silico testing of potential substrates, the ESP web server may support both basic and applied science.
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