ArticlePLoS computational biology2022
Machine learning modeling of family wide enzyme-substrate specificity screens.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
51 citing papers in PubMed, 108 citations in OpenAlex.
- 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
- A Novel Unsupervised Learning Model with Bayesian Hyperparameter Optimization for Predicting Substrates of Promiscuous Enzymes.ACS omega · 2026Article
- Discovery of a phenazine-thiol conjugase from sparse data using genome-informed machine learning.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- EPIC: A Machine-Learning Framework for Product-Dependent Behavior in β‑Glucosidases.ACS omega · 2026Article
- A machine learning-based workflow for transaminase selection.Chemical science · 2026Article
- Accurate and task-agnostic modeling of enzymatic reactions through multimodal relational learning.Acta pharmaceutica Sinica. B · 2026Article
- Discovery of a phenazine-thiol conjugase from sparse data using genome-informed machine learning.bioRxiv : the preprint server for biology · 2026Article
- Machine learning for enzyme catalytic activity: current progress and future horizons.Briefings in bioinformatics · 2026Review
- Enzyme Engineering Database (EnzEngDB): a platform for sharing and interpreting sequence-function relationships across protein engineering campaigns.Nucleic acids research · 2026Article
- Unlocking Enzyme Discovery: Leveraging Multi-Omics, Machine Learning, and De Novo Design.Methods in molecular biology (Clifton, N.J.) · 2026Article
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- Computational tools for nonnatural pathway design: Algorithms, applications, and challenges.Biodesign research · 2025Review
- CatPred: a comprehensive framework for deep learning in vitro enzyme kinetic parameters.Nature communications · 2025Article
- CPI-Pred: A deep learning framework for predicting functional parameters of compound-protein interactions.bioRxiv : the preprint server for biology · 2025Article
- Self-supervised domain adaptation of protein language model based solely on positive enzyme-reaction pairs.Computational and structural biotechnology journal · 2025Article
- AI-driven parametrization of Michaelis-Menten maximal velocity: Advancing in silico new approach methodologies (NAMs).NAM journal · 2025Article
- Improving generalizability of drug-target binding prediction by pre-trained multi-view molecular representations.Bioinformatics (Oxford, England) · 2024Article
- Discovery of alkaline laccases from basidiomycete fungi through machine learning-based approach.Biotechnology for biofuels and bioproducts · 2024Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
Biocatalysis is a promising approach to sustainably synthesize pharmaceuticals, complex natural products, and commodity chemicals at scale. However, the adoption of biocatalysis is limited by our ability to select enzymes that will catalyze their natural chemical transformation on non-natural substrates. While machine learning and in silico directed evolution are well-posed for this predictive modeling challenge, efforts to date have primarily aimed to increase activity against a single known substrate, rather than to identify enzymes capable of acting on new substrates of interest. To address this need, we curate 6 different high-quality enzyme family screens from the literature that each measure multiple enzymes against multiple substrates. We compare machine learning-based compound-protein interaction (CPI) modeling approaches from the literature used for predicting drug-target interactions. Surprisingly, comparing these interaction-based models against collections of independent (single task) enzyme-only or substrate-only models reveals that current CPI approaches are incapable of learning interactions between compounds and proteins in the current family level data regime. We further validate this observation by demonstrating that our no-interaction baseline can outperform CPI-based models from the literature used to guide the discovery of kinase inhibitors. Given the high performance of non-interaction based models, we introduce a new structure-based strategy for pooling residue representations across a protein sequence. Altogether, this work motivates a principled path forward in order to build and evaluate meaningful predictive models for biocatalysis and other drug discovery applications.
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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.