ReviewProtein engineering, design & selection : PEDS2021
Machine learning for enzyme engineering, selection and design.
Review in Protein engineering, design & selection : PEDS, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed.
- Advances in Machine Learning Models for Predicting Enzyme Kinetic Parameters.Journal of chemical information and modeling · 2026Review
- AI-Driven Enzyme Engineering: Emerging Models and Next-Generation Biotechnological Applications.Molecules (Basel, Switzerland) · 2025Review
- Reaction Engineering of In Vitro Natural Product Biosynthesis: Challenges and Strategies.Chembiochem : a European journal of chemical biology · 2025Review
- Unlocking New Bioactive Peptides from Coffee Pulp: A Metagenomics and AI-Driven Discovery Paradigm.Foods (Basel, Switzerland) · 2025Article
- Cellulose and cellulose-based materials for enzyme immobilization: a review.Biophysical reviews · 2025Review
- TopEC: prediction of Enzyme Commission classes by 3D graph neural networks and localized 3D protein descriptor.Nature communications · 2025Article
- Synthetic biology strategies for sustainable bioplastic production by yeasts.Journal of microbiology (Seoul, Korea) · 2025Review
- Spiers Memorial Lecture: Engineering biocatalysts.Faraday discussions · 2024Article
- Artificial Intelligence Methods and Models for Retro-Biosynthesis: A Scoping Review.ACS synthetic biology · 2024Article
- Article
- Descriptor-augmented machine learning for enzyme-chemical interaction predictions.Synthetic and systems biotechnology · 2024Article
- Structure-aware machine learning strategies for antimicrobial peptide discovery.Scientific reports · 2024Article
- Strategies for designing biocatalysts with new functions.Chemical Society reviews · 2024Review
- Machine Learning to Predict Enzyme-Substrate Interactions in Elucidation of Synthesis Pathways: A Review.Metabolites · 2024Review
- New advances in protein engineering for industrial applications: Key takeaways.Open life sciences · 2024Review
- Enzyme catalytic efficiency prediction: employing convolutional neural networks and XGBoost.Frontiers in artificial intelligence · 2024Article
- Advances in ligand-specific biosensing for structurally similar molecules.Cell systems · 2023Review
- Harnessing generative AI to decode enzyme catalysis and evolution for enhanced engineering.National science review · 2023Review
- Enhancing luciferase activity and stability through generative modeling of natural enzyme sequences.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Harnessing Generative AI to Decode Enzyme Catalysis and Evolution for Enhanced Engineering.bioRxiv : the preprint server for biology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Machine learning is a useful computational tool for large and complex tasks such as those in the field of enzyme engineering, selection and design. In this review, we examine enzyme-related applications of machine learning. We start by comparing tools that can identify the function of an enzyme and the site responsible for that function. Then we detail methods for optimizing important experimental properties, such as the enzyme environment and enzyme reactants. We describe recent advances in enzyme systems design and enzyme design itself. Throughout we compare and contrast the data and algorithms used for these tasks to illustrate how the algorithms and data can be best used by future designers.
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Identifiers
What OpenQuestion holds
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.