ReviewNational science review2023
Harnessing generative AI to decode enzyme catalysis and evolution for enhanced engineering.
Review in National science review, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 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
25 citing papers in PubMed.
- Evaluating Mechanical-Embedding ML/MM for Predicting Mutation Effects in Chorismate Mutase Catalysis.Journal of chemical theory and computation · 2026Article
- An enzyme-specific protein language model for catalytic property prediction.Nature communications · 2026Article
- Bridging Algorithms and Biocatalysis: Perspectives on AI-Supported Enzyme Engineering.Molecules (Basel, Switzerland) · 2026Review
- Mind the gap: An embedding guide to safely travel in sequence space.PLoS computational biology · 2026Article
- Chemical neighborhood exploration for substrate discovery in biocatalysis.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- How far can you go? Extrapolating values of catalytic activity from known protein landscapes in natural and directed evolution.Chemical Society reviews · 2026Review
- Generative Artificial Intelligence-Empowered Virtual Evolution of Enzyme with the VERnet Model.ACS catalysis · 2026Article
- Sustainable bioenergy manufacturing in plants.Plant communications · 2026Review
- Green bioconversion of insoluble chitin: chitinase development pathways via multi-strategy synergy.Bioresources and bioprocessing · 2026Review
- Deep learning guided design of protease substrates.Nature communications · 2026Article
- AI-Driven Enzyme Engineering: Emerging Models and Next-Generation Biotechnological Applications.Molecules (Basel, Switzerland) · 2025Review
- Approaches for regulating enzyme activities: Recent advances in experiment and computation.Current opinion in structural biology · 2025Review
- The Action of Plastic Degrading Enzyme Is Accelerated Mainly Due to an Increase in Thermal Stability Rather Than by an Inherent Catalytic Effect.Journal of the American Chemical Society · 2025Article
- A hybrid variational autoencoder and WGAN with gradient penalty for tertiary protein structure generation.Scientific reports · 2025Article
- Artificial intelligence driven innovations in biochemistry: A review of emerging research frontiers.Biomolecules & biomedicine · 2025Review
- Biochemical and Computational Characterization of Haloalkane Dehalogenase Variants Designed by Generative AI: Accelerating the SJournal of the American Chemical Society · 2025Article
- Hierarchical metabolic engineering for rewiring cellular metabolism.FEMS microbiology reviews · 2025Review
- Smart estimation of protective antioxidant enzymes' activity in savory (Satureja rechingeri L.) under drought stress and soil amendments.BMC plant biology · 2025Article
- Bioengineered therapeutic systems for improving antitumor immunity.National science review · 2025Review
- A survey on multimodal large language models.National science review · 2024Review
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Enzymes, as paramount protein catalysts, occupy a central role in fostering remarkable progress across numerous fields. However, the intricacy of sequence-function relationships continues to obscure our grasp of enzyme behaviors and curtails our capabilities in rational enzyme engineering. Generative artificial intelligence (AI), known for its proficiency in handling intricate data distributions, holds the potential to offer novel perspectives in enzyme research. Generative models could discern elusive patterns within the vast sequence space and uncover new functional enzyme sequences. This review highlights the recent advancements in employing generative AI for enzyme sequence analysis. We delve into the impact of generative AI in predicting mutation effects on enzyme fitness, catalytic activity and stability, rationalizing the laboratory evolution of
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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.