ReviewACS central science2024
Opportunities and Challenges for Machine Learning-Assisted Enzyme Engineering.
Review in ACS central science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 83 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.
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Who cites it
83 citing papers in PubMed.
- Deep Learning-driven synergistic engineering of PET hydrolase for post-consumer PET depolymerization.Synthetic and systems biotechnology · 2027Article
- Machine learning-assisted directed evolution of plant Rubisco.Science advances · 2026Article
- Alkaliphilic microbial cellulases: sources, structural insights, and bioengineering approaches for industrial applications.Archives of microbiology · 2026Review
- Next-Generation Manufacturing: The Evolving Role of Biocatalysis in AstraZeneca.Chembiochem : a European journal of chemical biology · 2026Review
- Enhancing prediction accuracy for enzyme activity engineering through sequence co-evolution and epistatic relationship modeling.Nucleic acids research · 2026Article
- Upcycling Vegetable Waste Into Functional Food Ingredients via Synergistic Microbial Engineering and Artificial Intelligence.Comprehensive reviews in food science and food safety · 2026Review
- Decoding the distribution, structure-function-redox potential relationship and recent advances in fungal laccases: a systematic approach.Bioprocess and biosystems engineering · 2026Review
- Enhancing Enzyme Activity With Mutation Combinations Guided by Few-Shot Learning and Causal Inference.Angewandte Chemie (International ed. in English) · 2026Article
- Data-driven construction of an imine reductase library capable of broad-scope reductive amination at equimolar substrate concentrations.Nature communications · 2026Article
- Rhobot-Screen: an integrated robotic platform for functional screening of rhodopsin variants.BMC biology · 2026Article
- Applications and limitations of AI tools in enzyme design.Protein science : a publication of the Protein Society · 2026Review
- Semiartificial COChemical reviews · 2026Review
- An enzyme-specific protein language model for catalytic property prediction.Nature communications · 2026Article
- Artificial intelligence and automation in enzyme engineering: evolution, advances, and future perspectives.Bioresources and bioprocessing · 2026Review
- 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
- Fast analysis and engineering of protein function by microbe-independent deep assembly and screening.Molecular systems biology · 2026Article
- Artificial intelligence driven protein design and sustainable nanomedicine for advanced theranostics.Bioactive materials · 2026Review
- Integrating Protein Language Models with Multimodal Embeddings to Accelerate Function Prediction of Uncharacterized Proteins.International journal of molecular sciences · 2026Review
- Boosting Computational Catalysis and Chemical Reactivity with Artificial Intelligence.Journal of the American Chemical Society · 2026Review
23 more citing papers are in PubMed but not listed here.
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
No grant is acknowledged in the PubMed record.
Abstract
Enzymes can be engineered at the level of their amino acid sequences to optimize key properties such as expression, stability, substrate range, and catalytic efficiency-or even to unlock new catalytic activities not found in nature. Because the search space of possible proteins is vast, enzyme engineering usually involves discovering an enzyme starting point that has some level of the desired activity followed by directed evolution to improve its "fitness" for a desired application. Recently, machine learning (ML) has emerged as a powerful tool to complement this empirical process. ML models can contribute to (1) starting point discovery by functional annotation of known protein sequences or generating novel protein sequences with desired functions and (2) navigating protein fitness landscapes for fitness optimization by learning mappings between protein sequences and their associated fitness values. In this Outlook, we explain how ML complements enzyme engineering and discuss its future potential to unlock improved engineering outcomes.
Identifiers
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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.