ReviewACS catalysis2023
Machine Learning-Guided Protein Engineering.
Review in ACS catalysis, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
66 citing papers in PubMed, 170 citations in OpenAlex.
- 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
- Bacterial Laccase: Recent Advances in Production, Engineering Strategies, and Diagnostic Applications.Biotechnology and bioengineering · 2026Review
- Next-Generation Manufacturing: The Evolving Role of Biocatalysis in AstraZeneca.Chembiochem : a European journal of chemical biology · 2026Review
- Data-driven construction of an imine reductase library capable of broad-scope reductive amination at equimolar substrate concentrations.Nature communications · 2026Article
- Rapid screening of staphylokinase protein variants using an unpurified cell-free expression system.FEBS open bio · 2026Article
- Molecular Adaptations of Thermophilic Proteins-From Mechanistic Understanding to Machine Learning Approaches.ChemistryOpen · 2026Review
- Therapeutic peptides and proteins: Status and developments in drug delivery.Journal of controlled release : official journal of the Controlled Release Society · 2026Review
- Artificial intelligence driven protein design and sustainable nanomedicine for advanced theranostics.Bioactive materials · 2026Review
- SynFit: Synergistic Contrastive Learning for Multi-Objective Protein Fitness Prediction and Optimization.bioRxiv : the preprint server for biology · 2026Article
- Directed evolution of enzymes at the crossroads of tradition and innovation.FEBS open bio · 2026Review
- Exploring the limits of pre-trained embeddings in machine-guided protein design: a case study on predicting AAV vector viability.Scientific reports · 2026Article
- Advances in Machine Learning Models for Predicting Enzyme Kinetic Parameters.Journal of chemical information and modeling · 2026Review
- Examining selection dynamics and limitations in multi-round protein selection of high diversity libraries.Protein engineering, design & selection : PEDS · 2026Article
- Optogenetic engineering of synthetic and natural receptors: design principles, functional mechanisms and biomedical applications.Regenerative biomaterials · 2026Review
- Productive chaos and precision engineering: decoupling discovery from manufacturing to revolutionize plant-inspired therapeutics.Frontiers in plant science · 2026Article
- Biocatalytic reduction of nitro-sulfonamides: enzyme engineering, process integration, and sustainable pathways toFrontiers in bioengineering and biotechnology · 2026Review
- Architectural good practices for reproducible benchmarking in protein machine learning.Frontiers in bioinformatics · 2026Article
- Family-Specialized Transformer for L-cystathionine gamma-lyase Engineering and Its Structural Interpretation.Computational and structural biotechnology journal · 2026Article
- AI-Driven Enzyme Engineering: Emerging Models and Next-Generation Biotechnological Applications.Molecules (Basel, Switzerland) · 2025Review
6 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
10 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent progress in engineering highly promising biocatalysts has increasingly involved machine learning methods. These methods leverage existing experimental and simulation data to aid in the discovery and annotation of promising enzymes, as well as in suggesting beneficial mutations for improving known targets. The field of machine learning for protein engineering is gathering steam, driven by recent success stories and notable progress in other areas. It already encompasses ambitious tasks such as understanding and predicting protein structure and function, catalytic efficiency, enantioselectivity, protein dynamics, stability, solubility, aggregation, and more. Nonetheless, the field is still evolving, with many challenges to overcome and questions to address. In this Perspective, we provide an overview of ongoing trends in this domain, highlight recent case studies, and examine the current limitations of machine learning-based methods. We emphasize the crucial importance of thorough experimental validation of emerging models before their use for rational protein design. We present our opinions on the fundamental problems and outline the potential directions for future research.
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.