ArticleNature communications2025
CatPred: a comprehensive framework for deep learning in vitro enzyme kinetic parameters.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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Who cites it
44 citing papers in PubMed.
- Deep Learning-driven synergistic engineering of PET hydrolase for post-consumer PET depolymerization.Synthetic and systems biotechnology · 2027Article
- Predicting Enzyme Turnover Numbers and Enabling Rational Enzyme Evolution.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- High-throughput physics-based enzyme engineering.bioRxiv : the preprint server for biology · 2026Article
- Upcycling Vegetable Waste Into Functional Food Ingredients via Synergistic Microbial Engineering and Artificial Intelligence.Comprehensive reviews in food science and food safety · 2026Review
- Integrative machine learning approaches for enzyme kinetic parameter prediction.Briefings in bioinformatics · 2026Review
- De novo L-(+)-tartaric acid biosynthesis in multi-modular engineered yeasts.Nature communications · 2026Article
- Metal binding site alignment enables network-driven discovery of recurrent geometries across sequence-divergent proteins and drug off-targets.PLoS computational biology · 2026Article
- EPIC: A Machine-Learning Framework for Product-Dependent Behavior in β‑Glucosidases.ACS omega · 2026Article
- AUKAT: Conditional VAE-Driven Augmentation and Neural Modeling of Enzyme Turnover Numbers.Biomolecules · 2026Article
- An enzyme-specific protein language model for catalytic property prediction.Nature communications · 2026Article
- How far can you go? Extrapolating values of catalytic activity from known protein landscapes in natural and directed evolution.Chemical Society reviews · 2026Review
- On why cancer cells require a great amount of glucose.Quantitative biology (Beijing, China) · 2026Article
- Machine learning based approaches for structure activity relationship analysis of heparanase inhibitors.Scientific reports · 2026Article
- EnzymeTuning improves enzyme-constrained metabolic modeling and proteome abundance prediction through deep learning.Nature communications · 2026Article
- The potential of metabolic engineering for sustainable phytosterol production.Advanced biotechnology · 2026Review
- Lignocellulose-mediated selection of potential halophilic PET-degrading enzymes from mangrove soil.Nature communications · 2026Article
- Integrating biocatalysis with continuous flow: current status, challenges, and future perspectives.Journal of advanced research · 2026Review
- UniKineG: Unified-Coordinate Geometric Graphs Enable Robust Enzyme Kinetic Prediction.International journal of molecular sciences · 2026Article
- Enzyme Catalytic Parameters and Evolution Across the Dissipation Plane.International journal of molecular sciences · 2026Review
- Flux sampling and context-specific genome-scale metabolic models for biotechnological applications.Trends in biotechnology · 2026Review
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Estimation of enzymatic activities still heavily relies on experimental assays, which can be cost and time-intensive. We present CatPred, a deep learning framework for predicting in vitro enzyme kinetic parameters, including turnover numbers (k
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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.