ArticleNature communications2023
Turnover number predictions for kinetically uncharacterized enzymes using machine and deep learning.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 80 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
80 citing papers in PubMed.
- Katalyst: knowledge-guided semantic alignment via contrastive learning for enzyme turnover prediction.Bioinformatics (Oxford, England) · 2026Article
- Predicting Enzyme Turnover Numbers and Enabling Rational Enzyme Evolution.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Integrative machine learning approaches for enzyme kinetic parameter prediction.Briefings in bioinformatics · 2026Review
- Computational approach for identification and characterization of a glucose-tolerant thermostable β-glucosidase from marine metagenome.Molecular diversity · 2026Article
- Mathematical and Computational Models of Biochemical Reactions and Cell Signaling-From Ordinary Differential Equations to Machine Learning.International journal of molecular sciences · 2026Review
- 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
- Annotating the pangenome reveals the diversity in the genetic basis for metabolic enzymes.Science advances · 2026Article
- EnzymeTuning improves enzyme-constrained metabolic modeling and proteome abundance prediction through deep learning.Nature communications · 2026Article
- KinForm: kinetics-informed feature optimised representation models for enzyme kNPJ systems biology and applications · 2026Article
- A geometric deep learning framework for genome-wide prediction of enzyme turnover number.Genome biology · 2026Article
- 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
- COBRA-k: A powerful framework bridging constraint-based and kinetic metabolic modeling.Science advances · 2026Article
- Enhancing kCommunications biology · 2026Article
- Advances in Machine Learning Models for Predicting Enzyme Kinetic Parameters.Journal of chemical information and modeling · 2026Review
- Machine learning for enzyme catalytic activity: current progress and future horizons.Briefings in bioinformatics · 2026Review
- GotEnzymes2: expanding coverage of enzyme kinetics and thermal properties.Nucleic acids research · 2026Article
- A critical role ofPNAS nexus · 2026Article
20 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
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The turnover number k
Indexed as
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.