ArticleFrontiers in artificial intelligence2024
Enzyme catalytic efficiency prediction: employing convolutional neural networks and XGBoost.
Article in Frontiers in artificial intelligence, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- From machine learning to multimodal models: The AI revolution in enzyme engineering.Biodesign research · 2026Review
- Machine learning for enzyme catalytic activity: current progress and future horizons.Briefings in bioinformatics · 2026Review
- AI-Driven Enzyme Engineering: Emerging Models and Next-Generation Biotechnological Applications.Molecules (Basel, Switzerland) · 2025Review
- Temperature adaptation in structure and function in lactate dehydrogenase-A reflects convergent evolution in a few key protein regions.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- A Three-Module Machine Learning Framework for Protein Sequence- and Temperature-DependentACS synthetic biology · 2025Article
- IECata: interpretable bilinear attention network and evidential deep learning improve the catalytic efficiency prediction of enzymes.Briefings in bioinformatics · 2025Article
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1 author.
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Abstract
Introduction: In the intricate realm of enzymology, the precise quantification of enzyme efficiency, epitomized by the turnover number ( Methods: In this context, we introduce "enzyme catalytic efficiency prediction (ECEP)," leveraging advanced deep learning techniques to enhance the previous implementation, TurNuP, for predicting the enzyme catalase Results: Preliminary assessments, compared against established models like TurNuP and DLKcat, underscore the superior predictive capabilities of ECEP, marking a pivotal shift Discussion: This improvement underscores the model's potential to enhance the field of bioinformatics, setting a new benchmark for performance.
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