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Enzyme functional classification using artificial intelligence.

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Title: Enzyme functional classification using artificial intelligence.
Authors: Kim, Ha Rim1,2 (AUTHOR), Ji, Hongkeun1,2 (AUTHOR), Kim, Gi Bae1,2,3 (AUTHOR), Lee, Sang Yup1,2,3,4,5 (AUTHOR) leesy@kaist.ac.kr
Source: Trends in Biotechnology. Sep2025, Vol. 43 Issue 9, p2214-2231. 18p.
Subjects: Artificial intelligence, Deep learning, Computational biology, Enzymatic analysis, Biological research methodology, Machine learning, Feature extraction
Abstract: Recent advances in computational biology, especially deep learning models, have significantly accelerated the discovery and annotation of enzyme functions. Diverse enzyme feature extraction methods and machine learning algorithms have facilitated the development of high-performing computational models for enzyme function classification. Deep neural network architectures, including convolutional neural networks, recurrent neural networks, transformers, and graph neural networks, have revolutionized data-driven approaches in predicting enzyme functions. Artificial intelligence has transformed enzyme function classification, making it more scalable and accurate than ever before. Enzymes are essential for cellular metabolism, and elucidating their functions is critical for advancing biochemical research. However, experimental methods are often time consuming and resource intensive. To address this, significant efforts have been directed toward applying artificial intelligence (AI) to enzyme function prediction, enabling high-throughput and scalable approaches. In this review, we discuss advances in AI-driven enzyme functional annotation, transitioning from traditional machine learning (ML) methods to state-of-the-art deep learning approaches. We highlight how deep learning enables models to automatically extract features from raw data without manual intervention, leading to enhanced performance. Finally, we discuss the discovery of novel enzyme functions and generation of de novo enzymes through the integration of generative AIs and bio big data as future research directions. [ABSTRACT FROM AUTHOR]
Copyright of Trends in Biotechnology is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 187647454
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PubType: Academic Journal
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  Data: Enzyme functional classification using artificial intelligence.
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  Data: <searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Computational+biology%22">Computational biology</searchLink><br /><searchLink fieldCode="DE" term="%22Enzymatic+analysis%22">Enzymatic analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Biological+research+methodology%22">Biological research methodology</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink>
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  Data: Recent advances in computational biology, especially deep learning models, have significantly accelerated the discovery and annotation of enzyme functions. Diverse enzyme feature extraction methods and machine learning algorithms have facilitated the development of high-performing computational models for enzyme function classification. Deep neural network architectures, including convolutional neural networks, recurrent neural networks, transformers, and graph neural networks, have revolutionized data-driven approaches in predicting enzyme functions. Artificial intelligence has transformed enzyme function classification, making it more scalable and accurate than ever before. Enzymes are essential for cellular metabolism, and elucidating their functions is critical for advancing biochemical research. However, experimental methods are often time consuming and resource intensive. To address this, significant efforts have been directed toward applying artificial intelligence (AI) to enzyme function prediction, enabling high-throughput and scalable approaches. In this review, we discuss advances in AI-driven enzyme functional annotation, transitioning from traditional machine learning (ML) methods to state-of-the-art deep learning approaches. We highlight how deep learning enables models to automatically extract features from raw data without manual intervention, leading to enhanced performance. Finally, we discuss the discovery of novel enzyme functions and generation of de novo enzymes through the integration of generative AIs and bio big data as future research directions. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Trends in Biotechnology is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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        Value: 10.1016/j.tibtech.2025.03.003
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      – Code: eng
        Text: English
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        Type: general
      – SubjectFull: Deep learning
        Type: general
      – SubjectFull: Computational biology
        Type: general
      – SubjectFull: Enzymatic analysis
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      – SubjectFull: Biological research methodology
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      – SubjectFull: Machine learning
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      – SubjectFull: Feature extraction
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      – TitleFull: Enzyme functional classification using artificial intelligence.
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            NameFull: Kim, Ha Rim
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            NameFull: Kim, Gi Bae
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            – D: 01
              M: 09
              Text: Sep2025
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              Y: 2025
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