{"title":"Enzyme functional classification using artificial intelligence.","authors":"Ha Rim Kim, Hongkeun Ji, Gi Bae Kim, Sang Yup Lee","doi":"10.1016/j.tibtech.2025.03.003","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":23324,"journal":{"name":"Trends in biotechnology","volume":" ","pages":""},"PeriodicalIF":14.3000,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Trends in biotechnology","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1016/j.tibtech.2025.03.003","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOTECHNOLOGY & APPLIED MICROBIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
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.
期刊介绍:
Trends in Biotechnology publishes reviews and perspectives on the applied biological sciences, focusing on useful science applied to, derived from, or inspired by living systems.
The major themes that TIBTECH is interested in include:
Bioprocessing (biochemical engineering, applied enzymology, industrial biotechnology, biofuels, metabolic engineering)
Omics (genome editing, single-cell technologies, bioinformatics, synthetic biology)
Materials and devices (bionanotechnology, biomaterials, diagnostics/imaging/detection, soft robotics, biosensors/bioelectronics)
Therapeutics (biofabrication, stem cells, tissue engineering and regenerative medicine, antibodies and other protein drugs, drug delivery)
Agroenvironment (environmental engineering, bioremediation, genetically modified crops, sustainable development).