Machine Learning-Driven Enzyme Mining: Opportunities, Challenges, and Future Perspectives
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arXiv
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| Format: | Preprint |
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2025
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| author | Zhang, Yanzi Moorhoff, Felix Qiu, Sizhe Dong, Wenjuan Medina-Ortiz, David Zhao, Jing Davari, Mehdi D. |
| author_facet | Zhang, Yanzi Moorhoff, Felix Qiu, Sizhe Dong, Wenjuan Medina-Ortiz, David Zhao, Jing Davari, Mehdi D. |
| contents | Enzyme mining is rapidly evolving as a data-driven strategy to identify biocatalysts with tailored functions from the vast landscape of uncharacterized proteins. The integration of machine learning into these workflows enables high-throughput prediction of enzyme functions, including Enzyme Commission numbers, Gene Ontology terms, substrate specificity, and key catalytic properties such as kinetic parameters, optimal temperature, pH, solubility, and thermophilicity. This review provides a systematic overview of state-of-the-art machine learning models and highlights representative case studies that demonstrate their effectiveness in accelerating enzyme discovery.
Despite notable progress, current approaches remain limited by data scarcity, model generalizability, and interpretability. We discuss emerging strategies to overcome these challenges, including multi-task learning, integration of multi-modal data, and explainable AI. Together, these developments establish ML-guided enzyme mining as a scalable and predictive framework for uncovering novel biocatalysts, with broad applications in biocatalysis, biotechnology, and synthetic biology. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_07666 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Machine Learning-Driven Enzyme Mining: Opportunities, Challenges, and Future Perspectives Zhang, Yanzi Moorhoff, Felix Qiu, Sizhe Dong, Wenjuan Medina-Ortiz, David Zhao, Jing Davari, Mehdi D. Biomolecules Enzyme mining is rapidly evolving as a data-driven strategy to identify biocatalysts with tailored functions from the vast landscape of uncharacterized proteins. The integration of machine learning into these workflows enables high-throughput prediction of enzyme functions, including Enzyme Commission numbers, Gene Ontology terms, substrate specificity, and key catalytic properties such as kinetic parameters, optimal temperature, pH, solubility, and thermophilicity. This review provides a systematic overview of state-of-the-art machine learning models and highlights representative case studies that demonstrate their effectiveness in accelerating enzyme discovery. Despite notable progress, current approaches remain limited by data scarcity, model generalizability, and interpretability. We discuss emerging strategies to overcome these challenges, including multi-task learning, integration of multi-modal data, and explainable AI. Together, these developments establish ML-guided enzyme mining as a scalable and predictive framework for uncovering novel biocatalysts, with broad applications in biocatalysis, biotechnology, and synthetic biology. |
| title | Machine Learning-Driven Enzyme Mining: Opportunities, Challenges, and Future Perspectives |
| topic | Biomolecules |
| url | https://arxiv.org/abs/2507.07666 |