Of Revolutions and Roadblocks: The Emerging Role of Machine Learning in Biocatalysis.

Machine learning (ML) is rapidly turning into a key technology for biocatalysis. By learning patterns in amino acid sequences, protein structures, and functional data, ML models can help navigate complex fitness landscapes, uncover new enzymes in databases, and even design biocatalysts de novo. Along with advances in DNA synthesis and sequencing, laboratory automation, and high-throughput screening, ML is increasing the speed and efficiency of enzyme development. In this Outlook, we highlight recent applications of ML in the fields of enzyme discovery, design, and engineering, with a focus on current challenges and emerging solutions. Furthermore, we discuss barriers that impede a broader and faster adoption of ML-based workflows in the biocatalysis community. We conclude by suggesting best practices for fostering effective collaborations in this interdisciplinary field.

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Publication Details

Journal
Open Access CRIS of the University of Bern
Published
2025-10-22
DOI
https://doi.org/10.48620/92232
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.00
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article

Of Revolutions and Roadblocks: The Emerging Role of Machine Learning in Biocatalysis.

Open Access CRIS of the University of Bern
Machine Learning in Materials Science
article

Of Revolutions and Roadblocks: The Emerging Role of Machine Learning in Biocatalysis.

article en

Abstract

Machine learning (ML) is rapidly turning into a key technology for biocatalysis. By learning patterns in amino acid sequences, protein structures, and functional data, ML models can help navigate complex fitness landscapes, uncover new enzymes in databases, and even design biocatalysts de novo. Along with advances in DNA synthesis and sequencing, laboratory automation, and high-throughput screening, ML is increasing the speed and efficiency of enzyme development. In this Outlook, we highlight recent applications of ML in the fields of enzyme discovery, design, and engineering, with a focus on current challenges and emerging solutions. Furthermore, we discuss barriers that impede a broader and faster adoption of ML-based workflows in the biocatalysis community. We conclude by suggesting best practices for fostering effective collaborations in this interdisciplinary field.

Open Access CRIS of the University of Bern
Canadian Bulletin of Medical History (CA)
Industry, innovation and infrastructure
Openalex Percentile: Top 57%
Machine Learning in Materials Science
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