Work

Deep learning and generative artificial intelligence methods in enzyme and cell engineering

Year: 2025

Type: review

Abstract: Efficient enzymes and microbial factories are essential to promote the transition toward a sustainable bioeconomy. This review focuses on the progress of artificial intelligence (AI) methods in accelerating the development of optimized biocatalysts and genetic networks in cells. Recent advances in AI in the field of enzyme discovery, engineering, and de novo design are discussed. Additionally, we highlight examples of successful applications of AI in optimizing different components in cells, from gene expression regulation to metabolic pathway optimization and design. Finally, this review emphasizes the challenges limiting the reliability and generalizability of current AI methods.

Source: Current Opinion in Biotechnology

DOI: 10.1016/j.copbio.2025.103393

Authors: Steffen Docter, Benoit David, Holger Gohlke

Institutions: Forschungszentrum Jülich, Heinrich Heine University Düsseldorf

Language: English

Topic: Microbial Metabolic Engineering and Bioproduction

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: hybrid

Estimated APC paid: $4,530