Generative Artificial Intelligence for Function-Driven De Novo Enzyme Design

Abstract

The de novo design of artificial enzymes with customized catalytic functions represents a long-standing challenge in synthetic biology. Recent breakthroughs in deep learning, particularly the rise of Generative Artificial Intelligence (GAI), have transformed enzyme design from structure-centric strategies toward function-oriented paradigms. This review outlines the emerging computational frameworks that now span the entire design pipeline, including active site design, backbone generation, inverse folding, and virtual screening. Detailed description of active site, called a theozyme, is designed to stabilize transition states and can be guided by density functional theory (DFT) calculations that define the geometry of key catalytic components. Guided by the theozyme, GAI approaches such as diffusion and flow-matching models enable the generation of protein backbones pre-configured for catalysis. Inverse folding methods, exemplified by ProteinMPNN and LigandMPNN, further incorporate atomic-level constraints to optimize sequence-function compatibility. To assess and optimize catalytic performance, virtual screening platforms such as PLACER allow evaluation of protein-ligand conformational dynamics under catalytically relevant conditions. Through representative case studies, we illustrate how GAI-driven frameworks facilitate the rational creation of artificial enzymes with architectures distinct from natural homologs, thereby enabling catalytic activities not observed in nature. With the rapid progress and widespread adoption of GAI, we anticipate that de novo enzyme design with customized catalytic functions will soon evolve into a mature and broadly applicable methodology.

Keywords

De novo enzyme design / Generative artificial intelligence / Backbone design / Inverse folding

Author Contributions

Conceptualization, X.Q. and H.M.; Writing—Original Draft Preparation, X.Q. and D.W.; Writing—Review & Editing, X.Q., Z.S., X.L. and H.M.; Supervision, X.L. and H.M.; Funding Acquisition, X.L. and H.M.

Ethics Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

No data was used for the research described in the article.

Funding

This research was financially supported by “The Strategic Priority Research Program of the Chinese Academy of Sciences [XDC0110200] and National Natural Science Foundation of China [12326611]” and “The APC was funded by The Strategic Priority Research Program of the Chinese Academy of Sciences [XDC0110200]”.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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