Artificial intelligence and automation in enzyme engineering: evolution, advances, and future perspectives

Abstract

Natural enzymes often fail to meet industrial demands for catalytic efficiency, stability, and substrate specificity, creating a critical bottleneck in biomanufacturing. This review examines how artificial intelligence (AI) and automation are reshaping enzyme engineering from empirical trial‑and‑error toward data-driven, closed-loop design. We trace AI development from feature-engineered machine learning to supervised deep learning and self-supervised protein language models, and automation from standalone task execution to cascade integration and biofoundry-enabled build-test workflows. Their convergence is analyzed through a stage-based autonomy framework, highlighting the transition from semi-automated workflows to conditional and high-autonomy DBTL systems. Recent studies demonstrate that AI-guided prediction, automated experimentation, and active learning can accelerate enzyme optimization; however, key barriers remain, including biased datasets, limited out-of-distribution generalization, weak mechanistic interpretability, automation interoperability constraints, and unresolved multi-objective trade-offs. We discuss future directions involving FAIR-compliant data infrastructure, hybrid sequence-structure-physics models, modular automation platforms, and autonomous closed-loop systems. By integrating historical evolution, representative case studies, success and failure analysis, and practical bottlenecks, this review provides a roadmap for advancing AI-guided and autonomous enzyme engineering.

Keywords

Enzyme engineering / Artificial intelligence / Automation technologies / Protein language models / Biofoundry / Design-build-test-learn (DBTL) cycle

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Funding

Tianjin Municipal Science and Technology Program(25ZXWCSY00130)

National key Research and Development Program of International Cooperation Project(2023YFE0108100)

National Natural Science Foundation of China(32270135)

Key Project of the Tianjin Natural Science Foundation(24JCZDJC00620)

RIGHTS & PERMISSIONS

The Author(s)