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.