Harnessing Machine Learning for Enzyme Enantioselectivity: Toward Intelligent Biocatalysis Engineering
TL;DR
This review systematically evaluates how machine learning integrates multimodal datasets, including sequence, structural, and functional performance data, to advance the discovery and engineering of naturally enantioselective enzymes, and enable the de novo design of artificial enzymes for reaction-relevant applications.
Abstract
Enzyme enantioselectivity remains a central challenge in the biocatalytic synthesis of chiral pharmaceuticals and fine chemicals. Conventional evolution methods, constrained by prolonged experimental cycles and computationally intensive processes, struggle to comprehensively explore complex protein sequences. Although machine learning (ML) has been successfully applied to optimize enzymatic properties, such as catalytic efficiency and stability, its potential in enantioselectivity engineering remains underexploited. This review systematically evaluates how machine learning integrates multimodal datasets, including sequence, structural, and functional performance data, to advance the discovery and engineering of naturally enantioselective enzymes, and enable the de novo design of artificial enzymes for reaction-relevant applications. Furthermore, key algorithmic frameworks are reviewed. Persistent challenges such as data heterogeneity and limited model generalization capabilities are also critically examined. Moreover, machine learning is increasingly expected to bridge molecular-level enantioselective design with pathway optimization and reactor-scale process engineering, enabling a more integrated and efficient chiral biomanufacturing pipeline. Overall, these advancements open new avenues for efficient chiral biosynthesis and provide theoretical foundations and technical blueprints for a paradigm shift toward intelligence-driven biocatalysis engineering.