Recent advances in AI-driven deep eutectic solvent design for biocatalysis: Co-design of extremozyme and reaction medium.

Abstract

Deep eutectic solvents (DES) have emerged as sustainable, highly tunable alternatives to conventional organic media for non-aqueous biocatalysis. DES dynamically interact with enzymes to shift reaction equilibria, enhance thermostability and tailor stereoselectivity. However, the vast combinatorial space and complex physicochemical properties of DES make traditional trial-and-error solvent engineering highly inefficient. This review systematically examines recent advancements in DES-driven biocatalysis and highlights the role of artificial intelligence (AI) for rational DES design. Specifically, it synthesizes evidence that DES composition and water content jointly shape enzyme-specific microenvironments and thereby affect catalytic performance. Extremozymes are also evaluated as candidate catalysts for DES-containing media and as sources of potentially transferable adaptation features for enzyme engineering. It then discusses how AI can support DES formation screening, prediction of physicochemical properties using quantitative structure-property relationship (QSPR) models, and candidate formulation generation at the solvent level. At the system level, the focus shifts to predicting how a specific enzyme responds to a defined formulation in terms of activity and stability. Finally, we outline a mechanistically informed, multi-objective framework that integrates DES composition, enzyme sequence and structure, and operating conditions with physicochemical calculations, AI-based prediction, and experimental validation, providing a practical route toward genuine DES-enzyme co-design.