Authors
Yuxuan Hou, Jinyu Meng, Binghui Chen, Jifeng Yuan, Shizhen Wang
Published in
Biodesign research. Volume 8. Issue 3. Pages 100109. Epub Sep 10, 2026.
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.
PMID:
42834897
Bibliographic data and abstract were imported from PubMed on 06 Oct 2026.
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