Advances in Machine Learning Models for Predicting Enzyme Kinetic Parameters.

ABSTRACT: Enzyme kinetic parameters, including k_cat, K_m, k_cat/K_m, and K_i, are critical for guiding applications in enzyme engineering, metabolic modeling, and synthetic biology by providing quantitative information on enzyme activity under various conditions. Experimental determination of these parameters is often costly and time-consuming. Moreover, traditional computational methods are not well-suited to estimating these parameters. This motivated the development of machine learning (ML) models for in silico predictions. Here, we review recent advances in ML-based prediction of enzyme kinetic parameters by highlighting global models trained on diverse enzyme classes and local models catered toward spec

SUBMITTER: Malli A 

PROVIDER: S-EPMC12801326 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature