A systematic review of EC number prediction algorithms based on reaction chemical structures

MOTIVATION: Enzymes catalyse most of the metabolic reactions in living organisms. The functional characterization of enzymes is critical to understand the metabolic processes involved in different fields such as biotechnology, nutrition, ecology, and drug development. The Enzyme Commission (EC) numbers provide a hierarchical framework for classifying enzymatic reactions, linking gene and protein sequences to biochemical transformations. However, the incomplete annotation of biochemical reaction databases limits the accurate representation of metabolism of living organisms. RESULTS: To address this gap, several computational methods have been developed to predict EC numbers from reaction chemical structures, broadly classified into reaction similarity-based and machine/deep learning approaches. We systematically review these tools, comparing their performance predicting EC numbers from different databases, and assessing their robustness across enzyme classes. We also apply these methods to predict EC numbers from drug metabolism reactions, highlighting their potential to improve functional annotation of uncharacterized metabolic pathways. AVAILABILITY AND IMPLEMENTATION: All the necessary data and code to reproduce the results of this manuscript are available within https://github.com/PlanesLab/ECNumberPrediction GitHub repository and in Zenodo https://doi.org/10.5281/zenodo.22957403. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.