Author(s)

Donghan Li ¹

Affiliation(s)

¹ Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, China

Corresponding Author

Donghan Li

ABSTRACT

Accurate computational annotation of protein function addresses a critical bottleneck in bioinformatics. This study presents an explainable machine learning framework that predicts a protein's functional class from its physicochemical profile. A Random Forest model, trained on four structural descriptors from the Protein Data Bank, classifies proteins into three major enzyme classes: Hydrolase, Oxidoreductase, and Transferase. The model achieved robust performance (test accuracy: 71.42%), with SHAP analysis identifying molecular weight as the primary discriminative feature. To ensure practical utility and reproducibility, the model is deployed as an interactive Shiny application and an open-source R package, providing a reliable and accessible tool for the community.

KEYWORDS

R language; Database; Machine learning; Bioinformatics; Shiny Web; Protein prediction

CITE THIS PAPER

Donghan Li. Interpretable Machine Learning in Enzyme Classification: A SHAP-Guided Analysis of Global Structural Features. Journal of Artificial Intelligence Practice (2026). Vol. 9, No. 1, 108-121. DOI: http://dx.doi.org/10.23977/jaip.2026.090113.

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