Classifying Enzymatic Functions Using Graph Neural Networks: Insights from the ENZYMES Dataset

This study explores the use of Graph Neural Networks (GNNs) to classify enzyme functions using the ENZYMES dataset, which represents proteins and their interactions as graphs. The literature review highlights the evolution and efficacy of Graph Neural Networks (GNNs) in capturing complex relational data in graph structures, revolutionizing fields such as bioinformatics by facilitating advanced enzyme classification and potential drug discovery through enhanced analysis of biological interactions. The methodology uses the ENZYMES dataset from the TUDataset, which encodes protein structures into graphs for enzyme function classification. A structured Graph Neural Network was utilized with layers designed to capture complex graph hierarchies, employing preprocessing steps, advanced graph convolution techniques, and rigorous training procedures to ensure the model’s effectiveness and reproducibility in predicting enzyme functions. The study demonstrates that the Graph Neural Network model effectively learned and generalized enzyme function classification, as evidenced by consistent improvements in training performance and strong testing results. The model’s robustness was further validated through detailed analyses such as confusion matrices, ROC curves, and precision-recall metrics, which also identified specific areas for future improvement in feature engineering and training approaches to enhance accuracy and model interpretability.