Predictive Modeling of Multi-Label Enzyme Substrate using Machine Learning

Enzymes play a critical role in catalyzing biochemical reactions, and accurately predicting their substrate specificity is essential for applications in drug discovery, metabolic engineering, and biotechnology. However, traditional experimental methods are time-consuming and resource-intensive, while existing computational approaches often rely on predicting Enzyme Commission (EC) numbers as coarse proxies for substrate specificity, failing to capture the many-to-many relationships between enzymes and their substrates. In this study, we present a hybrid deep learning framework for prediction of enzyme-substrate interactions. The proposed model integrates ProtT5 protein language model embeddings with extended connectivity fingerprints (ECFP4) and 3D molecular descriptors through a dedicated geometric attention mechanism that dynamically weights features based on interaction patterns. The key findings demonstrate that proposed approach achieves state-of-the-art performance, with an overall accuracy of 88.2% and F1-score of 0.823 on the BRENDA benchmark dataset. The model shows particular strength in predicting interactions for promiscuous enzymes (18.7% of the dataset), improving recall by 22% compared to EC-based prediction methods. The hybrid attention mechanism provides a 9.2% AUC improvement over static feature concatenation. The proposed methodology can be extended to other multi-label classification problems in biological systems, paving the way for future advancements in computational biology.