A General Transformer-Based Multi-Task Learning Framework for Predicting Interaction Types between Enzyme and Small Molecule

Predicting enzyme-small molecule interactions is critical for drug discovery and generally understanding the biochemical processes of life. While recent deep learning approaches have shown promising results, several challenges remain: the lack of a comprehensive training dataset, architectures lacking communication between representations of enzymes and small molecules, the tendency to simplify the problem as enzyme-substrate vs. enzyme--non-interacting, and thereby, misclassify enzyme-inhibitor pairs as substrates, and the negligence of the true impact of data leakage on the models performance. To address these issues, we present EMMA (Enzyme-small Molecule interaction Multi-head Attention), a transformer-based multi-task learning framework designed to learn pairwise interaction signals between enzymes and small molecules, that can well generalize to out-of-distribution data. EMMA operates directly on the SMILES string representation of small molecules and enzyme sequences, with two classification heads that distinguish enzyme--non-interacting, enzyme--substrate, and enzyme--inhibitor pairs. By evaluating EMMA under five distinct data-splitting regimes that control for different types of data leakage, we demonstrate that EMMA achieves a strong and robust performance, particularly for previously unseen combinations of enzymes and small molecules. Further, a deeper analysis highlights that the topological properties of the enzyme--small molecule interaction network are crucial for the model performance and its ability to generalize, yet again stressing the decisive role of well-designed training datasets for successful model training.