Taco-DDI: accurate prediction of drug-drug interaction events using graph transformer-based architecture and dynamic co-attention matrices.

Drug-drug interactions (DDIs) are critical in pharmaceutical research, as adverse interactions can pose significant risks for patient treatment plans. Accurate prediction of DDI events risk levels can provide valuable guidance for designing safer and more effective medical regimens. However, existing approaches often focus on interaction networks while overlooking the inherent molecular properties of drugs. In this study, we present Taco-DDI, a novel drug representation learning framework that utilizes a graph transformer-based model combined with a dynamic co-attention mechanism. Taco-DDI leverages the transformer architecture to derive atom-level feature encodings, capturing comprehensive molecular representations. Furthermore, it employs an adaptive co-attention matrix to identify essential substructures in drug molecules solely from structural information. Our results demonstrate that Taco-DDI achieves a 6.59 % relative accuracy improvement in DDI events risk levels prediction. Additionally, interpretability analysis confirms that Taco-DDI provides meaningful insights into DDI mechanisms, highlighting its practical utility as a robust tool for identifying DDI events risk levels.