Cross-Modal Method Based on Self-Attention Neural Networks for Drug-Target Prediction

Prediction of drug-target interactions (DTIs) plays a crucial role in drug retargeting, which can save costs and shorten time for drug development. However, existing methods are still unable to integrate the multimodal features of existing DTI datasets. In this work, we propose a new multi-head-based self-attention neural network approach, called SANN-DTI, for dti prediction. Specifically, entity embeddings in the knowledge graph are learned using DistMult, then this information is interacted with traditional drug and protein representations via multi-head self-attention neural networks, and finally DTIs is computed using fully connected neural networks for interaction features. SANN-DTI was evaluated in three scenarios across two baseline datasets. After ten fold cross-validation, our model outperforms the most advanced methods. In addition, SANN-DT has been applied to drug retargeting of breast cancer via HRBB2 targets. It was found that four of the top ten recommended drugs have been supported by the literature. Ligand-target docking results showed that the second-ranked drug in the recommended list had a clear affinity with HRBB2, which provides a promising approach for better understanding drug mode of action and drug repositioning.