GraMDTA: Multimodal Graph Neural Networks for Predicting Drug-Target Associations

Finding: novel drug-target associations is vital for drug discovery. However, screening millions of small molecules for a select target protein is challenging. Several computational approaches have been developed in the past using Machine learning methods for computational drug-target association (DTA) prediction predominantly use structural data of drugs and proteins. Some of these approaches use knowledge graph networks and link prediction. To the best of our knowledge there have been no approaches that use both structural learning that offers molecular-based representations and knowledge graph-based learning which offers interaction-based representations for DTA discovery. Based on the premise that multimodal sources of information acting complimentarily could improve the robustness of DTA predictions, we developed GraMDTA, a multimodal graph neural network that learns both structural and knowledge graph representations utilizing multi-head attention to fuse the multimodal representations. We compare GraMDTA with other computational approaches for DTA prediction to demonstrate the power of multimodal fusion for discovery of DTA.