Deep Learning Integration with Phenotypic Similarities and Heterogeneous Networks for Drug-Target Interaction Prediction

In the field of drug discovery, the accurate prediction of drug-target interactions (DTIs) is a critical yet challenging task, hindered by the intricate dynamics of biological systems and molecular interplay. To address this, we propose the DTI-VGAE model, a novel deep learning framework that integrates variational graph autoencoders (VGAE) with a multi-layer perceptron (MLP) for robust DTI prediction. Our approach focuses on three key aspects: learning distinct representations of drugs and proteins from heterogeneous networks, constructing Drug-Protein Pair (DPP) networks to capture the complex interactions, and employing MLP for the final prediction of DTIs. This comprehensive methodology not only enhances the accuracy of DTI predictions but also ensures greater reliability and stability. Validated through extensive 5-fold cross-validation, the DTI-VGAE model consistently outperforms existing methods, achieving superior average AUROC, AUPR scores, and accuracy. The DTI-VGAE model's innovative integration of VGAE and MLP offers a significant advancement in the computational approach to drug discovery, paving the way for more efficient and precise drug development processes.