A multimodule graph-based neural network for accurate drug-target interaction prediction via genomic, proteomic, and structural data fusion

Purpose: Drug-target interaction (DTI) prediction is a critical step for accelerating drug discovery and repurposing. However, the experimental approach for DTI is time-consuming and has experimental costs. Existing computational approaches typically rely on signal data modalities, limiting their ability to capture the complex biological mechanisms involved fully. Therefore, there is significant scope for advancing a deep learning framework that unifies the different data types and provides a more comprehensive understanding of molecular mechanisms for drug-target interaction predictions. Outcomes: To address this, we have developed GINCOVNET, a graph-based neural network for DTI prediction that integrates multiple data modalities, including the molecular structure information, the target sequence, and the molecular and target's perturbed gene expression. Our study evaluation demonstrated that the multi-data fusion model outperformed previous studies with an R² of 0.976 and MAE of 0.053, which is significantly higher compared to previous studies. Our ablation study shows that incorporating gene expression data improves the model's capabilities compared to molecule-target data. Furthermore, the molecular docking of the randomly selected molecule-target pair validates the reliability of our model in identifying potential interactions and its facilitation in the identification of repurposed drug interactions and novel therapeutic discoveries.