DrugGPS: Attention-guided multimodal fusion for intelligent exploration of drug-target and drug-disease interactions.

Abstract

<h4>Background and purpose</h4>Accurate prediction of drug-target interactions (DTIs) and drug-disease interactions (DDIs) are critical for accelerating the drug discovery process. However, conventional unimodal approaches struggle to capture the complex biochemical and pharmacological relationships between drugs and targets, thereby limiting model accuracy and generalisability. Hence, it is essential to develop innovative approaches that can enhance predictive performance.<h4>Experimental approach</h4>To overcome these limitations, we introduce DrugGPS as a novel multimodal predictive framework. DrugGPS integrates heterogeneous biological data, using a multi-channel feature fusion strategy guided by attention mechanisms for highly accurate DTI and DDI prediction.<h4>Key results</h4>DrugGPS integrates structural and sequential representations, biological relational networks and similarity-based graphs to learn enriched feature embeddings. It adopts an attention-based fusion module to distil and integrate cross-channel information, strengthening its ability to characterise complex chemical-biological interactions. Additionally, the framework incorporates MeSH-derived disease features to unify the modelling of drug-target-disease associations, thus providing deeper insights into therapeutic mechanisms. A case study on the mineralocorticoid receptor (MR) verifies its utility with the model by identifying four experimentally validated active compounds. An interactive visualisation platform was developed to explore predicted DTI and DDI interactions. Extensive experiments on public datasets show that DrugGPS outperforms state-of-the-art methods in accuracy, robustness and computational efficiency, demonstrating its potential for intelligent drug discovery and repositioning.<h4>Conclusion and implications</h4>These findings indicate the potential of DrugGPS to improve DTI and DDI interaction predictions for drug repositioning, and its potential in accelerating drug development and discovery.