Prediction of Drug-Target Interactions Based on Hypergraph Neural Networks With Multimodal Feature Fusion

Accurate drug-target interaction prediction is vital for drug discovery and optimization. Traditional experimental methods, while effective, are time-intensive and costly. HyperGCN-DTI, a novel framework that explicitly advances beyond existing models such as CHL-DTI and HHDTI by leveraging hypergraph neural networks with a multimodal feature fusion strategy. While exsiting methods primarily focuses on low-order graph representations and fixed heterogeneous network structures, HyperGCN-DTI incorporates richer multimodal fused features including embeddings from pretrained language models and diverse biological networks and build robust hypergraphs that capture high-order multi-entity relationships within drug-target pairs. This dual-channel architecture effectively captures both local topological connections and higher-order structural dependencies. HyperGCN-DTI outperforms state-of-the-art DTI prediction models across multiple datasets and remains robust under imbalanced and large-scale real-world datasets, demonstrating its superior predictive power. The model demonstrates significant improvements when using multimodal features and hypergraph-based message passing, with sensitivity analysis confirming stability across hyperparameter variations. Top-ranked predictions are validated through biomedical literature and molecular docking, underscoring the reliability and practical relevance of our approach. HyperGCN-DTI is the first DTI prediction model to jointly integrate such a wide range of heterogeneous information sources with hypergraph representation, significantly enhancing accuracy and robustness, particularly in sparse or noisy settings. The proposed model offers a powerful and generalizable tool for accelerating drug development and target identification.