Hierarchical multimodal self-attention-based graph neural network for DTI prediction

• Published in: Briefings in bioinformatics 2024-05, Vol.25 (4)
• Main Authors: Bian, Jilong, Lu, Hao, Dong, Guanghui, Wang, Guohua
• Format: Article
• Language: English
• Subjects: Algorithms; Computational Biology - methods; Data integration; Deep Learning; Drug development; Drug interaction; Drugs; Graph neural networks; Humans; Modal data; Neural networks; Neural Networks, Computer; Predictions; Problem Solving Protocol; Proteins; Proteins - chemistry; Proteins - metabolism
• ISSN: 1467-5463; 1477-4054; 1477-4054
• DOI: 10.1093/bib/bbae293

Abstract Drug–target interactions (DTIs) are a key part of drug development process and their accurate and efficient prediction can significantly boost development efficiency and reduce development time. Recent years have witnessed the rapid advancement of deep learning, resulting in an abundance of deep learning-based models for DTI prediction. However, most of these models used a single representation of drugs and proteins, making it difficult to comprehensively represent their characteristics. Multimodal data fusion can effectively compensate for the limitations of single-modal data. However, existing multimodal models for DTI prediction do not take into account both intra- and inter-modal interactions simultaneously, resulting in limited presentation capabilities of fused features and a reduction in DTI prediction accuracy. A hierarchical multimodal self-attention-based graph neural network for DTI prediction, called HMSA-DTI, is proposed to address multimodal feature fusion. Our proposed HMSA-DTI takes drug SMILES, drug molecular graphs, protein sequences and protein 2-mer sequences as inputs, and utilizes a hierarchical multimodal self-attention mechanism to achieve deep fusion of multimodal features of drugs and proteins, enabling the capture of intra- and inter-modal interactions between drugs and proteins. It is demonstrated that our proposed HMSA-DTI has significant advantages over other baseline methods on multiple evaluation metrics across five benchmark datasets.