SS-DTI: A deep learning method integrating semantic and structural information for drug-target interaction prediction.
Yujie Chun, Huaihu Li, Shunfang Wang
Author Information
Yujie Chun: Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming 650504, Yunnan, P. R. China. ORCID
Huaihu Li: Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming 650504, Yunnan, P. R. China. ORCID
Shunfang Wang: Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming 650504, Yunnan, P. R. China. ORCID
Drug-target interaction (DTI) prediction is pivotal in drug discovery and repurposing, providing a more efficient alternative to traditional wet-lab experiments by saving time and resources and expediting the identification of potential targets. Current DTI methods predominantly focus on extracting semantic features from drug and protein sequences or utilizing structural information, often neglecting the integration of both. This gap hinders the achievement of a comprehensive representation of drug and protein molecules. To address this, we propose SS-DTI, a novel end-to-end deep learning approach that integrates both semantic and structural information. Our method features a multi-scale semantic feature extraction block to capture local and global information from sequences and employs Graph Convolutional Networks (GCNs) to learn structural features. Evaluations on four benchmark datasets demonstrate that SS-DTI outperforms state-of-the-art methods, showcasing its superior predictive performance. Our code is available at https://github.com/RobinChun/SS-DTI.
