Chun Yujie, Li Huaihu, Wang Shunfang
Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming 650504, Yunnan, P. R. China.
Yunnan Key Laboratory of Intelligent Systems and Computing, Yunnan University, Kunming 650504, Yunnan, P. R. China.
J Bioinform Comput Biol. 2025 Feb;23(1):2550002. doi: 10.1142/S0219720025500027. Epub 2025 Mar 25.
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.
药物-靶点相互作用（DTI）预测在药物发现和药物再利用中至关重要，通过节省时间和资源以及加快潜在靶点的识别，为传统的湿实验室实验提供了一种更高效的替代方法。当前的DTI方法主要集中于从药物和蛋白质序列中提取语义特征或利用结构信息，常常忽略了两者的整合。这一差距阻碍了对药物和蛋白质分子的全面表征。为了解决这个问题，我们提出了SS-DTI，一种整合语义和结构信息的新型端到端深度学习方法。我们的方法具有一个多尺度语义特征提取模块，用于从序列中捕获局部和全局信息，并采用图卷积网络（GCN）来学习结构特征。在四个基准数据集上的评估表明，SS-DTI优于现有方法，展示了其卓越的预测性能。我们的代码可在https://github.com/RobinChun/SS-DTI获取。
