Wang Xun, Xia Zhijun, Feng Runqiu, Han Tongyu, Wang Hanyu, Yu Wenqian, Wang Xingguang
Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Changjiang West Road, Qingdao, 266580, Shandong, China.
Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software, Qingdao, Shandong, 266580, China.
BMC Biol. 2025 May 9;23(1):120. doi: 10.1186/s12915-025-02222-x.
Drug-target binding affinity (DTA) prediction can accelerate the drug screening process, and deep learning techniques have been used in all facets of drug research. Affinity prediction based on deep learning methods has proven crucial to drug discovery, design, and reuse. Among these, the sequence-based approach using 1D sequences of drugs and targets as inputs typically results in the loss of structural information, whereas the structure-based method frequently results in increased computing costs due to the intricate structure of the molecule graph.
We propose a sequential multifeature fusion method (SMFF-DTA) to achieve efficient and accurate prediction. SMFF-DTA uses sequential methods to represent the structural information and physicochemical properties of drugs and targets and introduces multiple attention blocks to capture interaction features closely.
As demonstrated by our extensive studies, SMFF-DTA outperforms the other methods in terms of various metrics, showing its advantages and effectiveness as a drug-target binding affinity predictor.
药物-靶点结合亲和力（DTA）预测可以加速药物筛选过程，深度学习技术已应用于药物研究的各个方面。基于深度学习方法的亲和力预测已被证明对药物发现、设计和再利用至关重要。其中，以药物和靶点的一维序列作为输入的基于序列的方法通常会导致结构信息的丢失，而基于结构的方法由于分子图结构复杂，计算成本往往会增加。
我们提出了一种序列多特征融合方法（SMFF-DTA）以实现高效准确的预测。SMFF-DTA使用序列方法来表示药物和靶点的结构信息和物理化学性质，并引入多个注意力模块以紧密捕捉相互作用特征。
我们的广泛研究表明，SMFF-DTA在各项指标上均优于其他方法，显示出其作为药物-靶点结合亲和力预测器的优势和有效性。
