Department of Computer Science and Technology, Shandong University of Technology, Zibo, 255000, China.
Department of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo, 255000, China.
Sci Rep. 2024 Mar 28;14(1):7416. doi: 10.1038/s41598-024-57879-1.
Predicting the interaction affinity between drugs and target proteins is crucial for rapid and accurate drug discovery and repositioning. Therefore, more accurate prediction of DTA has become a key area of research in the field of drug discovery and drug repositioning. However, traditional experimental methods have disadvantages such as long operation cycles, high manpower requirements, and high economic costs, making it difficult to predict specific interactions between drugs and target proteins quickly and accurately. Some methods mainly use the SMILES sequence of drugs and the primary structure of proteins as inputs, ignoring the graph information such as bond encoding, degree centrality encoding, spatial encoding of drug molecule graphs, and the structural information of proteins such as secondary structure and accessible surface area. Moreover, previous methods were based on protein sequences to learn feature representations, neglecting the completeness of information. To address the completeness of drug and protein structure information, we propose a Transformer graph-based early fusion research approach for drug-target affinity prediction (GEFormerDTA). Our method reduces prediction errors caused by insufficient feature learning. Experimental results on Davis and KIBA datasets showed a better prediction of drugtarget affinity than existing affinity prediction methods.
预测药物与靶标蛋白之间的相互作用亲和力对于快速准确的药物发现和重新定位至关重要。因此，更准确地预测 DTA 已成为药物发现和药物重定位领域的研究重点。然而，传统的实验方法存在操作周期长、人力需求高、经济成本高等缺点，难以快速准确地预测药物与靶标蛋白之间的特定相互作用。一些方法主要使用药物的 SMILES 序列和蛋白质的一级结构作为输入，忽略了药物分子图的键编码、度中心编码、空间编码和蛋白质的二级结构和可及表面积等图形信息。此外，以前的方法基于蛋白质序列来学习特征表示，忽略了信息的完整性。为了解决药物和蛋白质结构信息的完整性问题，我们提出了一种基于 Transformer 图的药物-靶标亲和力预测早期融合研究方法（GEFormerDTA）。我们的方法减少了由于特征学习不足而导致的预测误差。在 Davis 和 KIBA 数据集上的实验结果表明，该方法比现有的亲和力预测方法具有更好的药物-靶标亲和力预测能力。
