论文标题
药物目标亲和力预测方法基于异质数据的一致表达
Drug-target affinity prediction method based on consistent expression of heterogeneous data
论文作者
论文摘要
药物发现的第一步是发现针对特定靶标的药物活性的药物分子部分。因此，研究药物目标蛋白与小型化学分子之间的相互作用至关重要。但是，发现潜在的小药物分子的传统实验方法是劳动密集型且耗时的。目前，人们对使用与药物分子相关的数据库筛选小药物分子有很大的兴趣。在本文中，我们提出了一种使用深度学习模型来预测药物目标结合亲和力的方法。该方法使用改进的GRU和GNN从药物目标蛋白序列和药物分子图中提取特征，以获取其特征矢量。合并的矢量用作药物目标分子对的矢量表示，然后馈入完全连接的网络以预测药物靶标结合亲和力。该提出的模型证明了其在预测戴维斯和KIBA数据集的药物目标结合亲和力方面的准确性和有效性。
The first step in drug discovery is finding drug molecule moieties with medicinal activity against specific targets. Therefore, it is crucial to investigate the interaction between drug-target proteins and small chemical molecules. However, traditional experimental methods for discovering potential small drug molecules are labor-intensive and time-consuming. There is currently a lot of interest in building computational models to screen small drug molecules using drug molecule-related databases. In this paper, we propose a method for predicting drug-target binding affinity using deep learning models. This method uses a modified GRU and GNN to extract features from the drug-target protein sequences and the drug molecule map, respectively, to obtain their feature vectors. The combined vectors are used as vector representations of drug-target molecule pairs and then fed into a fully connected network to predict drug-target binding affinity. This proposed model demonstrates its accuracy and effectiveness in predicting drug-target binding affinity on the DAVIS and KIBA datasets.
