论文标题
用eproivariant线图网络预测蛋白质 - 配体的结合亲和力
Predicting Protein-Ligand Binding Affinity with Equivariant Line Graph Network
论文作者
论文摘要
三维（3D）蛋白配体复合物的结合亲和力预测对于药物重新定位和虚拟药物筛查至关重要。现有方法将3D蛋白质配体复合物转换为二维（2D）图，然后使用图形神经网络（GNN）预测其结合亲和力。但是，根据3D复合物的不变局部坐标系统提取2D图的节点和边缘特征。结果，该方法无法完全学习复合物的全球信息，例如物理对称性和键的拓扑信息。为了解决这些问题，我们提出了一个新型的模棱两可的线图网络（ELGN），用于3D蛋白配体配合物的亲和力预测。提出的ELGN首先在3D复合体中添加了一个超级节点，然后基于3D复合体构建了线图。之后，ELGN使用新的E（3） - 等级网络层来传递基于3D复合物的全局坐标系之间的节点和边缘之间的消息。两个实际数据集的实验结果证明了ELGN对几个最新基准的有效性。
Binding affinity prediction of three-dimensional (3D) protein ligand complexes is critical for drug repositioning and virtual drug screening. Existing approaches transform a 3D protein-ligand complex to a two-dimensional (2D) graph, and then use graph neural networks (GNNs) to predict its binding affinity. However, the node and edge features of the 2D graph are extracted based on invariant local coordinate systems of the 3D complex. As a result, the method can not fully learn the global information of the complex, such as, the physical symmetry and the topological information of bonds. To address these issues, we propose a novel Equivariant Line Graph Network (ELGN) for affinity prediction of 3D protein ligand complexes. The proposed ELGN firstly adds a super node to the 3D complex, and then builds a line graph based on the 3D complex. After that, ELGN uses a new E(3)-equivariant network layer to pass the messages between nodes and edges based on the global coordinate system of the 3D complex. Experimental results on two real datasets demonstrate the effectiveness of ELGN over several state-of-the-art baselines.
