Drug discovery often relies on the successful prediction of protein-ligand
binding affinity. Recent advances have shown great promise in applying graph
neural networks (GNNs) for better affinity prediction by learning the
representations of protein-ligand complexes. However, existing solutions
usually treat protein-ligand complexes as topological graph data, thus the
biomolecular structural information is not fully utilized. The essential
long-range interactions among atoms are also neglected in GNN models. To this
end, we propose a structure-aware interactive graph neural network (SIGN) which
consists of two components: polar-inspired graph attention layers (PGAL) and
pairwise interactive pooling (PiPool). Specifically, PGAL iteratively performs
the node-edge aggregation process to update embeddings of nodes and edges while
preserving the distance and angle information among atoms. Then, PiPool is
adopted to gather interactive edges with a subsequent reconstruction loss to
reflect the global interactions. Exhaustive experimental study on two
benchmarks verifies the superiority of SIGN.

本研究提出了一种结构感知交互图神经网络 (SIGN)，用于预测蛋白质 - 配体复合物的亲和力。SIGN 包含极性启发式图注意力层（PGAL）和配对交互池（PiPool）两个部分，能充分利用生物分子结构信息并考虑原子之间的长程相互作用，在两个基准测试中显示其优越性。