  Accurately predicting the binding affinity between drugs and proteins is an
essential step for computational drug discovery. Since graph neural networks
(GNNs) have demonstrated remarkable success in various graph-related tasks,
GNNs have been considered as a promising tool to improve the binding affinity
prediction in recent years. However, most of the existing GNN architectures can
only encode the topological graph structure of drugs and proteins without
considering the relative spatial information among their atoms. Whereas,
different from other graph datasets such as social networks and commonsense
knowledge graphs, the relative spatial position and chemical bonds among atoms
have significant impacts on the binding affinity. To this end, in this paper,
we propose a diStance-aware Molecule graph Attention Network (S-MAN) tailored
to drug-target binding affinity prediction. As a dedicated solution, we first
propose a position encoding mechanism to integrate the topological structure
and spatial position information into the constructed pocket-ligand graph.
Moreover, we propose a novel edge-node hierarchical attentive aggregation
structure which has edge-level aggregation and node-level aggregation. The
hierarchical attentive aggregation can capture spatial dependencies among
atoms, as well as fuse the position-enhanced information with the capability of
discriminating multiple spatial relations among atoms. Finally, we conduct
extensive experiments on two standard datasets to demonstrate the effectiveness
of S-MAN.
