Fig. 1: Schematic diagram of the SpHN-VDA architecture.

a An illustrative example of Spatial Hierarchical Network and Community Hierarchical Network reveals their markedly distinct structures and learning processes. b The pipeline of SpHN-VDA. We formulated the whole process as five phases. c The process of prior knowledge learning, whose information is extracted from the drug synergistic/antagonistic interaction network and virus sequences, is used to initialize the features. Furthermore, both the drug spatial structure and the association structure are essential for VDA prediction. Thus, the SpHN-VDA contains two-level perspectives: d the micro-atom-level perspective, which serves each atom of the molecule as the node and connects the spatially proximate atoms to learn the atom-level features, and e the macro entity-level perspective, which serves drugs and viruses as nodes and uses the prior knowledge information and atom-level representation as node initialization to learn the entity-level features. f According to the prediction results of the SpHN-VDA, we evaluate the performance in diverse scenarios, containing sample splitting with multiple ratios, out-of-distribution, and perturbational datasets; provide interpretable biochemical evidence by uncovering the complete reasoning process from the 3D molecular structure to the biological association metapath coherently; find a potential candidate drug with high confidence through further biological data analysis of gene expression analysis and CMap; and visualize the molecular docking result for further verification.