Identifying binding compounds against a target protein is crucial for large-scale virtual screening in drug development. Recently, network-based methods have been developed for compound-protein interaction (CPI) prediction. However, they are difficult to be applied to unseen (i.e., never-seen-before) proteins and compounds. In this study, we propose SgCPI to incorporate local known interacting networks to predict CPI interactions. SgCPI randomly samples the local CPI network of the query compound-protein pair as a subgraph and applies a heterogeneous graph neural network (HGNN) to embed the active/inactive message of the subgraph. For unseen compounds and proteins, SgCPI-KD takes SgCPI as the teacher model to distillate its knowledge by estimating the potential neighbors. Experimental results indicate: (1) the sampled subgraphs of the CPI network introduce efficient knowledge for unseen molecular prediction with the HGNNs, and (2) the knowledge distillation strategy is beneficial to the double-blind interaction prediction by estimating molecular neighbors and distilling knowledge.

识别针对靶蛋白的结合化合物对于药物开发中的大规模虚拟筛选至关重要。最近，基于网络的方法被开发用于化合物-蛋白质相互作用（CPI）预测。然而，它们很难应用于看不见的（即以前从未见过的）蛋白质和化合物。在本研究中，我们建议 SgCPI 结合本地已知的交互网络来预测 CPI 交互。 SgCPI 将查询化合物-蛋白质对的本地 CPI 网络随机采样为子图，并应用异构图神经网络 (HGNN) 来嵌入子图的活动/非活动消息。对于看不见的化合物和蛋白质，SgCPI-KD 以 SgCPI 作为教师模型，通过估计潜在的邻居来提炼其知识。实验结果表明：（1）CPI网络的采样子图引入了HGNN用于看不见的分子预测的有效知识，（2）知识蒸馏策略通过估计分子邻居和蒸馏知识有利于双盲相互作用预测。