  Predicating macroscopic influences of drugs on human body, like efficacy and
toxicity, is a central problem of small-molecule based drug discovery.
Molecules can be represented as an undirected graph, and we can utilize graph
convolution networks to predication molecular properties. However, graph
convolutional networks and other graph neural networks all focus on learning
node-level representation rather than graph-level representation. Previous
works simply sum all feature vectors for all nodes in the graph to obtain the
graph feature vector for drug predication. In this paper, we introduce a dummy
super node that is connected with all nodes in the graph by a directed edge as
the representation of the graph and modify the graph operation to help the
dummy super node learn graph-level feature. Thus, we can handle graph-level
classification and regression in the same way as node-level classification and
regression. In addition, we apply focal loss to address class imbalance in drug
datasets. The experiments on MoleculeNet show that our method can effectively
improve the performance of molecular properties predication.
