Learning Graph-Level Representation for Drug Discovery

Understand

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.