Graph Neural Networks: Graph Classification (Part III)

When It Comes to Labeling Whole Graphs, Not Just Nodes

Many real-life situations can be modeled as graphs, but turning the relational structure of these graphs into valuable information that can help solve complex tasks is a real challenge.

Take the example of drug discovery. In the early stages of drug development, scientists need to screen large libraries often composed of hundreds of thousands of compounds (drug candidates) against targets (biological events). This requires the use of an arsenal of tools such as robotics, data processing and control software, and sensitive detectors [1]. Such approaches can be very time consuming and expensive, with a very low hit rate (a typical hit rate is less than 1% in most assays!).

And, yet, the molecules' atomic composition and arrangement can already tell us a lot about their biological behavior.

Objective

This article focuses on using graph neural networks for graph classification. It also explores explainability techniques for these models.

As an illustration, we will develop a use case predicting the toxicity of a molecule. We’ll use its representation as a graph where the nodes are atoms connected by edges corresponding to chemical…