Graph Convolutional Neural Networks for Molecule Generation

Abstract

In this talk, I will discuss a graph convolutional neural network architecture for the molecule generation task. The proposed approach consists of two steps. First, a graph ConvNet is used to auto-encode molecules in one-shot. Second, beam search is applied to the output of neural networks to produce a valid chemical solution. Numerical experiments demonstrate the performances of this learning system.