Graph Convolutional Neural Networks for Molecule Generation


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.

Sep 23, 2019 12:00 AM
Institute for Pure and Applied Mathematics, UCLA
Los Angeles, CA
Xavier Bresson
Assoc. Professor of Computer Science

Xavier Bresson is Associate Professor in Computer Science at NTU, Singapore. He is a leading researcher in the field of Graph Deep Learning, a new framework that combines graph theory and deep learning techniques.