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Graph Convolutional Neural Networks for Molecule Generation

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13 November 2020


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Graph Convolutional Neural Networks for Molecule Generation

  • Machine Learning for Physics and the Physics of Learning 2019
  • Workshop I: From Passive to Active: Generative and Reinforcement Learning with Physics

  • “Graph Convolutional Neural Networks for Molecule Generation”
  • Xavier Bresson, Nanyang Technological University

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.

  • Institute for Pure and Applied Mathematics, UCLA
  • September 23, 2019

For more information: http://www.ipam.ucla.edu/mlpws1


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