Graph Convolutional Neural Networks for Molecule Generation and Travelling Salesman Problem

Xavier Bresson
Nanyang Technological University, Singapore

In this talk, I will discuss how to apply graph convolutional neural networks to quantum chemistry and operational research. The same high-level paradigm can be applied to generate new molecules with optimized chemical properties and to solve the Travelling Salesman Problem. The proposed approach consists of two steps. First, a graph ConvNet is used to auto-encode molecules and estimate TSP solutions in one-shot. Second, beam search is applied to the output of neural networks to produce a valid chemical or combinatorial solution. Numerical experiments demonstrate the performances of this learning system.

Presentation (PDF File)

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