Host: The Japanese Society for Artificial Intelligence
Name : The 32nd Annual Conference of the Japanese Society for Artificial Intelligence, 2018
Number : 32
Location : [in Japanese]
Date : June 05, 2018 - June 08, 2018
This paper is concerned with a graph grammar that can be inferred from data and always generates valence-consistent molecular graphs. Our result is that the requirement above can be satisfied by a hyperedge replacement grammar inferred from molecular hypergraphs, which we call a molecular hypergraph grammar (MHG). By substituting MHG for SMILES (a grammar generating a string representation of a molecule) in a generative model of molecules, we can generate novel molecules without decoding errors, which have been one of the common issues when using SMILES.
