2018
Constrained Graph Variational Autoencoders for Molecule Design
NeurIPS 2018poster
Graphs are ubiquitous data structures for representing interactions between entities. With an emphasis on applications in chemistry, we explore the task of learning to generate graphs that conform to a distribution observed in training data. We propose a variational autoencoder model in which both e…