422 research outputs found
Conditional Constrained Graph Variational Autoencoders for Molecule Design
In recent years, deep generative models for graphs have been used to generate
new molecules. These models have produced good results, leading to several
proposals in the literature. However, these models may have troubles learning
some of the complex laws governing the chemical world. In this work, we explore
the usage of the histogram of atom valences to drive the generation of
molecules in such models. We present Conditional Constrained Graph Variational
Autoencoder (CCGVAE), a model that implements this key-idea in a
state-of-the-art model, and shows improved results on several evaluation
metrics on two commonly adopted datasets for molecule generation
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