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Found 2 papers, 1 papers with code
All SMILES Variational Autoencoder
Variational autoencoders (VAEs) defined over SMILES string and graph-based representations of molecules promise to improve the optimization of molecular properties, thereby revolutionizing the pharmaceuticals and materials industries.
Ranked #1 on
Molecular Graph Generation
on ZINC
(QED Top-3 metric)
Discrete Variational Autoencoders
We present a novel method to train a class of probabilistic models with discrete latent variables using the variational autoencoder framework, including backpropagation through the discrete latent variables.
