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We seek to automate the design of molecules based on specific chemicalproperties. In computational terms, this task involves continuous embedding andgeneration of molecular graphs. Our primary contribution is the directrealization of molecular graphs, a task previously approached by generatinglinear SMILES strings instead of graphs. Our junction tree variationalautoencoder generates molecular graphs in two phases, by first generating atree-structured scaffold over chemical substructures, and then combining theminto a molecule with a graph message passing network. This approach allows usto incrementally expand molecules while maintaining chemical validity at everystep. We evaluate our model on multiple tasks ranging from molecular generationto optimization. Across these tasks, our model outperforms previousstate-of-the-art baselines by a significant margin.

Junction Tree Variational Autoencoder for Molecular Graph Generation