Junction Tree Variational Autoencoder for Molecular Graph Generation | TDLS

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Toronto Deep Learning Series, 24 September 2018

For slides and more information, visit https://tdls.a-i.science/events/2018-09-24/

Paper Review: https://arxiv.org/abs/1802.04364

Speaker: Rouzbeh Afrasiabi (Multiple Sclerosis Society of Canada)
Organizer: https://www.linkedin.com/in/amirfz/

Host: Microsoft Canada
Date: Sep 24th, 2018

Junction Tree Variational Autoencoder for Molecular Graph Generation

We seek to automate the design of molecules based on specific chemical properties. In computational terms, this task involves continuous embedding and generation of molecular graphs. Our primary contribution is the direct realization of molecular graphs, a task previously approached by generating linear SMILES strings instead of graphs. Our junction tree variational autoencoder generates molecular graphs in two phases, by first generating a tree-structured scaffold over chemical substructures, and then combining them into a molecule with a graph message passing network. This approach allows us to incrementally expand molecules while maintaining chemical validity at every step. We evaluate our model on multiple tasks ranging from molecular generation to optimization. Across these tasks, our model outperforms previous state-of-the-art baselines by a significant margin.



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Tags:
Science & Technology
Deep Learning
Chemistry
Variational Autoencoder
Molecular Graph Generation
neural chemistry
junction tree