Timezone: »
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.
Author Information
Wengong Jin (MIT Computer Science and Artificial Intelligence Laboratory)
Regina Barzilay (MIT CSAIL)
Tommi Jaakkola (MIT)
Related Events (a corresponding poster, oral, or spotlight)
-
2018 Oral: Junction Tree Variational Autoencoder for Molecular Graph Generation »
Fri Jul 13th 09:00 -- 09:20 AM Room A7
More from the Same Authors
-
2020 Poster: Generalization and Representational Limits of Graph Neural Networks »
Vikas K Garg · Stefanie Jegelka · Tommi Jaakkola -
2020 Poster: Multi-Objective Molecule Generation using Interpretable Substructures »
Wengong Jin · Regina Barzilay · Tommi Jaakkola -
2020 Poster: Educating Text Autoencoders: Latent Representation Guidance via Denoising »
Tianxiao Shen · Jonas Mueller · Regina Barzilay · Tommi Jaakkola -
2020 Poster: Invariant Rationalization »
Shiyu Chang · Yang Zhang · Mo Yu · Tommi Jaakkola -
2020 Poster: Predicting deliberative outcomes »
Vikas K Garg · Tommi Jaakkola -
2020 Poster: Hierarchical Generation of Molecular Graphs using Structural Motifs »
Wengong Jin · Regina Barzilay · Tommi Jaakkola -
2020 Poster: Improving Molecular Design by Stochastic Iterative Target Augmentation »
Kevin Yang · Wengong Jin · Kyle Swanson · Regina Barzilay · Tommi Jaakkola -
2019 Poster: Functional Transparency for Structured Data: a Game-Theoretic Approach »
Guang-He Lee · Wengong Jin · David Alvarez-Melis · Tommi Jaakkola -
2019 Oral: Functional Transparency for Structured Data: a Game-Theoretic Approach »
Guang-He Lee · Wengong Jin · David Alvarez-Melis · Tommi Jaakkola -
2017 Poster: Learning Sleep Stages from Radio Signals: A Conditional Adversarial Architecture »
Mingmin Zhao · Shichao Yue · Dina Katabi · Tommi Jaakkola · Matt Bianchi -
2017 Talk: Learning Sleep Stages from Radio Signals: A Conditional Adversarial Architecture »
Mingmin Zhao · Shichao Yue · Dina Katabi · Tommi Jaakkola · Matt Bianchi -
2017 Poster: Sequence to Better Sequence: Continuous Revision of Combinatorial Structures »
Jonas Mueller · David Gifford · Tommi Jaakkola -
2017 Talk: Sequence to Better Sequence: Continuous Revision of Combinatorial Structures »
Jonas Mueller · David Gifford · Tommi Jaakkola -
2017 Poster: Deriving Neural Architectures from Sequence and Graph Kernels »
Tao Lei · Wengong Jin · Regina Barzilay · Tommi Jaakkola -
2017 Talk: Deriving Neural Architectures from Sequence and Graph Kernels »
Tao Lei · Wengong Jin · Regina Barzilay · Tommi Jaakkola