Learning multimodal graph-to-graph translation for molecular optimization
Name
1812.01070.pdf
Description
Accepted version
Size
1.42 MB
Format
Adobe PDF
Checksum (MD5)
f36676d3a561b98945330ee83abfc81c
Author(s)
Jin, W
Yang, K
Barzilay, R
Jaakkola, T
Date Issued
January 2019
Journal
7th International Conference on Learning Representations, ICLR 2019
Citation
Jin, W, Yang, K, Barzilay, R and Jaakkola, T. 2019. "Learning multimodal graph-to-graph translation for molecular optimization." 7th International Conference on Learning Representations, ICLR 2019.
Version
Author's final manuscript
Abstract
© 7th International Conference on Learning Representations, ICLR 2019. All Rights Reserved. We view molecular optimization as a graph-to-graph translation problem. The goal is to learn to map from one molecular graph to another with better properties based on an available corpus of paired molecules. Since molecules can be optimized in different ways, there are multiple viable translations for each input graph. A key challenge is therefore to model diverse translation outputs. Our primary contributions include a junction tree encoder-decoder for learning diverse graph translations along with a novel adversarial training method for aligning distributions of molecules. Diverse output distributions in our model are explicitly realized by low-dimensional latent vectors that modulate the translation process. We evaluate our model on multiple molecular optimization tasks and show that our model outperforms previous state-of-the-art baselines.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://iclr.cc/Conferences/2019/Schedule?showEvent=719
