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NeurIPS_Submission_Wengong.pdf (1.03 MB)

Multi-Resolution Autoregressive Graph-to-Graph Translation for Molecules

submitted on 13.06.2019, 01:03 and posted on 13.06.2019, 15:14 by Wengong Jin, Regina Barzilay, Tommi S Jaakkola
The problem of accelerating drug discovery relies heavily on automatic tools to optimize precursor molecules to afford them with better biochemical properties. Our work in this paper substantially extends prior state-of-the-art on graph-to-graph translation methods for molecular optimization. In particular, we realize coherent multi-resolution representations by interweaving trees over substructures with the atom-level encoding of the original molecular graph. Moreover, our graph decoder is fully autoregressive, and interleaves each step of adding a new substructure with the process of resolving its connectivity to the emerging molecule. We evaluate our model on multiple molecular optimization tasks and show that our model outperforms previous state-of-the-art baselines by a large margin.


Email Address of Submitting Author


Massachusetts Institute of Technology


United States

ORCID For Submitting Author


Declaration of Conflict of Interest

No conflict of interest