# Hierarchical Graph-to-Graph Translation for Molecules

arXiv: https://arxiv.org/abs/1907.11223 · HTML: https://mlsanity.com/paper/1907.11223/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2019.

## Built on by (most cited first, 2 of 2)

- [Artificial Intelligence based Autonomous Molecular Design for Medical Therapeutic: A Perspective](https://mlsanity.com/paper/2102.06045/) (2021)
- [Transferring Chemical and Energetic Knowledge Between Molecular Systems with Machine Learning](https://mlsanity.com/paper/2205.03339/) (2022)

## Abstract (arXiv)

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 the encoding of substructure components 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 attachment to the emerging molecule. We evaluate our model on multiple molecular optimization tasks and show that our model significantly outperforms previous state-of-the-art baselines.