# ChemiRise: a data-driven retrosynthesis engine

arXiv: https://arxiv.org/abs/2108.04682 · HTML: https://mlsanity.com/paper/2108.04682/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 24 references that are in the map.
- First posted on arXiv 9 Aug 2021.

## Builds on (most cited first, 5 of 5)

- [Order Matters: Sequence to sequence for sets](https://mlsanity.com/paper/1511.06391/) (2015)
- [Learning to Plan Chemical Syntheses](https://mlsanity.com/paper/1708.04202/) (2017)
- [Retrosynthesis Prediction with Conditional Graph Logic Network](https://mlsanity.com/paper/2001.01408/) (2020)
- [Retrosynthetic reaction prediction using neural sequence-to-sequence models](https://mlsanity.com/paper/1706.01643/) (2017)
- [Chemi-net: a graph convolutional network for accurate drug property prediction](https://mlsanity.com/paper/1803.06236/) (2018)

## Built on by (most cited first, 1 of 1)

- [T- Hop: A framework for studying the importance path information in molecular graphs for chemical property prediction](https://mlsanity.com/paper/2407.14270/) (2024)

## Abstract (arXiv)

We have developed an end-to-end, retrosynthesis system, named ChemiRise, that can propose complete retrosynthesis routes for organic compounds rapidly and reliably. The system was trained on a processed patent database of over 3 million organic reactions. Experimental reactions were atom-mapped, clustered, and extracted into reaction templates. We then trained a graph convolutional neural network-based one-step reaction proposer using template embeddings and developed a guiding algorithm on the directed acyclic graph (DAG) of chemical compounds to find the best candidate to explore. The atom-mapping algorithm and the one-step reaction proposer were benchmarked against previous studies and showed better results. The final product was demonstrated by retrosynthesis routes reviewed and rated by human experts, showing satisfying functionality and a potential productivity boost in real-life use cases.