# Predicting Retrosynthetic Reaction using Self-Corrected Transformer Neural Networks

arXiv: https://arxiv.org/abs/1907.01356 · HTML: https://mlsanity.com/paper/1907.01356/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jul 2019.

## Built on by (most cited first, 3 of 3)

- [RetCL: A Selection-based Approach for Retrosynthesis via Contrastive Learning](https://mlsanity.com/paper/2105.00795/) (2021)
- [Bi-level Contrastive Learning for Knowledge-Enhanced Molecule Representations](https://mlsanity.com/paper/2306.01631/) (2023)
- [Knowledge-informed Molecular Learning: A Survey on Paradigm Transfer](https://mlsanity.com/paper/2202.10587/) (2022)

## Abstract (arXiv)

Synthesis planning is the process of recursively decomposing target molecules into available precursors. Computer-aided retrosynthesis can potentially assist chemists in designing synthetic routes, but at present it is cumbersome and provides results of dissatisfactory quality. In this study, we develop a template-free self-corrected retrosynthesis predictor (SCROP) to perform a retrosynthesis prediction task trained by using the Transformer neural network architecture. In the method, the retrosynthesis planning is converted as a machine translation problem between molecular linear notations of reactants and the products. Coupled with a neural network-based syntax corrector, our method achieves an accuracy of 59.0% on a standard benchmark dataset, which increases >21% over other deep learning methods, and >6% over template-based methods. More importantly, our method shows an accuracy 1.7 times higher than other state-of-the-art methods for compounds not appearing in the training set.