# G-MATT: Single-step Retrosynthesis Prediction using Molecular Grammar Tree Transformer

arXiv: https://arxiv.org/abs/2305.03153 · HTML: https://mlsanity.com/paper/2305.03153/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 39 references that are in the map.
- First posted on arXiv 4 May 2023.

## Builds on (most cited first, 11 of 11)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Molecular Transformer - A Model for Uncertainty-Calibrated Chemical Reaction Prediction](https://mlsanity.com/paper/1811.02633/) (2018)
- [Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network](https://mlsanity.com/paper/1709.04555/) (2017)
- [Retrosynthetic reaction prediction using neural sequence-to-sequence models](https://mlsanity.com/paper/1706.01643/) (2017)
- [SMILES2Vec: An Interpretable General-Purpose Deep Neural Network for Predicting Chemical Properties](https://mlsanity.com/paper/1712.02034/) (2017)
- [Learning to Make Generalizable and Diverse Predictions for Retrosynthesis](https://mlsanity.com/paper/1910.09688/) (2019)
- [Tree-Transformer: A Transformer-Based Method for Correction of Tree-Structured Data](https://mlsanity.com/paper/1908.00449/) (2019)
- [Linking the Neural Machine Translation and the Prediction of Organic Chemistry Reactions](https://mlsanity.com/paper/1612.09529/) (2016)
- [Retrosynthesis with Attention-Based NMT Model and Chemical Analysis of the "Wrong" Predictions](https://mlsanity.com/paper/1908.00727/) (2019)
- [Transformer with Tree-order Encoding for Neural Program Generation](https://mlsanity.com/paper/2206.13354/) (2022)
- [AI-driven Hypergraph Network of Organic Chemistry: Network Statistics and Applications in Reaction Classification](https://mlsanity.com/paper/2208.01647/) (2022)

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

- [Pre-trained Molecular Language Models with Random Functional Group Masking](https://mlsanity.com/paper/2411.01401/) (2024)

## Abstract (arXiv)

Various template-based and template-free approaches have been proposed for single-step retrosynthesis prediction in recent years. While these approaches demonstrate strong performance from a data-driven metrics standpoint, many model architectures do not incorporate underlying chemistry principles. Here, we propose a novel chemistry-aware retrosynthesis prediction framework that combines powerful data-driven models with prior domain knowledge. We present a tree-to-sequence transformer architecture that utilizes hierarchical SMILES grammar-based trees, incorporating crucial chemistry information that is often overlooked by SMILES text-based representations, such as local structures and functional groups. The proposed framework, grammar-based molecular attention tree transformer (G-MATT), achieves significant performance improvements compared to baseline retrosynthesis models. G-MATT achieves a promising top-1 accuracy of 51% (top-10 accuracy of 79.1%), invalid rate of 1.5%, and bioactive similarity rate of 74.8% on the USPTO- 50K dataset. Additional analyses of G-MATT attention maps demonstrate the ability to retain chemistry knowledge without relying on excessively complex model architectures.