# Linking the Neural Machine Translation and the Prediction of Organic Chemistry Reactions

arXiv: https://arxiv.org/abs/1612.09529 · HTML: https://mlsanity.com/paper/1612.09529/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Dec 2016.

## Built on by (most cited first, 8 of 8)

- [A Review of Large Language Models and Autonomous Agents in Chemistry](https://mlsanity.com/paper/2407.01603/) (2024)
- [Permutation invariant graph-to-sequence model for template-free retrosynthesis and reaction prediction](https://mlsanity.com/paper/2110.09681/) (2021)
- [Root-aligned SMILES: A Tight Representation for Chemical Reaction Prediction](https://mlsanity.com/paper/2203.11444/) (2022)
- [Modern Hopfield Networks for Few- and Zero-Shot Reaction Template Prediction](https://mlsanity.com/paper/2104.03279/) (2021)
- [G-MATT: Single-step Retrosynthesis Prediction using Molecular Grammar Tree Transformer](https://mlsanity.com/paper/2305.03153/) (2023)
- [Text-Augmented Multimodal LLMs for Chemical Reaction Condition Recommendation](https://mlsanity.com/paper/2407.15141/) (2024)
- [Driving Reaction Trajectories via Latent Flow Matching](https://mlsanity.com/paper/2602.10476/) (2026)
- [SynLaD: Latent Diffusion for Generating Synthesizable Molecules Conditioned on 3D Pharmacophore Profiles](https://mlsanity.com/paper/2607.01105/) (2026)

## Abstract (arXiv)

Finding the main product of a chemical reaction is one of the important problems of organic chemistry. This paper describes a method of applying a neural machine translation model to the prediction of organic chemical reactions. In order to translate 'reactants and reagents' to 'products', a gated recurrent unit based sequence-to-sequence model and a parser to generate input tokens for model from reaction SMILES strings were built. Training sets are composed of reactions from the patent databases, and reactions manually generated applying the elementary reactions in an organic chemistry textbook of Wade. The trained models were tested by examples and problems in the textbook. The prediction process does not need manual encoding of rules (e.g., SMARTS transformations) to predict products, hence it only needs sufficient training reaction sets to learn new types of reactions.