# "Found in Translation": Predicting Outcomes of Complex Organic Chemistry Reactions using Neural Sequence-to-Sequence Models

arXiv: https://arxiv.org/abs/1711.04810 · HTML: https://mlsanity.com/paper/1711.04810/

## Key facts

- Cited by 55 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2017.

## Built on by (most cited first, 30 of 55)

- [MLAgentBench: Evaluating Language Agents on Machine Learning Experimentation](https://mlsanity.com/paper/2310.03302/) (2023)
- [Large Language Models on Graphs: A Comprehensive Survey](https://mlsanity.com/paper/2312.02783/) (2023)
- [Geometric Deep Learning on Molecular Representations](https://mlsanity.com/paper/2107.12375/) (2021)
- [Unifying Molecular and Textual Representations via Multi-task Language Modelling](https://mlsanity.com/paper/2301.12586/) (2023)
- [14 Examples of How LLMs Can Transform Materials Science and Chemistry: A Reflection on a Large Language Model Hackathon](https://mlsanity.com/paper/2306.06283/) (2023)
- [MolXPT: Wrapping Molecules with Text for Generative Pre-training](https://mlsanity.com/paper/2305.10688/) (2023)
- [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)
- [Regression Transformer: Concurrent sequence regression and generation for molecular language modeling](https://mlsanity.com/paper/2202.01338/) (2022)
- [TransPolymer: a Transformer-based language model for polymer property predictions](https://mlsanity.com/paper/2209.01307/) (2022)
- [Chemical-Reaction-Aware Molecule Representation Learning](https://mlsanity.com/paper/2109.09888/) (2021)
- [MOFormer: Self-Supervised Transformer model for Metal-Organic Framework Property Prediction](https://mlsanity.com/paper/2210.14188/) (2022)
- [Self-Improved Retrosynthetic Planning](https://mlsanity.com/paper/2106.04880/) (2021)
- [Accurate and efficient structure elucidation from routine one-dimensional NMR spectra using multitask machine learning](https://mlsanity.com/paper/2408.08284/) (2024)
- [BARTSmiles: Generative Masked Language Models for Molecular Representations](https://mlsanity.com/paper/2211.16349/) (2022)
- [Non-Autoregressive Electron Redistribution Modeling for Reaction Prediction](https://mlsanity.com/paper/2106.07801/) (2021)
- [TITAN: T Cell Receptor Specificity Prediction with Bimodal Attention Networks](https://mlsanity.com/paper/2105.03323/) (2021)
- [Retrosynthetic Planning with Experience-Guided Monte Carlo Tree Search](https://mlsanity.com/paper/2112.06028/) (2021)
- [Modern Hopfield Networks for Few- and Zero-Shot Reaction Template Prediction](https://mlsanity.com/paper/2104.03279/) (2021)
- [SemiRetro: Semi-template framework boosts deep retrosynthesis prediction](https://mlsanity.com/paper/2202.08205/) (2022)
- [Toward automatic generation of control structures for process flow diagrams with large language models](https://mlsanity.com/paper/2211.05583/) (2022)
- [MOFGPT: Generative Design of Metal-Organic Frameworks using Language Models](https://mlsanity.com/paper/2506.00198/) (2025)
- [Hybrid quantum-classical machine learning for generative chemistry and drug design](https://mlsanity.com/paper/2108.11644/) (2021)
- [Learning from flowsheets: A generative transformer model for autocompletion of flowsheets](https://mlsanity.com/paper/2208.00859/) (2022)
- [Deep Learning Methods for Small Molecule Drug Discovery: A Survey](https://mlsanity.com/paper/2303.00313/) (2023)
- [Robust PDF Document Conversion Using Recurrent Neural Networks](https://mlsanity.com/paper/2102.09395/) (2021)
- [Equivariant Denoisers Cannot Copy Graphs: Align Your Graph Diffusion Models](https://mlsanity.com/paper/2405.17656/) (2024)
- [Aligning Transformers with Continuous Feedback via Energy Rank Alignment](https://mlsanity.com/paper/2405.12961/) (2024)
- [Dataset Bias in the Natural Sciences: A Case Study in Chemical Reaction Prediction and Synthesis Design](https://mlsanity.com/paper/2105.02637/) (2021)
- [Language models in molecular discovery](https://mlsanity.com/paper/2309.16235/) (2023)

## Abstract (arXiv)

There is an intuitive analogy of an organic chemist's understanding of a compound and a language speaker's understanding of a word. Consequently, it is possible to introduce the basic concepts and analyze potential impacts of linguistic analysis to the world of organic chemistry. In this work, we cast the reaction prediction task as a translation problem by introducing a template-free sequence-to-sequence model, trained end-to-end and fully data-driven. We propose a novel way of tokenization, which is arbitrarily extensible with reaction information. With this approach, we demonstrate results superior to the state-of-the-art solution by a significant margin on the top-1 accuracy. Specifically, our approach achieves an accuracy of 80.1% without relying on auxiliary knowledge such as reaction templates. Also, 66.4% accuracy is reached on a larger and noisier dataset.