# Neural networks for the prediction organic chemistry reactions

arXiv: https://arxiv.org/abs/1608.06296 · HTML: https://mlsanity.com/paper/1608.06296/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Aug 2016.

## Built on by (most cited first, 14 of 14)

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- [Challenging reaction prediction models to generalize to novel chemistry](https://mlsanity.com/paper/2501.06669/) (2025)
- [Evaluating Molecule Synthesizability via Retrosynthetic Planning and Reaction Prediction](https://mlsanity.com/paper/2411.08306/) (2024)
- [A Unified View of Deep Learning for Reaction and Retrosynthesis Prediction: Current Status and Future Challenges](https://mlsanity.com/paper/2306.15890/) (2023)
- [Accurate Molecular-Orbital-Based Machine Learning Energies via Unsupervised Clustering of Chemical Space](https://mlsanity.com/paper/2204.09831/) (2022)
- [A Self-feedback Knowledge Elicitation Approach for Chemical Reaction Predictions](https://mlsanity.com/paper/2404.09606/) (2024)
- [Challenges for Predictive Modeling with Neural Network Techniques using Error-Prone Dietary Intake Data](https://mlsanity.com/paper/2311.09338/) (2023)
- [ReactAIvate: A Deep Learning Approach to Predicting Reaction Mechanisms and Unmasking Reactivity Hotspots](https://mlsanity.com/paper/2407.10090/) (2024)
- [Computational framework for multistep metabolic pathway design](https://mlsanity.com/paper/2604.13471/) (2026)
- [SynLaD: Latent Diffusion for Generating Synthesizable Molecules Conditioned on 3D Pharmacophore Profiles](https://mlsanity.com/paper/2607.01105/) (2026)

## Abstract (arXiv)

Reaction prediction remains one of the major challenges for organic chemistry, and is a pre-requisite for efficient synthetic planning. It is desirable to develop algorithms that, like humans, "learn" from being exposed to examples of the application of the rules of organic chemistry. We explore the use of neural networks for predicting reaction types, using a new reaction fingerprinting method. We combine this predictor with SMARTS transformations to build a system which, given a set of reagents and re- actants, predicts the likely products. We test this method on problems from a popular organic chemistry textbook.