Neural networks for the prediction organic chemistry reactions
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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.
Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Aug 2016.
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Citing papers per year: 2021: 2; 2022: 2; 2023: 3; 2024: 3; 2025: 2; 2026: 2.
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- 2021Non-Autoregressive Electron Redistribution Modeling for Reaction Predictioncited by 17
- 2022Machine Learning for a Sustainable Energy Futurecited by 14
- 2021Modern Hopfield Networks for Few- and Zero-Shot Reaction Template Predictioncited by 13
- 2025Graph Neural Networks in Modern AI-aided Drug Discoverycited by 12
- 2023Deep Learning Methods for Small Molecule Drug Discovery: A Surveycited by 10
- 2025Challenging reaction prediction models to generalize to novel chemistrycited by 6
- 2024Evaluating Molecule Synthesizability via Retrosynthetic Planning and Reaction Predictioncited by 5
- 2023A Unified View of Deep Learning for Reaction and Retrosynthesis Prediction: Current Status and Future Challengescited by 4
- 2022Accurate Molecular-Orbital-Based Machine Learning Energies via Unsupervised Clustering of Chemical Spacecited by 3
- 2024A Self-feedback Knowledge Elicitation Approach for Chemical Reaction Predictionscited by 2
- 2023Challenges for Predictive Modeling with Neural Network Techniques using Error-Prone Dietary Intake Datacited by 0
- 2024ReactAIvate: A Deep Learning Approach to Predicting Reaction Mechanisms and Unmasking Reactivity Hotspotscited by 0
- 2026SynLaD: Latent Diffusion for Generating Synthesizable Molecules Conditioned on 3D Pharmacophore Profiles1 Jul 2026
- 2026Computational framework for multistep metabolic pathway design15 Apr 2026
- 2025Graph Neural Networks in Modern AI-aided Drug Discovery7 Jun 2025
- 2025Challenging reaction prediction models to generalize to novel chemistry11 Jan 2025
- 2024Evaluating Molecule Synthesizability via Retrosynthetic Planning and Reaction Prediction13 Nov 2024
- 2024ReactAIvate: A Deep Learning Approach to Predicting Reaction Mechanisms and Unmasking Reactivity Hotspots14 Jul 2024
- 2024A Self-feedback Knowledge Elicitation Approach for Chemical Reaction Predictions15 Apr 2024
- 2023Challenges for Predictive Modeling with Neural Network Techniques using Error-Prone Dietary Intake Data15 Nov 2023
- 2023A Unified View of Deep Learning for Reaction and Retrosynthesis Prediction: Current Status and Future Challenges28 Jun 2023
- 2023Deep Learning Methods for Small Molecule Drug Discovery: A Survey1 Mar 2023
- 2022Machine Learning for a Sustainable Energy Future19 Oct 2022
- 2022Accurate Molecular-Orbital-Based Machine Learning Energies via Unsupervised Clustering of Chemical Space21 Apr 2022
- 2021Non-Autoregressive Electron Redistribution Modeling for Reaction Predictioncited by 17
- 2022Machine Learning for a Sustainable Energy Futurecited by 14
- 2021Modern Hopfield Networks for Few- and Zero-Shot Reaction Template Predictioncited by 13
- 2025Graph Neural Networks in Modern AI-aided Drug Discoverycited by 12
- 2023Deep Learning Methods for Small Molecule Drug Discovery: A Surveycited by 10
- 2025Challenging reaction prediction models to generalize to novel chemistrycited by 6
- 2024Evaluating Molecule Synthesizability via Retrosynthetic Planning and Reaction Predictioncited by 5
- 2023A Unified View of Deep Learning for Reaction and Retrosynthesis Prediction: Current Status and Future Challengescited by 4
- 2022Accurate Molecular-Orbital-Based Machine Learning Energies via Unsupervised Clustering of Chemical Spacecited by 3
- 2024A Self-feedback Knowledge Elicitation Approach for Chemical Reaction Predictionscited by 2
- 2023Challenges for Predictive Modeling with Neural Network Techniques using Error-Prone Dietary Intake Datacited by 0
- 2024ReactAIvate: A Deep Learning Approach to Predicting Reaction Mechanisms and Unmasking Reactivity Hotspotscited by 0
12 most cited of 13 All 13 (JSON)
- 2026Computational framework for multistep metabolic pathway design15 Apr 2026
- 2025Graph Neural Networks in Modern AI-aided Drug Discovery7 Jun 2025
- 2025Challenging reaction prediction models to generalize to novel chemistry11 Jan 2025
- 2024Evaluating Molecule Synthesizability via Retrosynthetic Planning and Reaction Prediction13 Nov 2024
- 2024ReactAIvate: A Deep Learning Approach to Predicting Reaction Mechanisms and Unmasking Reactivity Hotspots14 Jul 2024
- 2024A Self-feedback Knowledge Elicitation Approach for Chemical Reaction Predictions15 Apr 2024
- 2023Challenges for Predictive Modeling with Neural Network Techniques using Error-Prone Dietary Intake Data15 Nov 2023
- 2023A Unified View of Deep Learning for Reaction and Retrosynthesis Prediction: Current Status and Future Challenges28 Jun 2023
- 2023Deep Learning Methods for Small Molecule Drug Discovery: A Survey1 Mar 2023
- 2022Machine Learning for a Sustainable Energy Future19 Oct 2022
- 2022Accurate Molecular-Orbital-Based Machine Learning Energies via Unsupervised Clustering of Chemical Space21 Apr 2022
- 2021Non-Autoregressive Electron Redistribution Modeling for Reaction Prediction8 Jun 2021
12 newest of 13 All 13 (JSON)