Neural‐Symbolic Machine Learning for Retrosynthesis and Reaction Prediction
Abstract
Reaction prediction and retrosynthesis are the cornerstones of organic chemistry. Rule‐based expert systems have been the most widespread approach to computationally solve these two related challenges to date. However, reaction rules often fail because they ignore the molecular context, which leads to reactivity conflicts. Herein, we report that deep neural networks can learn to resolve reactivity conflicts and to prioritize the most suitable transformation rules. We show that by training our model on 3.5 million reactions taken from the collective published knowledge of the entire discipline of chemistry, our model exhibits a top10‐accuracy of 95 % in retrosynthesis and 97 % for reaction prediction on a validation set of almost 1 million reactions.
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- Authors
- Segler, M. H. S., & Waller, M. P.
- Affiliations
- University of Münster (DE)
- Shanghai University (CN)
- Corresponding author
- Waller, Mark P.
- Venue
- Chemistry – A European Journal
- Publisher
- Wiley
- Published
- 2017-02-22
- Volume
- 23
- Issue
- 25
- Pages
- 5966-5971
- ISSN
- 0947-6539 (ISSN-L)
- 1521-3765
- OpenAlex
- W2580919858
- Cited by
- 548
- References
- 30
- Citations by year
- 2026: 29 · 2025: 58 · 2024: 66 · 2023: 77 · 2022: 54 · 2021: 87 · 2020: 76 · 2019: 56 · 2018: 33 · 2017: 12
- FWCI
- 36.50
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Chemistry - A European Journal (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Deutsche Forschungsgemeinschaft (SFB858 - Z1)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Chemistry › Physical and Theoretical Chemistry › History and advancements in chemistry
- Keywords
- retrosynthesis, chemical reaction prediction, organic chemistry, transformation rules, rule prioritization, deep neural network, chemical reaction data
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