Retrosynthetic Reaction Prediction Using Neural Sequence-to-Sequence Models
Open accessAbstract
We describe a fully data driven model that learns to perform a retrosynthetic reaction prediction task, which is treated as a sequence-to-sequence mapping problem. The end-to-end trained model has an encoder-decoder architecture that consists of two recurrent neural networks, which has previously shown great success in solving other sequence-to-sequence prediction tasks such as machine translation. The model is trained on 50,000 experimental reaction examples from the United States patent literature, which span 10 broad reaction types that are commonly used by medicinal chemists. We find that our model performs comparably with a rule-based expert system baseline model, and also overcomes certain limitations associated with rule-based expert systems and with any machine learning approach that contains a rule-based expert system component. Our model provides an important first step toward solving the challenging problem of computational retrosynthetic analysis.
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Sequence-to-sequence models applied to chemical reactions provided similar performance (ca 37% for top-1) to rule-based systems (35%).
“This methodology was applied to model reaction products and for retrosynthetic reaction route prediction, which provided similar performance (ca 37% for top-1) to rule-based systems (35%).140”
From QSAR without borders (2020) · cites this paper as [140]
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- Authors
- Liu, B., Ramsundar, B., Kawthekar, P., Shi, J., Gomes, J., Luu Nguyen, Q., Ho, S., Sloane, J., Wender, P., & Pande, V.
- Affiliations
- Stanford University (US)
- Department of Chemistry, Stanford University, Stanford, California 94305, United States
- Corresponding author
- Pande, Vijay
- ORCID
- Venue
- ACS Central Science
- Publisher
- American Chemical Society (ACS)
- Published
- 2017-09-05
- Volume
- 3
- Issue
- 10
- Pages
- 1103-1113
- ISSN
- 2374-7943 (ISSN-L)
- 2374-7951
- PMC
- PMC5658761
- OpenAlex
- W2621742623
- Cited by
- 536
- References
- 64
- Citations by year
- 2026: 32 · 2025: 67 · 2024: 79 · 2023: 81 · 2022: 58 · 2021: 70 · 2020: 69 · 2019: 46 · 2018: 31 · 2017: 3
- FWCI
- 30.58
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- ACS Central Science (publishedVersion, other-oa)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- arXiv (Cornell University)
- Funders
- Division of Chemistry (CHE1265956)
- National Institute of Allergy and Infectious Diseases (U19 AI109662)
- American Cancer Society (PF-15-007-01-CDD)
- Hertz Foundation
- National Institute of General Medical Sciences (R01 GM062868)
- 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, sequence-to-sequence models, encoder-decoder architecture, recurrent neural networks, patent literature, rule-based expert systems, medicinal chemistry
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