RetroCaptioner: beyond attention in end-to-end retrosynthesis transformer via contrastively captioned learnable graph representation Article Swipe
Xiaoyi Liu
,
Chengwei Ai
,
Hongpeng Yang
,
Ruihan Dong
,
Jijun Tang
,
Shuangjia Zheng
,
Fei Guo
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1093/bioinformatics/btae561
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1093/bioinformatics/btae561
Motivation Retrosynthesis identifies available precursor molecules for various and novel compounds. With the advancements and practicality of language models, Transformer-based models have increasingly been used to automate this process. However, many existing methods struggle to efficiently capture reaction transformation information, limiting the accuracy and applicability of their predictions. Results We introduce RetroCaptioner, an advanced end-to-end, Transformer-based framework featuring a Contrastive Reaction Center Captioner. This captioner guides the training of dual-view attention models using a contrastive learning approach. It leverages learned molecular graph representations to capture chemically plausible constraints within a single-step learning process. We integrate the single-encoder, dual-encoder, and encoder–decoder paradigms to effectively fuse information from the sequence and graph representations of molecules. This involves modifying the Transformer encoder into a uni-view sequence encoder and a dual-view module. Furthermore, we enhance the captioning of atomic correspondence between SMILES and graphs. Our proposed method, RetroCaptioner, achieved outstanding performance with 67.2% in top-1 and 93.4% in top-10 exact matched accuracy on the USPTO-50k dataset, alongside an exceptional SMILES validity score of 99.4%. In addition, RetroCaptioner has demonstrated its reliability in generating synthetic routes for the drug protokylol. Availability and implementation The code and data are available at https://github.com/guofei-tju/RetroCaptioner.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/bioinformatics/btae561
- OA Status
- gold
- Cited By
- 11
- References
- 30
- Related Works
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- OpenAlex ID
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4402947492Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1093/bioinformatics/btae561Digital Object Identifier
- Title
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RetroCaptioner: beyond attention in end-to-end retrosynthesis transformer via contrastively captioned learnable graph representationWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-09-01Full publication date if available
- Authors
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Xiaoyi Liu, Chengwei Ai, Hongpeng Yang, Ruihan Dong, Jijun Tang, Shuangjia Zheng, Fei GuoList of authors in order
- Landing page
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https://doi.org/10.1093/bioinformatics/btae561Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1093/bioinformatics/btae561Direct OA link when available
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Computer science, Encoder, Transformer, Artificial intelligence, Graph, Natural language processing, Theoretical computer science, Engineering, Operating system, Voltage, Electrical engineeringTop concepts (fields/topics) attached by OpenAlex
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11Total citation count in OpenAlex
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2025: 11Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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