QuoteTarget : A sequence‐based transformer protein language model to identify potentially druggable protein targets
Article Swipe
Jiaxiao Chen
,
Zhonghui Gu
,
Youjun Xu
,
Minghua Deng
,
Luhua Lai
,
Jianfeng Pei
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1002/pro.4555
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1002/pro.4555
The development of efficient computational methods for drug target protein identification can compensate for the high cost of experiments and is therefore of great significance for drug development. However, existing structure‐based drug target protein‐identification algorithms are limited by the insufficient number of proteins with experimentally resolved structures. Moreover, sequence‐based algorithms cannot effectively extract information from protein sequences and thus display insufficient accuracy. Here, we combined the sequence‐based self‐supervised pretraining protein language model ESM1b with a graph convolutional neural network classifier to develop an improved, sequence‐based drug target protein identification method. This complete model, named QuoteTarget, efficiently encodes proteins based on sequence information alone and achieves an accuracy of 95% with the nonredundant drug target and nondrug target datasets constructed for this study. When applied to all proteins from Homo sapiens , QuoteTarget identified 1213 potential undeveloped drug target proteins. We further inferred residue‐binding weights from the well‐trained network using the gradient‐weighted class activation mapping (Grad–Cam) algorithm. Notably, we found that without any binding site information input, significant residues inferred by the model closely match the experimentally confirmed drug molecule‐binding sites. Thus, our work provides a highly effective sequence‐based identifier for drug target proteins, as well to yield new insights into recognizing drug molecule‐binding sites. The entire model is available at https://github.com/Chenjxjx/drug-target-prediction .
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1002/pro.4555
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/pro.4555
- OA Status
- bronze
- Cited By
- 28
- References
- 55
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4312129273
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Raw OpenAlex JSON
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https://openalex.org/W4312129273Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1002/pro.4555Digital Object Identifier
- Title
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QuoteTarget : A sequence‐based transformer protein language model to identify potentially druggable protein targetsWork title - Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
-
2022-12-24Full publication date if available
- Authors
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Jiaxiao Chen, Zhonghui Gu, Youjun Xu, Minghua Deng, Luhua Lai, Jianfeng PeiList of authors in order
- Landing page
-
https://doi.org/10.1002/pro.4555Publisher landing page
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/pro.4555Direct link to full text PDF
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YesWhether a free full text is available
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bronzeOpen access status per OpenAlex
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/pro.4555Direct OA link when available
- Concepts
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Druggability, Computational biology, Computer science, Classifier (UML), Drug discovery, Protein sequencing, Sequence (biology), Drug development, Identification (biology), Artificial intelligence, Peptide sequence, Bioinformatics, Drug, Biology, Biochemistry, Gene, Botany, PharmacologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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28Total citation count in OpenAlex
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2025: 12, 2024: 8, 2023: 7, 2022: 1Per-year citation counts (last 5 years)
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55Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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