Adapting protein language models for rapid DTI prediction Article Swipe
Samuel Sledzieski
,
Rohit Singh
,
Lenore Cowen
,
Bonnie Berger
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1101/2022.11.03.515084
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1101/2022.11.03.515084
We consider the problem of sequence-based drug-target interaction (DTI) prediction, showing that a straightforward deep learning architecture that leverages pre-trained protein language models (PLMs) for protein embedding outperforms state of the art approaches, achieving higher accuracy, expanded generalizability, and an order of magnitude faster training. PLM embeddings are found to contain general information that is especially useful in few-shot (small training data set) and zero-shot instances (unseen proteins or drugs). Additionally, the PLM embeddings can be augmented with features tuned by task-specific pre-training, and we find that these task-specific features are more informative than baseline PLM features. We anticipate such transfer learning approaches will facilitate rapid prototyping of DTI models, especially in low-N scenarios.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2022.11.03.515084
- https://www.biorxiv.org/content/biorxiv/early/2022/11/04/2022.11.03.515084.full.pdf
- OA Status
- green
- Cited By
- 10
- References
- 22
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4308268814
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4308268814Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2022.11.03.515084Digital Object Identifier
- Title
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Adapting protein language models for rapid DTI predictionWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-11-04Full publication date if available
- Authors
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Samuel Sledzieski, Rohit Singh, Lenore Cowen, Bonnie BergerList of authors in order
- Landing page
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https://doi.org/10.1101/2022.11.03.515084Publisher landing page
- PDF URL
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https://www.biorxiv.org/content/biorxiv/early/2022/11/04/2022.11.03.515084.full.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://www.biorxiv.org/content/biorxiv/early/2022/11/04/2022.11.03.515084.full.pdfDirect OA link when available
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Generalizability theory, Computer science, Task (project management), Embedding, Artificial intelligence, Set (abstract data type), Machine learning, Transfer of learning, Training set, Engineering, Systems engineering, Mathematics, Statistics, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
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10Total citation count in OpenAlex
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2025: 2, 2024: 4, 2023: 2, 2022: 2Per-year citation counts (last 5 years)
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22Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.models, | 110 |
| abstract_inverted_index.problem | 4 |
| abstract_inverted_index.protein | 21, 26 |
| abstract_inverted_index.showing | 11 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.baseline | 95 |
| abstract_inverted_index.consider | 2 |
| abstract_inverted_index.expanded | 37 |
| abstract_inverted_index.features | 79, 90 |
| abstract_inverted_index.few-shot | 59 |
| abstract_inverted_index.language | 22 |
| abstract_inverted_index.learning | 16, 102 |
| abstract_inverted_index.proteins | 68 |
| abstract_inverted_index.training | 61 |
| abstract_inverted_index.transfer | 101 |
| abstract_inverted_index.accuracy, | 36 |
| abstract_inverted_index.achieving | 34 |
| abstract_inverted_index.augmented | 77 |
| abstract_inverted_index.embedding | 27 |
| abstract_inverted_index.features. | 97 |
| abstract_inverted_index.instances | 66 |
| abstract_inverted_index.leverages | 19 |
| abstract_inverted_index.magnitude | 43 |
| abstract_inverted_index.training. | 45 |
| abstract_inverted_index.zero-shot | 65 |
| abstract_inverted_index.anticipate | 99 |
| abstract_inverted_index.approaches | 103 |
| abstract_inverted_index.embeddings | 47, 74 |
| abstract_inverted_index.especially | 56, 111 |
| abstract_inverted_index.facilitate | 105 |
| abstract_inverted_index.scenarios. | 114 |
| abstract_inverted_index.approaches, | 33 |
| abstract_inverted_index.drug-target | 7 |
| abstract_inverted_index.information | 53 |
| abstract_inverted_index.informative | 93 |
| abstract_inverted_index.interaction | 8 |
| abstract_inverted_index.outperforms | 28 |
| abstract_inverted_index.pre-trained | 20 |
| abstract_inverted_index.prediction, | 10 |
| abstract_inverted_index.prototyping | 107 |
| abstract_inverted_index.architecture | 17 |
| abstract_inverted_index.Additionally, | 71 |
| abstract_inverted_index.pre-training, | 83 |
| abstract_inverted_index.task-specific | 82, 89 |
| abstract_inverted_index.sequence-based | 6 |
| abstract_inverted_index.straightforward | 14 |
| abstract_inverted_index.generalizability, | 38 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 94 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.85768813 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |