Ligand-Based Compound Activity Prediction via Few-Shot Learning Article Swipe
Peter Eckmann
,
Jake Anderson
,
Rose Yu
,
Michael K. Gilson
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1021/acs.jcim.4c00485
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1021/acs.jcim.4c00485
Predicting the activities of new compounds against biophysical or phenotypic assays based on the known activities of one or a few existing compounds is a common goal in early stage drug discovery. This problem can be cast as a "few-shot learning" challenge, and prior studies have developed few-shot learning methods to classify compounds as active versus inactive. However, the ability to go beyond classification and rank compounds by expected affinity is more valuable. We describe Few-Shot Compound Activity Prediction (FS-CAP), a novel neural architecture trained on a large bioactivity data set to predict compound activities against an assay outside the training set, based on only the activities of a few known compounds against the same assay. Our model aggregates encodings generated from the known compounds and their activities to capture assay information and uses a separate encoder for the new compound whose activity is to be predicted. The new method provides encouraging results relative to traditional chemical-similarity-based techniques as well as other state-of-the-art few-shot learning methods in tests on a variety of ligand-based drug discovery settings and data sets. The code for FS-CAP is available at https://github.com/Rose-STL-Lab/FS-CAP.
Related Topics
Concepts
Drug discovery
Computer science
Artificial intelligence
Set (abstract data type)
Training set
Shot (pellet)
Encoder
Similarity (geometry)
Machine learning
Autoencoder
Data set
Deep learning
Computational biology
Chemistry
Bioinformatics
Biology
Organic chemistry
Operating system
Image (mathematics)
Programming language
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1021/acs.jcim.4c00485
- https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.4c00485
- OA Status
- hybrid
- Cited By
- 7
- References
- 40
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4400183011
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4400183011Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1021/acs.jcim.4c00485Digital Object Identifier
- Title
-
Ligand-Based Compound Activity Prediction via Few-Shot LearningWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-07-01Full publication date if available
- Authors
-
Peter Eckmann, Jake Anderson, Rose Yu, Michael K. GilsonList of authors in order
- Landing page
-
https://doi.org/10.1021/acs.jcim.4c00485Publisher landing page
- PDF URL
-
https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.4c00485Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
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https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.4c00485Direct OA link when available
- Concepts
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Drug discovery, Computer science, Artificial intelligence, Set (abstract data type), Training set, Shot (pellet), Encoder, Similarity (geometry), Machine learning, Autoencoder, Data set, Deep learning, Computational biology, Chemistry, Bioinformatics, Biology, Organic chemistry, Operating system, Image (mathematics), Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
7Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 5, 2024: 2Per-year citation counts (last 5 years)
- References (count)
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40Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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