Building Block-Based Binding Predictions for DNA-Encoded Libraries Article Swipe
Chris Zhang
,
Mary Pitman
,
Anjali Dixit
,
Sumudu P. Leelananda
,
Henri Palacci
,
Meghan Lawler
,
Svetlana Belyanskaya
,
LaShadric Grady
,
Joe Franklin
,
Nicolas Tilmans
,
David L. Mobley
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.26434/chemrxiv-2023-pq197
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.26434/chemrxiv-2023-pq197
DNA-encoded libraries (DELs) provide the means to make and screen millions of diverse compounds against a target of interest in a single experiment. However, despite producing large volumes of binding data at a relatively low cost, the DEL selection process is susceptible to noise, necessitating computational follow-up to increase signal-to-noise ratios. In this work, we present a set of informatics tools to analyze DEL selection data so that subsequent DEL screens probe productive regions of chemical space. Our approach segments DEL data at the individual building block level to identify productive building blocks in a library. We show how similar building blocks have a similar probability of binding, which we then employ to predict the behavior of untested building blocks. Lastly, we build a model from the inference that the combined behavior of individual building blocks is predictive of the activity of an overall compound. We report a performance of more than an order of magnitude greater than random guessing on a holdout set, demonstrating that our model can serve as a baseline for comparison against other machine learning models on DEL data.
Related Topics
Concepts
Block (permutation group theory)
Computer science
Inference
Selection (genetic algorithm)
Noise (video)
Set (abstract data type)
Process (computing)
Informatics
Model building
Data mining
Machine learning
Encoding (memory)
Synthetic data
Artificial intelligence
Mathematics
Engineering
Programming language
Quantum mechanics
Electrical engineering
Geometry
Physics
Image (mathematics)
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.26434/chemrxiv-2023-pq197
- https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/6438943f08c86922ffeffe57/original/building-block-based-binding-predictions-for-dna-encoded-libraries.pdf
- OA Status
- gold
- References
- 39
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4366088344
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4366088344Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.26434/chemrxiv-2023-pq197Digital Object Identifier
- Title
-
Building Block-Based Binding Predictions for DNA-Encoded LibrariesWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-04-17Full publication date if available
- Authors
-
Chris Zhang, Mary Pitman, Anjali Dixit, Sumudu P. Leelananda, Henri Palacci, Meghan Lawler, Svetlana Belyanskaya, LaShadric Grady, Joe Franklin, Nicolas Tilmans, David L. MobleyList of authors in order
- Landing page
-
https://doi.org/10.26434/chemrxiv-2023-pq197Publisher landing page
- PDF URL
-
https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/6438943f08c86922ffeffe57/original/building-block-based-binding-predictions-for-dna-encoded-libraries.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/6438943f08c86922ffeffe57/original/building-block-based-binding-predictions-for-dna-encoded-libraries.pdfDirect OA link when available
- Concepts
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Block (permutation group theory), Computer science, Inference, Selection (genetic algorithm), Noise (video), Set (abstract data type), Process (computing), Informatics, Model building, Data mining, Machine learning, Encoding (memory), Synthetic data, Artificial intelligence, Mathematics, Engineering, Programming language, Quantum mechanics, Electrical engineering, Geometry, Physics, Image (mathematics)Top concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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39Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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