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Machine Learning Informs RNA-Binding Chemical Space

Kamyar Yazdani, Deondre Jordan, Mo Yang, Christopher R. Fullenkamp, Timothy E. H. Allen, Rabia T. Khan, John S. Schneekloth Jr.
doi: https://doi.org/10.1101/2022.08.01.502065
Kamyar Yazdani
1Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, Frederick, MD 21702-1201, USA
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Deondre Jordan
1Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, Frederick, MD 21702-1201, USA
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Mo Yang
1Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, Frederick, MD 21702-1201, USA
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Christopher R. Fullenkamp
1Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, Frederick, MD 21702-1201, USA
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Timothy E. H. Allen
2Ladder Therapeutics, USA
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Rabia T. Khan
2Ladder Therapeutics, USA
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John S. Schneekloth Jr.
1Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, Frederick, MD 21702-1201, USA
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  • For correspondence: schneeklothjs@mail.nih.gov
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Abstract

Small molecule targeting of RNA has emerged as a new frontier in medicinal chemistry, but compared to the protein targeting literature our understanding of chemical matter that binds to RNA is limited. In this study, we report Repository Of BInders to Nucleic acids (ROBIN), a new library of nucleic acid binders identified by small molecule microarray (SMM) screening. The complete results of 36 individual nucleic acid SMM screens against a library of 24,572 small molecules are reported (including a total of 1,627,072 interactions assayed). A set of 2,003 RNA-binding small molecules is identified, representing the largest fully public, experimentally derived library of its kind to date. Machine learning is used to develop highly predictive and interpretable models to characterize RNA-binding molecules. This work demonstrates that machine learning algorithms applied to experimentally derived sets of RNA binders are a powerful method to inform RNA-targeted chemical space.

Competing Interest Statement

The authors declare the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: T.E.H.A. and R.K. are current employees of Ladder Therapeutics Inc. and may hold stock or other financial interests in Ladder Therapeutics Inc.

Footnotes

  • https://doi.org/10.6084/m9.figshare.20401974

  • https://github.com/ky66/ROBIN

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted August 01, 2022.
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Machine Learning Informs RNA-Binding Chemical Space
Kamyar Yazdani, Deondre Jordan, Mo Yang, Christopher R. Fullenkamp, Timothy E. H. Allen, Rabia T. Khan, John S. Schneekloth Jr.
bioRxiv 2022.08.01.502065; doi: https://doi.org/10.1101/2022.08.01.502065
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Machine Learning Informs RNA-Binding Chemical Space
Kamyar Yazdani, Deondre Jordan, Mo Yang, Christopher R. Fullenkamp, Timothy E. H. Allen, Rabia T. Khan, John S. Schneekloth Jr.
bioRxiv 2022.08.01.502065; doi: https://doi.org/10.1101/2022.08.01.502065

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