RT Journal Article
SR Electronic
T1 Machine Learning Informs RNA-Binding Chemical Space
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2022.08.01.502065
DO 10.1101/2022.08.01.502065
A1 Kamyar Yazdani
A1 Deondre Jordan
A1 Mo Yang
A1 Christopher R. Fullenkamp
A1 Timothy E. H. Allen
A1 Rabia T. Khan
A1 John S. Schneekloth, Jr.
YR 2022
UL http://biorxiv.org/content/early/2022/08/01/2022.08.01.502065.abstract
AB 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 StatementThe 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.