Machine Learning Informs RNA‐Binding Chemical Space. (6th February 2023) - Yazdani, Kamyar; Jordan, Deondre; Yang, Mo; Fullenkamp, Christopher R.; Calabrese, David R.; Boer, Robert; Hilimire, Thomas; Allen, Timothy E. H.; Khan, Rabia T.; Schneekloth, John S.
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 reported R epository O f BI nders to N ucleic 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 were reported (including a total of 1 627 072 interactions assayed). A set of 2 003 RNA‐binding small molecules was identified, representing the largest fully public, experimentally derived library of its kind to date. Machine learning was 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. Abstract : RNA is increasingly considered a therapeutic target of interest for small molecules. We report an in‐depth analysis of 36 high throughput small molecule microarray screens against nucleic acid targets. Development of machine learning algorithms revealed a complex set of physicochemical properties that can distinguish between RNA‐ and protein‐binding drug‐like small molecules.