Machine Learning Informs RNA-Binding Chemical Space

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 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 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.

Automated summary

This study presents a new library of nucleic acid binders called ROBIN, which was identified through small molecule microarray (SMM) screening. The complete results of 36 SMM screens against a library of 24,572 small molecules were reported, resulting in the identification of 2,003 RNA-binding small molecules. Machine learning was used to develop predictive and interpretable models for characterizing these RNA-binding molecules. This work highlights the power of machine learning in informing the field of RNA-targeted chemistry.