Allen, T. E. H. | Search bioRxiv

bioRxiv2024

Physicochemical Principles Driving Small Molecule Binding to RNA

The possibility of using RNA-targeting small molecules to treat diseases is gaining traction as the next frontier of drug discovery and development. The chemical characteristics of small molecules that bind to RNA are still relatively poorly understood, particularly in comparison to protein-targeting small molecules. To fill this gap, we have generated an unprecedented amount of RNA-small molecule binding data, and used it to derive physicochemical rules of thumb that could be used to define areas of chemical space enriched for RNA binders - the Small molecules Targeting RNA (STaR) rules of thumb. These rules have been applied to publicly available RNA-small molecule datasets and found to be largely generalizable. Furthermore, a number of patented RNA-targeting compounds and FDA-approved compounds also pass these rules, as well as key RNA binding approved drug case studies including Risdiplam. We anticipate this work will significantly accelerate the exploration of the RNA-targeted chemical space, towards unlocking RNAs potential as a small molecule drug target. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=130 HEIGHT=200 SRC="FIGDIR/small/578268v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@15ced07org.highwire.dtl.DTLVardef@1cd891eorg.highwire.dtl.DTLVardef@e51599org.highwire.dtl.DTLVardef@1ecfa86_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2022

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 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.

biochemistry↗