Recognizing the power of machine learning and other computational methods to accelerate progress in small molecule targeting of RNA

RNAstructuresregulateawiderangeofprocessesinbiologyanddisease,yetsmallmoleculechemicalprobesordrugsthat can modulate these functions are rare. Machine learning and other computational methods are well poised to fill gaps in knowledgeandovercometheinherentchallengesinRNAtargeting,suchasthedynamicnatureofRNAandthedifficultyof obtainingRNAhigh-resolutionstructures.Successfultoolstodateincludeprincipalcomponentanalysis,lineardiscriminate analysis, k-nearest neighbor, artificial neural networks, multiple linear regression, and many others. Employment of these tools has revealed critical factors for selective recognition in RNA:small molecule complexes, predictable differences in RNA- and protein-binding ligands, and quantitative structure activity relationships that allow the rational design of small molecules for a given RNA target. Herein we present our perspective on the value of using machine learning and other computation methods to advance RNA:small molecule targeting, including select examples and their validation as well as necessary and promising future directions that will be key to accelerate discoveries in this important field.