Quantitative Structure-Activity Relationship (QSAR) Study PredictsSmall-Molecule Binding to RNA Structure

The diversity of RNA structural elements and theirdocumented role in human diseases make RNA an attractive therapeutictarget. However, progress in drug discovery and development has beenhindered by challenges in the determination of high-resolution RNAstructures and a limited understanding of the parameters that drive RNArecognition by small molecules, including a lack of validated quantitativestructure-activity relationships (QSARs). Herein, we develop QSARmodels that quantitatively predict both thermodynamic- and kinetic-basedbinding parameters of small molecules and the HIV-1 transactivationresponse (TAR) RNA model system. Small molecules bearing diversescaffolds were screened against TAR using surface plasmon resonance.Multiple linear regression (MLR) combined with feature selection affordedrobust models that allowed direct interpretation of the properties critical forboth binding strength and kinetic rate constants. These models were validated with new molecules, and their accurate performancewas confirmed via comparison to ensemble tree methods, supporting the general applicability of this platform

Automated summary

The determination of high-resolution RNA structures and the recognition of RNA by small molecules have been challenging in the field of RNA therapeutics. In this study, QSAR models were developed to predict both thermodynamic and kinetic binding parameters of small molecules and HIV-1 TAR RNA. These models allowed for the direct interpretation of critical properties for binding strength and kinetic rate constants and were validated and compared to other methods, demonstrating their accuracy and general applicability.