Quantitative Structure–Activity Relationship (QSAR) Study Predicts Small-Molecule Binding to RNA Structure

The diversity of
RNA structural elements and their documented role
in human diseases make RNA an attractive therapeutic target. However,
progress in drug discovery and development has been hindered by challenges
in the determination of high-resolution RNA structures and a limited
understanding of the parameters that drive RNA recognition by small
molecules, including a lack of validated quantitative structure–activity
relationships (QSARs). Herein, we develop QSAR models that quantitatively
predict both thermodynamic- and kinetic-based binding parameters of
small molecules and the HIV-1 transactivation response (TAR) RNA model
system. Small molecules bearing diverse scaffolds were screened against
TAR using surface plasmon resonance. Multiple linear regression (MLR)
combined with feature selection afforded robust models that allowed
direct interpretation of the properties critical for both binding
strength and kinetic rate constants. These models were validated with
new molecules, and their accurate performance was confirmed via comparison
to ensemble tree methods, supporting the general applicability of
this platform.