Pinned Repositories
rnamigos2
Rapid structure-based virtual screening for RNA targets.
QSAR
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 (QSAR). Herein, we developed QSAR models that quantitatively predict both thermodynamic and kinetic-based binding parameters of small molecules and the HIV-1 TAR model system. Small molecules bearing diverse scaffolds was screened against the HIV-1 TAR using surface plasmon resonance. Then multiple linear regression (MLR) combined with feature selection was performed to afford robust models that allowed direct interpretation of properties critical for both binding strength and kinetic rate constants. These models were externally validated with new molecules and their accurate performance confirmed via comparison to ensemble tree methods.
QSAR-DMZ
QSAR2.0_CJTVAE
hargrove-lab's Repositories
hargrove-lab/QSAR
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 (QSAR). Herein, we developed QSAR models that quantitatively predict both thermodynamic and kinetic-based binding parameters of small molecules and the HIV-1 TAR model system. Small molecules bearing diverse scaffolds was screened against the HIV-1 TAR using surface plasmon resonance. Then multiple linear regression (MLR) combined with feature selection was performed to afford robust models that allowed direct interpretation of properties critical for both binding strength and kinetic rate constants. These models were externally validated with new molecules and their accurate performance confirmed via comparison to ensemble tree methods.
hargrove-lab/QSAR-DMZ
hargrove-lab/QSAR2.0_CJTVAE