Predictive QSAR models development and validation for human ether-a-go-go related gene (hERG) blockers using newer...

Area of Science:

• Computational chemistry
• Medicinal chemistry
• Pharmacology

Background:

• Quantitative structure-activity relationship (QSAR) models are crucial for drug discovery.
• Validating QSAR models is essential for reliable predictions.
• Human ether-a-go-go-related gene (hERG) blockers are important drug targets.

Purpose of the Study:

• To derive active conformers using a pharmacophore-based method for physicochemical descriptor calculation.
• To validate quantitative structure-activity relationship models using distance-based approaches.
• To analyze the discriminant properties of molecules within the models.

Main Methods:

• Pharmacophore-based active conformer selection.
• Calculation of physicochemical descriptors.
• Validation of regression models using Q(2) values.
• Application of distance-based approaches, including Mahalanobis distance (MD).

Main Results:

• Significant regression models were developed and validated, yielding high Q(2) values.
• Mahalanobis distance (MD) values indicated that compounds with extreme hERG blocking activity had high MD.
• Compounds with high MD values exhibited fewer residual errors in predicted activity.
• Molecular descriptors suggest that bond flexibility facilitates π-π interactions with protein aromatic residues.

Conclusions:

• Distance-based approaches, particularly Mahalanobis distance, serve as a significant validation tool for QSAR models.
• The identified molecular descriptors provide insights into the mechanism of hERG channel blockade.
• This computational approach aids in understanding structure-activity relationships for drug design targeting hERG.