Influence of conformation on GRIND-based three-dimensional quantitative structure-activity relationship (3D-QSAR)

Area of Science:

• Medicinal Chemistry
• Computational Chemistry
• Drug Discovery

Background:

• Quantitative Structure-Activity Relationship (QSAR) studies are crucial for drug design.
• GRIND-based 3D-QSAR methods can be sensitive to the flexibility of the dataset used.
• Oxidosqualene cyclase (OSC) is a validated target for therapeutic intervention.

Purpose of the Study:

• To evaluate the impact of dataset flexibility on GRIND-based 3D-QSAR models for OSC inhibitors.
• To identify the optimal conformational representation for developing robust QSAR models.
• To improve the predictive performance of QSAR models for drug discovery.

Main Methods:

• Conformational analysis was performed to generate molecular structures.
• A series of oxidosqualene cyclase (OSC) inhibitors were analyzed using GRIND-based 3D-QSAR.
• Statistical models were built and validated using independent test sets.
• Model performance was assessed using the predictive correlation coefficient (q2).

Main Results:

• Statistical models were found to be largely independent of the specific dataset.
• Outliers were consistently identified across different model variations.
• While test set predictions were acceptable, not all models exhibited strong predictive correlation (q2).
• The best predictive model was achieved using a dataset comprising only the lowest energy conformers.

Conclusions:

• The selection of molecular conformers significantly impacts the predictive power of 3D-QSAR models.
• Utilizing low-energy conformers is essential for developing reliable QSAR models for OSC inhibitors.
• This approach enhances the accuracy of computational drug design and discovery efforts.