Refinement of Catalyst hypotheses using simplex optimisation.

Area of Science:

• Computational chemistry
• Medicinal chemistry
• Drug discovery

Background:

• 3D Quantitative Structure-Activity Relationship (QSAR) models are crucial for predicting drug efficacy.
• Hypothesis generation and optimization are key steps in developing accurate QSAR models.
• Existing methods may not fully leverage structural information for improved predictions.

Purpose of the Study:

• To optimize and expand 3D QSAR Catalyst hypotheses using advanced computational methods.
• To evaluate the impact of hypothesis optimization on model predictivity across different biological targets.
• To investigate the benefit of incorporating receptor geometry into QSAR hypothesis generation.

Main Methods:

• Utilized the HypoOpt program in conjunction with the MSI citest program for hypothesis optimization.
• Employed simplex optimization coupled with cross-validation (leave-one-out) for model refinement.
• Applied three diverse datasets: angiotensin converting enzyme inhibition, squalene epoxidase inhibition, and HIV protease inhibition.

Main Results:

• Simplex optimization with cross-validation led to improved QSAR models with enhanced predictivity for external test sets.
• Incorporating Catalyst 'excluded volume' features, representing active site geometry for HIV protease inhibitors, significantly improved hypothesis predictivity.
• Optimized hypotheses demonstrated superior predictive performance compared to those developed without receptor information.

Conclusions:

• The HypoOpt and citest programs effectively optimize 3D QSAR hypotheses, enhancing predictive accuracy.
• Leveraging receptor geometry in QSAR modeling, particularly for enzyme inhibitors, substantially improves predictive power.
• This approach offers a valuable strategy for accelerating drug discovery through more accurate virtual screening and lead optimization.