Simultaneous determination of bioactive conformations and alignment rules by multi-way PLS modeling

Area of Science:

• Computational chemistry
• Medicinal chemistry
• Drug discovery

Background:

• Quantitative Structure-Activity Relationship (QSAR) methods are crucial for predicting drug efficacy.
• Traditional QSAR methods often struggle with conformational flexibility and defining optimal alignment rules.
• Accurate selection of bioactive conformations and alignment is essential for reliable 3D-QSAR models.

Purpose of the Study:

• To develop a novel three-dimensional quantitative structure-activity relationship (3D-QSAR) method.
• To simultaneously select bioactive conformations and the alignment rule for molecular modeling.
• To improve the accuracy and reliability of QSAR predictions.

Main Methods:

• Conformational analysis to generate possible molecular structures.
• Superposition of generated conformers onto a template with various alignment rules.
• Calculation of 3D field variables as structural descriptors.
• Application of four-way partial least-squares (PLS) analysis for model building.
• Validation using a dataset of benzodiazepine derivatives (CCK-B antagonists).

Main Results:

• Successful selection of appropriate conformers and alignment rules.
• Development of a significant PLS model correlating structure with biological activity.
• Generation of reasonable 3D coefficient contour maps.
• High prediction accuracy demonstrated through external validation.

Conclusions:

• The proposed 3D-QSAR method effectively identifies bioactive conformations and alignment rules.
• The method provides a significant improvement in developing predictive QSAR models.
• This approach enhances the ability to predict the activity of new chemical entities, such as CCK-B antagonists.