Radial distribution function descriptors: an alternative for predicting A2 A adenosine receptors agonists

Area of Science:

• Medicinal Chemistry
• Computational Chemistry
• Pharmacology

Background:

• Adenosine receptors, particularly A2A, are crucial drug targets.
• Developing selective agonists requires understanding structure-activity relationships.
• Existing computational methods have limitations in predicting ligand activity.

Purpose of the Study:

• To apply the Radial Distribution Function (RDF) approach to model A2A adenosine receptor agonists.
• To compare the predictive power of RDF with nine other computational descriptor sets.
• To identify key structural features influencing ligand affinity for the A2A receptor.

Main Methods:

• Utilized the Radial Distribution Function (RDF) approach for quantitative structure-activity relationship (QSAR) modeling.
• Investigated 29 adenosine analogues, including N6-arylcarbamoyl, 2-arylalkynyl-N6-arylcarbamoyl, and N6-carboxamido derivatives.
• Compared RDF model performance against nine alternative descriptor sets (e.g., Galvez Topological Charges, BCUT, WHIM).

Main Results:

• The RDF model successfully described approximately 85% of the variance in experimental activity.
• Alternative methods explained a maximum of 78% of the variance with an equivalent number of variables.
• The developed model highlights the significance of bulkiness and stereoselectivity in A2A receptor affinity.

Conclusions:

• The Radial Distribution Function approach provides a robust and accurate method for modeling A2A adenosine receptor agonists.
• RDF-based QSAR significantly outperforms other common descriptor types for this specific compound series.
• Structural bulkiness and stereochemical factors are critical determinants for high affinity to the A2A adenosine receptor.