A categorical structure-activity relationship analysis of GPR119 ligands

Area of Science:

• Computational Chemistry
• Medicinal Chemistry
• Pharmacology

Background:

• The categorical structure-activity relationship (cat-SAR) expert system has proven effective for analyzing chemical toxicity.
• G protein-coupled receptor 119 (GPR119) ligands are crucial therapeutic targets.

Purpose of the Study:

• To apply the cat-SAR expert system to model G protein-coupled receptor 119 (GPR119) ligands.
• To develop a predictive model for identifying potential GPR119 agonists.

Main Methods:

• Utilized a fragment-based approach to develop four distinct cat-SAR models for GPR119.
• Employed a leave-one-out validation strategy to assess model performance.
• Analyzed known GPR119 agonists and experimentally validated non-agonists.

Main Results:

• The best GPR119 cat-SAR model achieved 99% overall concordance, 99% sensitivity, and 100% specificity.
• Fragment analysis findings aligned with existing GPR119 structure-activity relationship (SAR) data.
• Demonstrated the successful application of the cat-SAR system to G protein-coupled receptor ligands.

Conclusions:

• Developed a highly predictive cat-SAR model for rapid screening of prospective GPR119 ligands.
• Highlighted the importance of considering the model's applicability domain.
• Established the utility of the cat-SAR expert system for modeling G protein-coupled receptor ligands, including therapeutic agents.