The ?-Aminobutyric Acid (GABA)A receptor is one of the most complicated superfamily of ligand-gated ion channels and is responsible for anxiety and sleep disorders. Current computational GABAA receptor ligand models typically focus on one binding site and do not include information about the GABAergic mechanism. To design better anti-anxiety and hypnotic drugs, the various GABAA receptor binding sites and mechanism needs to be considered. We developed various GABAA receptor Quantitative Structure Activity Relationship (QSAR) models for diverse classes of agonists, antagonists, allosteric modulators, and ion channel inhibitors. By combining various modeling approaches with different types of chemical descriptors, the resulting QSAR models are externally predictive and cover most GABAA binding sites and allosteric sites. The potential GABAA receptor ligands can be revealed after applying these models and screening an external chemical library. Furthermore, the relevant GABAergic mechanism for each active hit will be revealed by studying the virtual profiles generated by the models. This research project will lead to the discovery of potential anti-anxiety and hypnotic ligands and provide mechanism profiles for future experimental tests.

Computational profiling of the binding mechanisms of GABA<sub>A</sub> receptor ligands

246th National Meeting (2013)

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.

Computational profiling of the binding mechanisms of GABAA receptor ligands

Computational profiling of the binding mechanisms of GABA_A receptor ligands