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Updated: May 20, 2026

Methods for the Discovery of Novel Compounds Modulating a Gamma-Aminobutyric Acid Receptor Type A Neurotransmission
Published on: August 16, 2018
Pharmacophore models for GABA(A) modulators: implications in CNS drug discovery
Nanda Ghoshal1, R Suyambu Kesava Vijayan
1Indian Institute of Chemical Biology (A unit of CSIR), Structural Biology and Bioinformatics Division, 4, Raja S.C. Mullick Road, Kolkata-700032, India +91 33 2473 3491 ext. 854 ; +91 33 2473 5197 ; nghoshal@iicb.res.in.
Importance Of The Field:
GABA(A) ion channel is a validated drug target, implicated in the pathophysiology of various neurological and psychiatric disorders. Structural investigations on GABA(A) are currently precluded in the absence of experimentally resolved structure. Pharmacophore modeling circumvents such issues and proves to be a powerful and successful method in drug discovery.
Areas Covered In This Review:
The present reviews encompass pharmacophoric models available in the literature for the orthosteric GABA and the allosteric benzodiazepine binding site. Success stories from these simplistic pharmacophore models in scaffold hopping and strategic lead optimization have been highlighted. Recent advances in pharmacophore modeling that can leverage CNS drug discovery programs and deliver astounding results have been reviewed.
What The Reader Will Gain:
Readers are bound to gain a comprehensive insight on different computational techniques used by different groups to arrive at simple, yet sophisticated pharmacophore models. In the absence of experimentally unresolved active site geometry of GABA(A), these models will provide the reader an opportunity to translate these pharmacophoric features to the microscopic phenomenon of supramolecular ligand interaction.
Take Home Message:
Pharmacophore modeling has now evolved as a mainstay approach for lead generation and optimization in drug discovery programs. Of late, many advances in pharmacophore perception have emerged. Such advancements should be used to confront activity profiling and early stage risk assessment in a high-throughput fashion. Extending such technologies has the potential not only to reduce time and cost, but also to prevent late stage attrition in drug discovery.
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