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Current Pharmaceutical Design

Editor-in-Chief

ISSN (Print): 1381-6128
ISSN (Online): 1873-4286

Research Article

Discovery of Novel GABAAR Allosteric Modulators Through Reinforcement Learning

Author(s): Amit Michaeli*, Immanuel Lerner, Maria Zatsepin, Shaul Mezan and Alexandra Vardi Kilshtain

Volume 26 , Issue 44 , 2020

Page: [5713 - 5719] Pages: 7

DOI: 10.2174/1381612826666201113104150

Price: $65

Abstract

Background: As not all target proteins can be easily screened in vitro, advanced virtual screening is becoming critical.

Objective: In this study, we demonstrate the application of reinforcement learning guided virtual screening for γ-aminobutyric acid A receptor (GABAAR) modulating peptides.

Methods: Structure-based virtual screening was performed on a receptor homology model. Screened molecules deemed to be novel were synthesized and analyzed using patch-clamp analysis.

Results: 13 molecules were synthesized and 11 showed positive allosteric modulation, with two showing 50% activation at the low micromolar range.

Conclusion: Reinforcement learning guided virtual screening is a viable method for the discovery of novel molecules that modulate a difficult to screen transmembrane receptor.

Keywords: Virtual Screening, structure-based drug design, peptides, chlorine channel, allosteric, in silico, reinforcement learning.

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