Discovery of Novel GABAAR Allosteric Modulators Through Reinforcement Learning

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