Discovery of Novel GABAAR Allosteric Modulators Through Reinforcement Learning

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 gamma-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.