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.
[http://dx.doi.org/10.2174/1381612821666150914121624] [PMID: 26365137]
[http://dx.doi.org/10.3389/fnmol.2016.00051] [PMID: 27445687]