Amit Michaeli*, Immanuel Lerner, Maria Zatsepin, Shaul Mezan and Alexandra Vardi Kilshtain Pages 5713 - 5719 ( 7 )
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.
Virtual Screening, structure-based drug design, peptides, chlorine channel, allosteric, in silico, reinforcement learning.
Department of Computational Chemistry, Pepticom Ltd, Jerusalem, Department of Computational Chemistry, Pepticom Ltd, Jerusalem, Department of Computational Chemistry, Pepticom Ltd, Jerusalem, Department of Computational Chemistry, Pepticom Ltd, Jerusalem, Department of Computational Chemistry, Pepticom Ltd, Jerusalem