Machine Learning‐Driven Discovery of GABA‐A Ligands with Pronounced Neuroprotective Efficacy and Resistance to P‐gp Efflux

Many studies have pointed to GABA‐A receptors as a promising therapeutic target for promoting recovery after stroke, owing to their neuroprotective efficacy and the enhancement of synaptic GABAergic currents. However, identifying nanomolar‐affinity ligands that evade P‐glycoprotein–mediated efflux can pose a significant challenge. To overcome this barrier, we developed a structure‐based machine learning workflow integrating molecular docking, which screened over 160 000 virtual analogs and identified eight synthetically accessible molecules. The synthesized compounds exhibited Ki values at the GABA‐A receptor ranging from 50 to 1600 nM, with the most promising being 4d (Ki = 62 ± 11 nM) and 4h (Ki = 50 ± 3 nM), both of which confirmed PAM efficacy at GABA‐A receptors. Both ligands exhibited neuroprotective activity by attenuating glutamate‐induced Ca2+ overload, preserving mitochondrial membrane potential and enhancing cell viability following oxygen–glucose deprivation in HT‐22 neurons. In MDR1‐MDCKII bidirectional assays, compound 4d (azetidine derivative) exhibited symmetric permeability (efflux ratio = 0.94), while 4h (bicyclic amino alcohol) was identified as a P‐gp substrate (efflux ratio = 2.04), suggesting that eliminating a single hydrogen‐bond donor at the amide tail is critical for minimizing efflux. Collectively, this study identifies compound 4d as a potent, low‐efflux GABA‐A receptor PAM with neuroprotective properties, supporting its further investigation.