AI-Driven Discovery and Optimization of Positive Allosteric Modulators for NMDA Receptors: Potential Applications in Depression

N-Methyl-d-aspartate receptors (NMDARs) are extensively distributed throughout the central nervous system (CNS), and their dysfunction is implicated in depressive disorder. Positive allosteric modulators (PAMs) enhance the receptor's sensitivity and activity to agonists without direct activation. In this study, using structure-based virtual screening and artificial intelligence (AI)-assisted optimization, we identified Y36, a benzene-substituted piperidinol derivative and potent GluN2A-selective PAM. Y36 showed higher efficacy (E_max = 397.7%) than GNE-3419 (E_max = 196.4%), reducing the EC₅₀ values and increasing the E_max values for glutamate/glycine at GluN2A receptor. In chronic restraint stress (CRS) mice, Y36 significantly alleviated depression-related behaviors in multiple behavioral assessments, highlighting its superior antidepressant effects. Preliminary studies also confirmed favorable pharmacokinetic (PK) profiles and blood-brain barrier (BBB) penetration for Y36, with no signs of addiction, weight gain, or organ and tissue damage in mice. These results suggest that Y36 offers promising potential as a novel antidepressant with multiple antidepressant-like properties.