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 (Emax = 397.7%) than GNE-3419 (Emax = 196.4%), reducing the EC50 values and increasing the Emax 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.