摘要The GluN1/GluN3A receptor,a unique excitatory glycine receptor recently identified in the central nervous system,challenges traditional perspectives of N-methyl-D-aspartate(NMDA)receptor diversity and glycinergic signaling.Its role in emotional regulation positions it as a potential therapeutic target for neuropsychiatric disorders.However,pharmacological research on GluN1/GluN3A receptors remains at an early stage.Traditional high-throughput screening methods for ion channel drug discovery often lack efficiency,particularly when applied to large compound libraries.To address this concern,we designed a deep learning-based strategy that balances efficiency and accuracy for identifying GluN1/GluN3A inhibitors.First,a sequence-based scoring function was developed to rapidly screen a library containing 18 million compounds,reducing the pool to approximately 105 candidates.Next,two complex-based scoring functions,IGModel and RTMScore,were employed to precisely score and rank the remaining candidates.Finally,an active molecule with an IC50 of 2.87±0.80 μM for the GluN1/GluN3A receptor was confirmed through whole-cell voltage-clamp electrophysiology.This study also presents a paradigm for integrating deep learning into rapid and precise high-throughput screening.