Evidence map›Paper›PMID 40797112›Full record

ArticleActa pharmacologica Sinica2026

Identification of small-molecule inhibitors for GluN1/GluN3A NMDA receptors via a multiscale CNN-based prediction model.

Li Han, Yue Zeng, Zhi-Yan Qu, Sui Fang, Hai-Ying Wang, Ya-Shuo Dong, Xiang-Ming Zeng, Tong-Yan Zhang, Ze-Bin Yu, Ling Kang and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Acta pharmacologica Sinica, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Li Han *Software and Big Data Technology Department, Dalian Neusoft University of Information, Dalian, 116023, China.
Yue Zeng *State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China.
Zhi-Yan QuState Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China.
Sui FangState Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China.
Hai-Ying WangState Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China.
Ya-Shuo DongState Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China.
Xiang-Ming ZengSoftware and Big Data Technology Department, Dalian Neusoft University of Information, Dalian, 116023, China.
Tong-Yan ZhangSoftware and Big Data Technology Department, Dalian Neusoft University of Information, Dalian, 116023, China.
Ze-Bin YuSoftware and Big Data Technology Department, Dalian Neusoft University of Information, Dalian, 116023, China.
Ling KangNeusoft Research Institute, Dalian Neusoft University of Information, Dalian, 116023, China. kangling@neusoft.edu.cn.
Zhao-Bing GaoState Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China. zbgao@simm.ac.cn.
Quan GuoNeusoft Research Institute, Dalian Neusoft University of Information, Dalian, 116023, China. guoquan@neusoft.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

N-methyl-D-aspartate receptors (NMDARs) are critical mediators of excitatory neurotransmission and are composed of seven subunits (GluN1, GluN2A-D, and GluN3A-B) that form diverse receptor subtypes. While GluN1/GluN2 subtypes have been extensively characterized and have led to approved therapeutics, the GluN1/GluN3A subtype remains underexplored despite emerging evidence of its involvement in neuropsychiatric disorders. Efficient identification of modulators requires accurate prediction of drug-target affinity (DTA), particularly for challenging targets such as GluN1/GluN3A. In this study, we applied the ImageDTA model, which is a multiscale 2D convolutional neural network (CNN), to virtually screen 18 million small molecules for GluN1/GluN3A inhibitors. This artificial intelligence (AI)-driven approach prioritized 12 compounds, three of which demonstrated potent inhibitory activity (IC₅₀ < 30 µM) in experimental validation. The most potent hit, with an IC

Indexed as

Neural Networks, ComputerReceptors, N-Methyl-D-AspartateSmall Molecule LibrariesArtificial IntelligenceDrug DiscoveryHumansNerve Tissue ProteinsGRIN1 protein, humanNerve Tissue ProteinsReceptors, N-Methyl-D-AspartateSmall Molecule Librariesconvolutional neural networksdrug-target binding affinityGluN1/GluN3AImageDTAN-methyl-D-aspartate receptorsvirtual screening

Identifiers

PMID40797112
PMCPMC12764916

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.