<p>GluN3A-containing NMDA receptors have recently emerged as promising therapeutic targets for neurological disorders. However, discovering potent modulators remains a significant challenge, primarily due to the limitations of traditional high-throughput screening methods. In this study, we introduce a novel drug-target affinity prediction method, CLG-DTA, designed to enhance drug discovery for the GluN1/GluN3A receptor. This graph contrastive learning-based method incorporates natural language supervision by transforming regression labels into textual representation, and integrating them with traditional affinity data to enhance molecular representation. Additionally, a numerical knowledge graph is employed to refine continuous text embeddings, enabling precise modeling of complex drug-target interactions across diverse data modalities. Using CLG-DTA, we screened a library of 18 million compounds and identified 12 candidates for experimental validation. Among them, five compounds exhibited significant activity, with Boeravinone E demonstrating the highest potency (<InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="41401_2025_1580_Article_IEq1.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="31" /> </InlineMediaObject> <EquationSource Format="TEX">${{{\rm{IC}}}}_{50}$</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mrow> <mi mathvariant="normal">IC</mi> </mrow> <mrow> <mn>50</mn> </mrow> </msub> </math></EquationSource> </InlineEquation> = 3.40 <InlineEquation ID="IEq2"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="41401_2025_1580_Article_IEq2.gif" Format="GIF" Height="13" Rendition="HTML" Resolution="72" Type="Linedraw" Width="19" /> </InlineMediaObject> <EquationSource Format="TEX">$\pm$</EquationSource> <EquationSource Format="MATHML"><math> <mo>±</mo> </math></EquationSource> </InlineEquation> 0.91 μM). These findings highlight the potential of CLG-DTA in accelerating the identification of promising GluN1/GluN3A modulators and lay a robust foundation for future therapeutic development.</p>

Contrastive learning-based drug screening model for GluN1/GluN3A inhibitors

期刊论文
发表日期：

2025年6月6日

查看全文

Kun Li,
Yue Zeng,
Yi-da Xiong,
Hao-chen Wu,
Sui Fang,
Zhi-yan Qu,
Yan Zhu,
Bo Du,
Zhao-bing Gao,
Wen-bin Hu

摘要

GluN3A-containing NMDA receptors have recently emerged as promising therapeutic targets for neurological disorders. However, discovering potent modulators remains a significant challenge, primarily due to the limitations of traditional high-throughput screening methods. In this study, we introduce a novel drug-target affinity prediction method, CLG-DTA, designed to enhance drug discovery for the GluN1/GluN3A receptor. This graph contrastive learning-based method incorporates natural language supervision by transforming regression labels into textual representation, and integrating them with traditional affinity data to enhance molecular representation. Additionally, a numerical knowledge graph is employed to refine continuous text embeddings, enabling precise modeling of complex drug-target interactions across diverse data modalities. Using CLG-DTA, we screened a library of 18 million compounds and identified 12 candidates for experimental validation. Among them, five compounds exhibited significant activity, with Boeravinone E demonstrating the highest potency ( ${{{\rm{IC}}}}_{50}$ ${IC}_{50}$ = 3.40 $\pm$ $\pm$ 0.91 μM). These findings highlight the potential of CLG-DTA in accelerating the identification of promising GluN1/GluN3A modulators and lay a robust foundation for future therapeutic development.