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An Attention-Gated Graph Spiking Neural Membrane System for Structure-Activity Relationship Prediction.

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Title: An Attention-Gated Graph Spiking Neural Membrane System for Structure-Activity Relationship Prediction.
Authors: Fu, Jun1 (AUTHOR), Zhang, Jianyi1 (AUTHOR) zjy@besti.edu.cn, Peng, Hong2 (AUTHOR), Zhang, Wenxuan3 (AUTHOR), Han, Kaiying4 (AUTHOR), Hei, Xiali5 (AUTHOR)
Source: International Journal of Neural Systems. Aug2026, Vol. 36 Issue 8, p1-19. 19p.
Subjects: Structure-activity relationships, Graph neural networks, Classification algorithms, Dynamic models, Biologically inspired computing, Artificial neural networks, Cheminformatics
Abstract: Spiking Neural P (SNP) systems have attracted increasing attention due to their biologically inspired, event-driven computation and inherent capability for temporal modeling. However, most existing SNP variants rely on fixed or purely local information propagation mechanisms, which limits their ability to capture long-range dependencies and contextual interactions in complex structured data. To address this limitation, we propose an Attention-Gated Spiking Neural membrane system (AGSNP), which incorporates an attention-guided gating mechanism directly into the spiking neuron dynamics. Unlike prior SNP models that treat attention as an external aggregation operation, AGSNP embeds attention signals into the nonlinear spiking update and memory regulation process. This design enables adaptive information propagation across distant structural components while preserving biologically inspired spiking behavior. To evaluate the effectiveness of the proposed architecture, AGSNP is instantiated within a graph-based learning framework and applied to Structure Activity Relationship (SAR) prediction. Experiments on three publicly available benchmark datasets demonstrate that AGSNP consistently outperforms representative baseline methods. Notably, under limited data availability and severe class imbalance, the proposed model achieves improvements of approximately 2.0–5.7% in AUC and related metrics on the Tox21 dataset, and 3.5–17.0% on the MUV dataset. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Neural Systems is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 193121369
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: An Attention-Gated Graph Spiking Neural Membrane System for Structure-Activity Relationship Prediction.
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  Data: <searchLink fieldCode="AR" term="%22Fu%2C+Jun%22">Fu, Jun</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Jianyi%22">Zhang, Jianyi</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> zjy@besti.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Peng%2C+Hong%22">Peng, Hong</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Wenxuan%22">Zhang, Wenxuan</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Han%2C+Kaiying%22">Han, Kaiying</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hei%2C+Xiali%22">Hei, Xiali</searchLink><relatesTo>5</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Neural+Systems%22">International Journal of Neural Systems</searchLink>. Aug2026, Vol. 36 Issue 8, p1-19. 19p.
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  Data: <searchLink fieldCode="DE" term="%22Structure-activity+relationships%22">Structure-activity relationships</searchLink><br /><searchLink fieldCode="DE" term="%22Graph+neural+networks%22">Graph neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Classification+algorithms%22">Classification algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Dynamic+models%22">Dynamic models</searchLink><br /><searchLink fieldCode="DE" term="%22Biologically+inspired+computing%22">Biologically inspired computing</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Cheminformatics%22">Cheminformatics</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: Spiking Neural P (SNP) systems have attracted increasing attention due to their biologically inspired, event-driven computation and inherent capability for temporal modeling. However, most existing SNP variants rely on fixed or purely local information propagation mechanisms, which limits their ability to capture long-range dependencies and contextual interactions in complex structured data. To address this limitation, we propose an Attention-Gated Spiking Neural membrane system (AGSNP), which incorporates an attention-guided gating mechanism directly into the spiking neuron dynamics. Unlike prior SNP models that treat attention as an external aggregation operation, AGSNP embeds attention signals into the nonlinear spiking update and memory regulation process. This design enables adaptive information propagation across distant structural components while preserving biologically inspired spiking behavior. To evaluate the effectiveness of the proposed architecture, AGSNP is instantiated within a graph-based learning framework and applied to Structure Activity Relationship (SAR) prediction. Experiments on three publicly available benchmark datasets demonstrate that AGSNP consistently outperforms representative baseline methods. Notably, under limited data availability and severe class imbalance, the proposed model achieves improvements of approximately 2.0–5.7% in AUC and related metrics on the Tox21 dataset, and 3.5–17.0% on the MUV dataset. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Neural Systems is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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        Value: 10.1142/S0129065726500231
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      – Code: eng
        Text: English
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        PageCount: 19
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    Subjects:
      – SubjectFull: Structure-activity relationships
        Type: general
      – SubjectFull: Graph neural networks
        Type: general
      – SubjectFull: Classification algorithms
        Type: general
      – SubjectFull: Dynamic models
        Type: general
      – SubjectFull: Biologically inspired computing
        Type: general
      – SubjectFull: Artificial neural networks
        Type: general
      – SubjectFull: Cheminformatics
        Type: general
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      – TitleFull: An Attention-Gated Graph Spiking Neural Membrane System for Structure-Activity Relationship Prediction.
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            NameFull: Fu, Jun
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            NameFull: Zhang, Jianyi
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            NameFull: Peng, Hong
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            NameFull: Zhang, Wenxuan
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            NameFull: Han, Kaiying
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              M: 08
              Text: Aug2026
              Type: published
              Y: 2026
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              Value: 36
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