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

Jun Fu¹, Jianyi Zhang¹, Hong Peng²

¹Beijing Electronic Science and Technology Institute, Beijing 100070, P. R. China.

International Journal of Neural Systems

|February 27, 2026

Summary

This summary is machine-generated.

Attention-Gated Spiking Neural membrane systems (AGSNP) improve long-range dependency modeling in complex data. This biologically inspired model enhances Structure Activity Relationship prediction, especially with limited or imbalanced datasets.

Keywords:

Spiking neural membrane system graph attention mechanism structure activity relationship

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Area of Science:

Computational Neuroscience
Artificial Intelligence
Machine Learning

Background:

Spiking Neural P (SNP) systems offer event-driven computation and temporal modeling.
Existing SNP models struggle with long-range dependencies due to fixed or local information propagation.

Purpose of the Study:

Introduce the Attention-Gated Spiking Neural membrane system (AGSNP) to enhance SNP capabilities.
Address limitations in capturing contextual interactions in complex structured data.

Main Methods:

Incorporate an attention-guided gating mechanism directly into spiking neuron dynamics.
Embed attention signals into nonlinear spiking updates and memory regulation.
Instantiate AGSNP within a graph-based learning framework for Structure Activity Relationship (SAR) prediction.

Main Results:

AGSNP consistently outperforms representative baseline methods on benchmark datasets.
Achieved significant improvements (2.0-5.7% AUC on Tox21, 3.5-17.0% on MUV) under limited data and class imbalance.
Demonstrated effective adaptive information propagation across distant structural components.

Conclusions:

AGSNP offers a novel approach to enhance biologically inspired neural networks.
The model's embedded attention mechanism improves performance on complex predictive tasks like SAR.
AGSNP shows promise for applications requiring robust modeling of structured data with limited or imbalanced information.