COLDLNA: Enhancing long-range node features extraction to improve robust generalization ability of drug-target binding affinity prediction in cold-start scenarios

Recent advances in deep learning have driven significant progress in drug-target affinity (DTA) prediction. However, many models do not effectively utilize drug molecular graphs or capture long-range protein features, limiting their predictive accuracy. To address these limitations, a novel COLDLNA...

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Bibliographic Details
Published in:Journal of bioinformatics and computational biology Vol. 23; no. 5; p. 2550013
Main Authors: Xu, Ting, Jiang, Shaohua, Ding, Weibin, Wang, Peng
Format: Journal Article
Language:English
Published: Singapore 01.10.2025
Subjects:
Algorithms
Animals
Binding Sites
Caenorhabditis elegans
Computational Biology - methods
Databases, Protein
Deep Learning
Humans
Protein Binding
Proteins - chemistry
Proteins - metabolism
graph convolutional layer
Attention mechanism
long-range features
drug-target binding affinity
ISSN:1757-6334, 1757-6334
Online Access:Get more information
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Summary:Recent advances in deep learning have driven significant progress in drug-target affinity (DTA) prediction. However, many models do not effectively utilize drug molecular graphs or capture long-range protein features, limiting their predictive accuracy. To address these limitations, a novel COLDLNA model is designed for robust DTA prediction. The model employs the Long-range Node Attention Module to refine drug structure representations, while leveraging the Convolutional Attention Module to elucidate critical binding sites by extracting pivotal long-range information from protein amino acid sequences. Compared with the baseline model GraphDTA, COLDLNA reduced the MSE by 12.2% and 11.5% on the Davis and KIBA datasets, respectively. Additionally, its strong generalization ability was further validated on the Human dataset, C. elegans dataset, and in cold-start scenarios.
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ISSN:1757-6334
1757-6334
DOI:10.1142/S0219720025500131