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

Authors

Ting Xu, Shaohua Jiang, Weibin Ding, Peng Wang

Published in

Journal of bioinformatics and computational biology. Pages 2550013. Sep 20, 2025. Epub Sep 20, 2025.

Abstract

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.

PMID:
40977369
Bibliographic data and abstract were imported from PubMed on 22 Sep 2025.

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