ArticleFrontiers in immunology2026
A hybrid high activity aware framework integrating graph attention network and transformer for half maximal inhibitory concentration prediction.
Article in Frontiers in immunology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Introduction: Tyrosine kinase inhibitors targeting the c-KIT receptor are pivotal in the targeted therapy of malignancies such as gastrointestinal stromal tumors (GIST). The bioactivity of these inhibitors is typically quantified by the half-maximal inhibitory concentration (IC Methods: To address this need, we propose HGATT-a hybrid high activity aware framework integrating Graph Attention Network (GAT) and Transformer-for high-accuracy half maximal inhibitory concentration (IC Results: On an independent test set, HGATT achieved a mean squared error (MSE) of 0.28 and a coefficient of determination (R²) of 0.57, corresponding to an approximate 44% reduction in MSE compared to the second-best baseline. Discussion: Experimental results demonstrate that HGATT outperforms not only individual graph neural network (GNN)- and machine learning-based models but also other related drug-target prediction methods and baseline regression approaches, exhibiting superior predictive accuracy.
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