Frontiers in immunology · 2026
A hybrid high activity aware framework integrating graph attention network and transformer for half maximal inhibitory concentration prediction.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Ban Dingcheng, Pan Lu, Zhang Xueli, Xu Peng, Wang Binbin, Liu Tao, Pan Deng, Li Xianbin
Аннотация
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 (IC50), making its accurate prediction a critical computational task for accelerating the discovery and optimization of anticancer lead compounds.
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 (IC50) prediction of c-KIT inhibitors. The model was trained on inhibitor data targeting c-KIT and related kinase families sourced from the BindingDB and ChEMBL databases. By extracting atom-level graph features, Morgan fingerprints, and physicochemical descriptors from SMILES strings, HGATT constructs a unified molecular representation that integrates both local structural and global information. Its architecture employs multidimensional graph attention mechanisms and gated residual modules to simultaneously capture atomic-level local structural features and macroscopic molecular properties. Stabilized training strategies, including gradient clipping, were adopted to enhance training efficiency and model robustness.
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.
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 (IC50) prediction of c-KIT inhibitors. The model was trained on inhibitor data targeting c-KIT and related kinase families sourced from the BindingDB and ChEMBL databases. By extracting atom-level graph features, Morgan fingerprints, and physicochemical descriptors from SMILES strings, HGATT constructs a unified molecular representation that integrates both local structural and global information. Its architecture employs multidimensional graph attention mechanisms and gated residual modules to simultaneously capture atomic-level local structural features and macroscopic molecular properties. Stabilized training strategies, including gradient clipping, were adopted to enhance training efficiency and model robustness.
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.