A hybrid high activity aware framework integrating graph attention network and transformer for half maximal inhibitory concentration prediction.

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.