Towards Drug Repurposing for Leukemia: An Ensemble Graph Neural Network Model for Drug Response Prediction
I. T. Anjusha, K. A. Abdul Nazeer, Nabizath Saleena · International Journal of Artificial Intelligence Tools · 2025
Drug response prediction is an essential part of precision medicine, which assesses the efficacy of drugs in specific cancer cell lines. Recently, numerous deep learning models have been developed for drug response prediction. The ability to effectively represent complicated interactions within a network makes Graph Neural Network (GNN) models a prominent area of exploration. To exploit the diverse capabilities of different GNN models, the proposed model uses an ensemble approach that combines results from Graph Convolutional Network (GCN), Graph SAmple and aggreGatE Network (GraphSAGE) and Graph Attention Network (GAT)-based models for drug response prediction. The proposed ensemble GNN model is used to predict the efficacy of drugs in treating leukemia. The model outperformed individual models when evaluated with the GDSC1000 dataset, with lower root mean squared error, higher Pearson’s correlation coefficient, higher coefficient of determination and higher Spearman’s correlation coefficient. Furthermore, most of the drugs identified by the model as potential candidates for the treatment of leukemia are currently in clinical trials or have supporting evidence in the PubMed literature database.