DrugCellGNN: Graph Convolutional Networks for Integrating Omics and Drug Similarities in Cancer Therapy Prediction

Predicting drug response in cancer cell lines is a critical step toward precision oncology, enabling more efficient therapeutic discovery and personalized treatment strategies. However, the complexity of drug–cell interactions, driven by diverse omics profiles and structural variability among drugs, poses significant challenges for conventional machine learning approaches. In this study, we propose an end-to-end pipeline that integrates multi-omics data (gene expression, copy number variation, and mutations) with chemical structure representations of drugs to predict binary drug response. Our method employs principal component analysis (PCA) for dimensionality reduction of high-dimensional omics data, followed by the computation of drug–drug and cell–cell similarity matrices. These are used to construct a heterogeneous graph combining intra-class similarities with drug–cell interactions. A customized graph neural network model, DrugCellGNN, is then applied to learn context-aware embeddings of drugs and cells. The fused representations are passed to a downstream multi-layer perceptron for classification. To address class imbalance, we introduce a dynamic focal loss function that adaptively emphasizes hard-to-classify examples. Evaluation on the GDSC dataset with an 80/20 train–test split demonstrates strong performance: Accuracy = 0.8935, F1 = 0.9201, AUC = 0.9510. This work highlights the utility of graph-based integration of multi-omics and drug features for drug sensitivity prediction. By leveraging both molecular and relational information, the proposed framework offers a robust and extensible foundation for advancing computational approaches in precision oncology.

@article{Aly2025,
  title     = {DrugCellGNN: Graph Convolutional Networks for Integrating Omics and Drug Similarities in Cancer Therapy Prediction},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {12},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Gehad Awad Aly and Rania Ahmed Abdel Azeem Abul Seoud and Dina Ahmed Salem},
  doi       = {10.14569/IJACSA.2025.0161281},
  url       = {https://doi.org/10.14569/IJACSA.2025.0161281}
}