HiDRA: Hierarchical Network for Drug Response Prediction with Attention

Understanding differences in drug
responses between patients is
crucial for delivering effective cancer treatment. We describe an
interpretable AI model for use in predicting drug responses in cancer
cells at the gene, molecular pathway, and drug level, which we have
called the hierarchical network for drug response prediction with
attention. We found that the model shows better accuracy in predicting
drugs having efficacy against a given cell line than other state-of-the-art
methods, with a root mean squared error of 1.0064, a Pearson’s
correlation coefficient of 0.9307, and an R2 value of 0.8647. We also confirmed that the model gives high attention
to drug-target genes and cancer-related pathways when predicting a
response. The validity of predicted results was proven by in vitro
cytotoxicity assay. Overall, we propose that our hierarchical and
interpretable AI-based model is capable of interpreting intrinsic
characteristics of cancer cells and drugs for accurate prediction
of cancer-drug responses.