HiDRA: Hierarchical Network for Drug Response Prediction with Attention

被引:21
作者
Jin, Iljung [1 ]
Nam, Hojung [1 ,2 ]
机构
[1] Gwangju Inst Sci & Technol GIST, Sch Elect Engn & Comp Sci, Gwangju 61005, South Korea
[2] Gwangju Inst Sci & Technol GIST, AI Grad Sch, Gwangju 61005, South Korea
关键词
CONNECTIVITY MAP; SIGNATURES; LCK; ENCYCLOPEDIA; SENSITIVITY; INHIBITOR; DISCOVERY; A-770041; DATABASE; GENES;
D O I
10.1021/acs.jcim.1c00706
中图分类号
R914 [药物化学];
学科分类号
100701 ;
摘要
Understanding differences in drug responses between patients is crucial for delivering effective cancer treatment. We describe an interpretable Al 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 R-2 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 Al-based model is capable of interpreting intrinsic characteristics of cancer cells and drugs for accurate prediction of cancer-drug responses.
引用
收藏
页码:3858 / 3867
页数:10
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