  Accurate drug response prediction (DRP) is a crucial yet challenging task in
precision medicine. This paper presents a novel Attention-Guided Multi-omics
Integration (AGMI) approach for DRP, which first constructs a Multi-edge Graph
(MeG) for each cell line, and then aggregates multi-omics features to predict
drug response using a novel structure, called Graph edge-aware Network (GeNet).
For the first time, our AGMI approach explores gene constraint based
multi-omics integration for DRP with the whole-genome using GNNs. Empirical
experiments on the CCLE and GDSC datasets show that our AGMI largely
outperforms state-of-the-art DRP methods by 8.3%--34.2% on four metrics. Our
data and code are available at https://github.com/yivan-WYYGDSG/AGMI.
