Efficiently predicting drug response remains a challenge in the realm of drug discovery. By leveraging two types of multiomics data, transcriptomics, and genomics, we create a comprehensive representation of target cells and enable drug response prediction for personalized medicine. To address this issue, we propose four novel model architectures that combine graph transformers with varying positions of multiheaded self-attention mechanisms. A majority of our architectures utilize multiple transformer models, one with a graph attention mechanism and the other with a multiheaded self-attention mechanism, to generate latent representations of both the drug and omics data, respectively. Unlike previous approaches that apply attention only to one data type, either drug or genomics, our model architectures employ this technique for both, with a goal to procure more comprehensive latent representations. The latent representations are then concatenated and input into a fully connected network to predict the IC-50 score, a measure of cell drug response. We experiment with all four of these architectures and extract results from them. The novel model without the multiheaded self-attention mechanism seems to give us the most accurate results on our holdout set. Our study greatly contributes to the future of drug discovery and precision medicine by looking to optimize the time and accuracy of drug response prediction, as well as using multiomics data for a personalized approach to the problem.