The majority of cancer drug sensitivity models are built utilizing genomic data measured before drug application to predict the steady state sensitivity of an applied drug. Restricting models to this type of data is limiting and can only explain one small piece of the puzzle. Better characterization of cancer cells can be accomplished through the use of proteomic data as this more directly corresponds to cellular activity. We have implemented models that predict cell viability utilizing protein expression measured post drug application. These models are built utilizing the Random Forest, Elastic Net, Partial Least Square Regression and Support Vector Regression algorithms in addition to stacked models. We have also utilized these same algorithms to predict the average protein inhibition of a cancer drug utilizing cell viability screens as input. Protein expression and cell viability data is taken from the HMS-LINCS database. We have shown that cell viability can be effectively predicted utilizing proteomic data and that we can estimate cancer drug protein inhibition utilizing a small number of cell line screens.