Virtual Drug Response Prediction

PI Rick Stevens, Argonne National Laboratory

Utilizing data frames too large for conventional systems and a deep learning workflow designed to provide new approaches to personalized cancer medicine, this project enables billions of virtual drugs to be screened singly and in numerous combinations, while predicting their effects on tumor cells. The workflow is built from the CANDLE (CANcer Distributed Learning Environment) framework to optimize model hyper-parameters and perform billions of inferences to quantify model uncertainty and ultimately deliver results to be tested in pre-clinical experiments.