  By combining various cancer cell line (CCL) drug screening panels, the size
of the data has grown significantly to begin understanding how advances in deep
learning can advance drug response predictions. In this paper we train >35,000
neural network models, sweeping over common featurization techniques. We found
the RNA-seq to be highly redundant and informative even with subsets larger
than 128 features. We found the inclusion of single nucleotide polymorphisms
(SNPs) coded as count matrices improved model performance significantly, and no
substantial difference in model performance with respect to molecular
featurization between the common open source MOrdred descriptors and Dragon7
descriptors. Alongside this analysis, we outline data integration between CCL
screening datasets and present evidence that new metrics and imbalanced data
techniques, as well as advances in data standardization, need to be developed.
