Over the past decade, convolutional neural networks (CNNs) have revolutionized predictive modeling of data containing spatial correlations, specifically excelling at image analysis tasks due to their embedded feature extraction and improved generalization. However, outside of image or sequence data, datasets typically lack the structural correlation needed to exploit the benefits of CNN modeling. This is especially true regarding anticancer drug sensitivity prediction tasks, as the data used is often tabular without any embedded information in the ordering or locations of the features when utilizing data other than DNA or RNA sequences. This chapter provides a computational procedure, REpresentation of Features as Images with NEighborhood Dependencies (REFINED), that maps high-dimensional feature vectors into compact 2D images suitable for CNN-based deep learning. The pairing of REFINED mappings with CNNs enables enhanced predictive performance through reduced model parameterization and improved embedded feature extraction as compared to fully connected alternatives utilizing the high-dimensional feature vectors.