  This Perspective provides examples of current and future applications of deep
learning in pharmacogenomics, including: (1) identification of novel regulatory
variants located in noncoding domains and their function as applied to
pharmacoepigenomics; (2) patient stratification from medical records; and (3)
prediction of drugs, targets, and their interactions. Deep learning
encapsulates a family of machine learning algorithms that over the last decade
has transformed many important subfields of artificial intelligence (AI) and
has demonstrated breakthrough performance improvements on a wide range of tasks
in biomedicine. We anticipate that in the future deep learning will be widely
used to predict personalized drug response and optimize medication selection
and dosing, using knowledge extracted from large and complex molecular,
epidemiological, clinical, and demographic datasets.
