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

Deep Learning in Pharmacogenomics: From Gene Regulation to Patient Stratification