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Combining high\u2010content screening (HCS) with artificial intelligence efficiently detects drug\u2010induced cellular phenotypes from a large pool of compounds. However, previous studies primarily focus on cell classification and cannot interpret models using morphological features. Additionally, the existing morphology\u2010based studies use only few features, which cannot accurately characterize cell phenotypic perturbations. Herein, \u03c0\u2010PhenoDrug, a deep learning\u2010based pipeline, is developed for cell phenotype\u2010driven drug screening. It integrates cell segmentation, morphological profile construction, and phenotype analysis modules of HCS processes. \u03c0\u2010PhenoDrug is applied to evaluate drug response in various human melanoma cell lines. The results demonstrate that \u03c0\u2010PhenoDrug can evaluate drug killing effects across different cell lines via both supervised and unsupervised modes. 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