π‐PhenoDrug: A Comprehensive Deep Learning‐Based Pipeline for Phenotypic Drug Screening in High‐Content Analysis

Image‐based screening is an efficient drug discovery strategy. Combining high‐content screening (HCS) with artificial intelligence efficiently detects drug‐induced 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‐based studies use only few features, which cannot accurately characterize cell phenotypic perturbations. Herein, π‐PhenoDrug, a deep learning‐based pipeline, is developed for cell phenotype‐driven drug screening. It integrates cell segmentation, morphological profile construction, and phenotype analysis modules of HCS processes. π‐PhenoDrug is applied to evaluate drug response in various human melanoma cell lines. The results demonstrate that π‐PhenoDrug can evaluate drug killing effects across different cell lines via both supervised and unsupervised modes. Furthermore, π‐PhenoDrug identifies drugs with potential killing effects on melanoma cells from a library of diverse compounds. Compared with traditional single‐readout assays, this method is more sensitive to compounds that induce weaker phenotypes, reducing the risk of overlooking effective drugs. These results confirm that π‐PhenoDrug can achieve high‐throughput and accuracy analysis of cell phenotypic data using an unbiased and automated workflow, improving drug discovery efficiency.