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Image-based high-content screening in drug discovery.
Drug Discov. Today 25, 1348-1361 (2020)
While target-based drug discovery strategies rely on the precise knowledge of the identity and function of the drug targets, phenotypic drug discovery (PDD) approaches allow the identification of novel drugs based on knowledge of a distinct phenotype. Image-based high-content screening (HCS) is a potent PDD strategy that characterizes small-molecule effects through the quantification of features that depict cellular changes among or within cell populations, thereby generating valuable data sets for subsequent data analysis. However, these data can be complex, making image analysis from large HCS campaigns challenging. Technological advances in image acquisition, processing, and analysis as well as machine-learning (ML) approaches for the analysis of multidimensional data sets have rendered HCS as a viable technology for small-molecule drug discovery. Here, we discuss HCS concepts, current workflows as well as opportunities and challenges of image-based phenotypic screening and data analysis.
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Publication type
Article: Journal article
Document type
Review
Keywords
Pluripotent Stem-cells; Neural-networks; In-vitro; Software; Assay; Polypharmacology; Validation; Industry; Platform; Genome
ISSN (print) / ISBN
1359-6446
e-ISSN
1878-5832
Journal
Drug discovery today
Quellenangaben
Volume: 25,
Issue: 8,
Pages: 1348-1361
Publisher
Elsevier
Publishing Place
Cambridge
Non-patent literature
Publications
Reviewing status
Peer reviewed