{"doi":"10.1016/j.drudis.2020.06.001","title":"Image-based high-content screening in drug discovery","abstract":null,"journal":"Drug Discovery Today","year":2020,"id":663108,"datarank":0.7143260902196635,"base_score":4.762173934797756,"endowment":4.762173934797756,"self_citation_contribution":0.7143260902196635,"citation_network_contribution":0.0,"self_endowment_contribution":0.7143260902196635,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":116,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":66096,"name":"Kenji Schorpp","orcid":"0000-0001-9428-9877","position":1,"is_corresponding":false},{"id":88160,"name":"Ina Rothenaigner","orcid":null,"position":2,"is_corresponding":false},{"id":66097,"name":"Kamyar Hadian","orcid":"0000-0001-8727-2575","position":3,"is_corresponding":false},{"id":1234088,"name":"Sean Lin","orcid":"0000-0001-9691-5149","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Image-based high-content screening in drug discovery","abstract":"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.","is_dataset_classified":null,"base_score":4.762173934797756,"endowment":4.762173934797756,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"32561299","pmcid":null,"openalex_id":"https://openalex.org/W3034751833","authors":[],"funders":[],"total_grants":0,"fwci":10.7034,"citation_percentile":0.98625654,"influential_citations":0,"citation_trend":[{"year":2020,"count":2},{"year":2021,"count":11},{"year":2022,"count":22},{"year":2023,"count":21},{"year":2024,"count":25},{"year":2025,"count":27},{"year":2026,"count":8}],"oa_status":"green","license":"other-oa","oa_locations":[{"url":"https://push-zb.helmholtz-muenchen.de/frontdoor.php?source_opus=59512","host_type":"repository"},{"url":"https://push-zb.helmholtz-muenchen.de/frontdoor.php?source_opus=59512","host_type":"repository"},{"url":"https://api.elsevier.com/content/article/PII:S1359644620302269?httpAccept=text/xml","host_type":"publisher"},{"url":"https://api.elsevier.com/content/article/PII:S1359644620302269?httpAccept=text/plain","host_type":"publisher"},{"url":"https://doi.org/10.1016/j.drudis.2020.06.001","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/32561299","host_type":"repository"},{"url":"https://push-zb.helmholtz-munich.de/frontdoor.php?source_opus=59512","host_type":"repository"}],"fields_of_study":["Cell Image Analysis Techniques","Image Processing Techniques and Applications","Advanced Fluorescence Microscopy Techniques","Drug Discovery","High-Throughput Screening Assays","Humans","Machine Learning","Phenotype"],"mesh_terms":["Machine Learning","Humans","Phenotype","Drug Discovery","High-Throughput Screening Assays"],"keywords":["Drug discovery","High-content screening","Workflow","Computer science","Knowledge extraction","Computational biology","Function (biology)","Identification (biology)","Data science","Image (mathematics)","Phenotypic screening","Drug","Data mining","Phenotype","Bioinformatics","Artificial intelligence","Biology","Cell","Pharmacology"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-12T19:40:41.293416Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}