Your browser doesn't support javascript.
loading
qHTSWaterfall: 3-dimensional visualization software for quantitative high-throughput screening (qHTS) data.
Queme, Bryan; Braisted, John C; Dranchak, Patricia; Inglese, James.
Affiliation
  • Queme B; National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, MD, 20850, USA.
  • Braisted JC; National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, MD, 20850, USA. john.braisted@nih.gov.
  • Dranchak P; National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, MD, 20850, USA.
  • Inglese J; National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, MD, 20850, USA.
J Cheminform ; 15(1): 39, 2023 Mar 31.
Article in En | MEDLINE | ID: mdl-37004072
ABSTRACT
High throughput screening (HTS) is widely used in drug discovery and chemical biology to identify and characterize agents having pharmacologic properties often by evaluation of large chemical libraries. Standard HTS data can be simply plotted as an x-y graph usually represented as % activity of a compound tested at a single concentration vs compound ID, whereas quantitative HTS (qHTS) data incorporates a third axis represented by concentration. By virtue of the additional data points arising from the compound titration and the incorporation of logistic fit parameters that define the concentration-response curve, such as EC50 and Hill slope, qHTS data has been challenging to display on a single graph. Here we provide a flexible solution to the rapid plotting of complete qHTS data sets to produce a 3-axis plot we call qHTS Waterfall Plots. The software described here can be generally applied to any 3-axis dataset and is available as both an R package and an R shiny application.
Key words

Full text: 1 Database: MEDLINE Type of study: Diagnostic_studies / Screening_studies Language: En Journal: J Cheminform Year: 2023 Type: Article Affiliation country: United States

Full text: 1 Database: MEDLINE Type of study: Diagnostic_studies / Screening_studies Language: En Journal: J Cheminform Year: 2023 Type: Article Affiliation country: United States