title: Exploiting domain knowledge for improved quantitative high-throughput screening curve fitting.
abstract: Least-squares fitting of the Hill equation to quantitative high-throughput screening (qHTS) assays results in frequent unsatisfactory fits. We learn and exploit prior knowledge to improve the Hill fitting in a nonlinear regression method called domain knowledge fitter (DK-fitter). This paper formulates and solves DK-fitter for 44 public qHTS data sets. This new Hill parameter estimation technique is validated using three unbiased approaches, including a novel method that involves generating simulated samples. This paper fosters the extraction of higher quality information from screens for improved potency evaluation.
authors: Department of Mathematical Sciences, Rensselaer Polytechnic Institute, 110 8th Street, Troy, New York 12180, USA. chbergeron@gmail.com
authors: Charles
authors: C
authors: Bergeron
authors: Gregory
authors: G
authors: Moore
authors: Michael
authors: M
authors: Krein
authors: Curt M
authors: CM
authors: Breneman
authors: Kristin P
authors: KP
authors: Bennett
mesh_terms: D019295:Computational Biology / Q000379:methods* / Q000706:statistics & numerical data; D015195:Drug Design; D004791:Enzyme Inhibitors / Q000494:pharmacology; D057166:High-Throughput Screening Assays; D008956:Models, Chemical; D011770:Pyruvate Kinase / Q000037:antagonists & inhibitors / Q000378:metabolism; D021281:Quantitative Structure-Activity Relationship; D012044:Regression Analysis
chemical_list: D004791:Enzyme Inhibitors; D011770:Pyruvate Kinase