title: Introducing Bayesian thinking to high-throughput screening for false-negative rate estimation.
abstract: High-throughput screening (HTS) has been widely used to identify active compounds (hits) that bind to biological targets. Because of cost concerns, the comprehensive screening of millions of compounds is typically conducted without replication. Real hits that fail to exhibit measurable activity in the primary screen due to random experimental errors will be lost as false-negatives. Conceivably, the projected false-negative rate is a parameter that reflects screening quality. Furthermore, it can be used to guide the selection of optimal numbers of compounds for hit confirmation. Therefore, a method that predicts false-negative rates from the primary screening data is extremely valuable. In this article, we describe the implementation of a pilot screen on a representative fraction (1%) of the screening library in order to obtain information about assay variability as well as a preliminary hit activity distribution profile. Using this training data set, we then developed an algorithm based on Bayesian logic and Monte Carlo simulation to estimate the number of true active compounds and potential missed hits from the full library screen. We have applied this strategy to five screening projects. The results demonstrate that this method produces useful predictions on the numbers of false negatives.
authors.affiliation: 1Research Informatics, F. Hoffmann-La Roche Inc., Nutley, NJ, USA.
authors.forename: Xin
authors.initials: X
authors.lastname: Wei
authors.forename: Lin
authors.initials: L
authors.lastname: Gao
authors.forename: Xiaolei
authors.initials: X
authors.lastname: Zhang
authors.forename: Hong
authors.initials: H
authors.lastname: Qian
authors.forename: Karen
authors.initials: K
authors.lastname: Rowan
authors.forename: David
authors.initials: D
authors.lastname: Mark
authors.forename: Zhengwei
authors.initials: Z
authors.lastname: Peng
authors.forename: Kuo-Sen
authors.initials: KS
authors.lastname: Huang
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D057166:High-Throughput Screening Assays / Q000706:statistics & numerical data*; D006801:Humans; D009010:Monte Carlo Method; D054852:Small Molecule Libraries / Q000737:chemistry* / Q000494:pharmacology
chemical_list: D054852:Small Molecule Libraries
keywords: Bayesian analysis; false-negative rate; high-throughput screening; prior and posterior distribution