Abstract：
:Iterative screening has emerged as a promising approach to increase the efficiency of high-throughput screening (HTS) campaigns in drug discovery. By learning from a subset of the compound library, inferences on what compounds to screen next can be made by predictive models. One of the challenges of iterative screening is to decide how many iterations to perform. This is mainly related to difficulties in estimating the prospective hit rate in any given iteration. In this article, a novel method based on Venn-ABERS predictors is proposed. The method provides accurate estimates of the number of hits retrieved in any given iteration during an HTS campaign. The estimates provide the necessary information to support the decision on the number of iterations needed to maximize the screening outcome. Thus, this method offers a prospective screening strategy for early-stage drug discovery.
journal_name
J Chem Inf Modeljournal_title
Journal of chemical information and modelingauthors
Buendia R,Kogej T,Engkvist O,Carlsson L,Linusson H,Johansson U,Toccaceli P,Ahlberg Edoi
10.1021/acs.jcim.8b00724subject
Has Abstractpub_date
2019-03-25 00:00:00pages
1230-1237issue
3eissn
1549-9596issn
1549-960Xjournal_volume
59pub_type
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