Quantitative Structure–Property Relationship
Modeling: A Valuable Support in High-Throughput Screening Quality
Control
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Abstract
Evaluation
of important pharmacokinetic properties such as hydrophobicity
by high-throughput screening (HTS) methods is a major issue in drug
discovery. In this paper, we present measurements of the chromatographic
hydrophobicity index (CHI) on a subset of the French chemical library
Chimiothèque Nationale (CN). The data were used in quantitative
structure–property relationship (QSPR) modeling in order to
annotate the CN. An algorithm is proposed to detect problematic molecules
with large prediction errors, called outliers. In order to find an
explanation for these large discrepancies between predicted and experimental
values, these compounds were reanalyzed experimentally. As the first
selected outliers indeed had experimental problems, including hydrolysis
or sheer absence of expected structure, we herewith propose the use
of QSPR as a support tool for quality control of screening data and
encourage cooperation between experimental and theoretical teams to
improve results. The corrected data were used to produce a model,
which is freely available on our web server at http://infochim.u-strasbg.fr/webserv/VSEngine.html