Literature DB >> 25420717

Impact of selection bias on the evaluation of clusters of chemical compounds in the drug discovery process.

Ariel Alonso1, Elasma Milanzi, Geert Molenberghs, Christophe Buyck, Luc Bijnens.   

Abstract

Expert opinion plays an important role when selecting promising clusters of chemical compounds in the drug discovery process. Indeed, experts can qualitatively assess the potential of each cluster, and with appropriate statistical methods, these qualitative assessments can be quantified into a success probability for each of them. However, one crucial element often overlooked is the procedure by which the clusters are assigned to/selected by the experts for evaluation. In the present work, the impact such a procedure may have on the statistical analysis and the entire evaluation process is studied. It has been shown that some implementations of the selection procedure may seriously compromise the validity of the evaluation even when the rating and selection processes are independent. Consequently, the fully random allocation of the clusters to the experts is strongly advocated.
Copyright © 2014 John Wiley & Sons, Ltd.

Keywords:  drug discovery; hierarchical models; missing data; sensitivity analysis

Mesh:

Substances:

Year:  2014        PMID: 25420717     DOI: 10.1002/pst.1665

Source DB:  PubMed          Journal:  Pharm Stat        ISSN: 1539-1604            Impact factor:   1.894


  1 in total

1.  Computational chemistry at Janssen.

Authors:  Herman van Vlijmen; Renee L Desjarlais; Tara Mirzadegan
Journal:  J Comput Aided Mol Des       Date:  2016-12-19       Impact factor: 3.686

  1 in total

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