Intercorrelation Limits in Molecular Descriptor Preselection for QSAR/QSPR

Anita Rácz, Dávid Bajusz, Károly Héberger

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

QSAR/QSPR (quantitative structure-activity/property relationship) modeling has been a prevalent approach in various, overlapping sub-fields of computational, medicinal and environmental chemistry for decades. The generation and selection of molecular descriptors is an essential part of this process. In typical QSAR workflows, the starting pool of molecular descriptors is rationalized based on filtering out descriptors which are (i) constant throughout the whole dataset, or (ii) very strongly correlated to another descriptor. While the former is fairly straightforward, the latter involves a level of subjectivity when deciding what exactly is considered to be a strong correlation. Despite that, most QSAR modeling studies do not report on this step. In this study, we examine in detail the effect of various possible descriptor intercorrelation limits on the resulting QSAR models. Statistical comparisons are carried out based on four case studies from contemporary QSAR literature, using a combined methodology based on sum of ranking differences (SRD) and analysis of variance (ANOVA).

Original languageEnglish
Article number1800154
JournalMolecular Informatics
Volume38
Issue number8-9
DOIs
Publication statusPublished - Aug 1 2019

Keywords

  • QSAR
  • analysis of variance
  • correlation
  • descriptor
  • regression
  • sum of ranking differences

ASJC Scopus subject areas

  • Structural Biology
  • Molecular Medicine
  • Drug Discovery
  • Computer Science Applications
  • Organic Chemistry

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