QSPR checking and validation: a case study with hydroxy radical reaction rate constant.

Abstract:

:Traditionally, QSAR and QSPR models have been fitted by splitting the available compounds into separate learning and validation sets. The model is then fitted to the learning set and assessed using the validation set. Cross-validation (CV) uses all available compounds for both purposes, so that the full body of available information is brought to bear on both the learning and the validation portions of the study. The price paid for this additional information is a substantially greater computational load. A common mistake in using CV is to omit some of the repetitive computations. This mistake leads to substantial bias in the assessment. A hydroxyl radical reaction rate dataset is used to illustrate the superiority of CV and the pitfalls from its improper execution when modeling using nearest neighbors, paralleling behavior in the well-studied linear model setting.

journal_name

SAR QSAR Environ Res

authors

Hawkins DM,Kraker JJ,Basak SC,Mills D

doi

10.1080/10629360802349058

subject

Has Abstract

pub_date

2008-01-01 00:00:00

pages

525-39

issue

5-6

eissn

1062-936X

issn

1029-046X

pii

904002942

journal_volume

19

pub_type

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