The Importance of Being Earnest:
Validation is the Absolute Essential for Successful Application and Interpretation of QSPR Models
Alexander Tropsha1,†, Paola Gramatica2, Vijay K. Gombar3
Article first published online: 16 APR 2003
Abstract
This paper emphasizes the importance of rigorous validation as a crucial, integral component of Quantitative Structure Property Relationship (QSPR) model development. We consider some examples of published QSPR models, which in spite of their high fitted accuracy for the training sets and apparent mechanistic appeal, fail rigorous validation tests, and, thus, may lack practical utility as reliable screening tools. We present a set of simple guidelines for developing validated and predictive QSPR models. To this end, we discuss several validation strategies including (1) randomization of the modelled property, also called Y-scrambling, (2) multiple leave-many-out cross-validations, and (3) external validation using rational division of a dataset into training and test sets. We also highlight the need to establish the domain of model applicability in the chemical space to flag molecules for which predictions may be unreliable, and discuss some algorithms that can be used for this purpose. We advocate the broad use of these guidelines in the development of predictive QSPR models.
DOI:
10.1002/qsar.200390007
Copyright © 2003 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
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