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Principles of QSAR models validation: internal and external
The recent REACH Policy of the European Union has led to scientists and regulators to focus their attention on establishing general validation principles for QSAR models in the context of chemical regulation (previously known as the Setubal, nowadays, the OECD principles). This paper gives a brief analysis of some principles: unambiguous algorithm, Applicability Domain (AD), and statistical validation. Some concerns related to QSAR algorithm reproducibility and an example of a fast check of the applicability domain for MLR models are presented. Common myths and misconceptions related to popular techniques for verifying internal predictivity, particularly for MLR models (for instance crossvalidation, bootstrap), are commented on and compared with commonly used statistical techniques for external validation. The differences in the two validating approaches are highlighted, and evidence is presented that only models that have been validated externally, after their internal validation, can be considered reliable and applicable for both external prediction and regulatory purposes. (“Validation is one of those words.that is constantly used and seldom defined” as stated by A. R. Feinstein in the book Multivariate Analysis: An Introduction, Yale University Press, New Haven, 1996).
Principles of QSAR models validation: internal and external
The recent REACH Policy of the European Union has led to scientists and regulators to focus their attention on establishing general validation principles for QSAR models in the context of chemical regulation (previously known as the Setubal, nowadays, the OECD principles). This paper gives a brief analysis of some principles: unambiguous algorithm, Applicability Domain (AD), and statistical validation. Some concerns related to QSAR algorithm reproducibility and an example of a fast check of the applicability domain for MLR models are presented. Common myths and misconceptions related to popular techniques for verifying internal predictivity, particularly for MLR models (for instance crossvalidation, bootstrap), are commented on and compared with commonly used statistical techniques for external validation. The differences in the two validating approaches are highlighted, and evidence is presented that only models that have been validated externally, after their internal validation, can be considered reliable and applicable for both external prediction and regulatory purposes. (“Validation is one of those words.that is constantly used and seldom defined” as stated by A. R. Feinstein in the book Multivariate Analysis: An Introduction, Yale University Press, New Haven, 1996).
Principles of QSAR models validation: internal and external
GRAMATICA, PAOLA (Autor:in) / Gramatica, Paola
01.01.2007
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
DDC:
690
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