title: On the development and validation of QSAR models.
abstract: The fundamental and more critical steps that are necessary for the development and validation of QSAR models are presented in this chapter as best practices in the field. These procedures are discussed in the context of predictive QSAR modelling that is focused on achieving models of the highest statistical quality and with external predictive power. The most important and most used statistical parameters needed to verify the real performances of QSAR models (of both linear regression and classification) are presented. Special emphasis is placed on the validation of models, both internally and externally, as well as on the need to define model applicability domains, which should be done when models are employed for the prediction of new external compounds.
authors.affiliation: QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Theoretical and Applied Sciences, University of Insubria, via Dunant 3, Varese, Italy. paola.gramatica@uninsubria.it
authors.forename: Paola
authors.initials: P
authors.lastname: Gramatica
mesh_terms: D003663:Decision Trees; D008962:Models, Theoretical; D025341:Principal Component Analysis; D021281:Quantitative Structure-Activity Relationship; D012372:ROC Curve; D012044:Regression Analysis; D015203:Reproducibility of Results