title: QSAR applicabilty domain estimation by projection of the training set descriptor space: a review.
abstract: As the use of Quantitative Structure Activity Relationship (QSAR) models for chemical management increases, the reliability of the predictions from such models is a matter of growing concern. The OECD QSAR Validation Principles recommend that a model should be used within its applicability domain (AD). The Setubal Workshop report provided conceptual guidance on defining a (Q)SAR AD, but it is difficult to use directly. The practical application of the AD concept requires an operational definition that permits the design of an automatic (computerised), quantitative procedure to determine a models AD. An attempt is made to address this need, and methods and criteria for estimating AD through training set interpolation in descriptor space are reviewed. It is proposed that response space should be included in the training set representation. Thus, training set chemicals are points in n-dimensional descriptor space and m-dimensional model response space. Four major approaches for estimating interpolation regions in a multivariate space are reviewed and compared: range, distance, geometrical, and probability density distribution.
authors.affiliation: Procter and Gamble, Eurocor, Central Product Safety, 100 Temselaan, 1853 Strombeek-Bever, Belgium. Jaworska.j@pg.com
authors.forename: Joanna
authors.initials: J
authors.lastname: Jaworska
authors.forename: Nina
authors.initials: N
authors.lastname: Nikolova-Jeliazkova
authors.forename: Tom
authors.initials: T
authors.lastname: Aldenberg
mesh_terms: D000588:Amines / Q000737:chemistry; D005544:Forecasting; D008956:Models, Chemical; D015233:Models, Statistical; D016296:Mutagenesis; D021281:Quantitative Structure-Activity Relationship; D012475:Salmonella / Q000737:chemistry
chemical_list: D000588:Amines