One of the OECD principles for model validation requires defining the Applicability Domain (AD) for the QSAR models. This is important since the reliable predictions are generally limited to query chemicals structurally similar to the training compounds used to build the model. Therefore, characterization of interpolation space is significant in defining the AD and in this study some existing descriptor-based approaches performing this task are discussed and compared by implementing them on existing validated datasets from the literature. Algorithms adopted by different approaches allow defining the interpolation space in several ways, while defined thresholds contribute significantly to the extrapolations. For each dataset and approach implemented for this study, the comparison analysis was carried out by considering the model statistics and relative position of test set with respect to the training space.

Sahigara, F., Mansouri, K., Ballabio, D., Mauri, A., Consonni, V., Todeschini, R. (2012). Comparison of Different Approaches to Define the Applicability Domain of QSAR Models. MOLECULES, 17(5), 4791-4810 [10.3390/molecules17054791].

Comparison of Different Approaches to Define the Applicability Domain of QSAR Models

BALLABIO, DAVIDE;MAURI, ANDREA;CONSONNI, VIVIANA;TODESCHINI, ROBERTO
2012

Abstract

One of the OECD principles for model validation requires defining the Applicability Domain (AD) for the QSAR models. This is important since the reliable predictions are generally limited to query chemicals structurally similar to the training compounds used to build the model. Therefore, characterization of interpolation space is significant in defining the AD and in this study some existing descriptor-based approaches performing this task are discussed and compared by implementing them on existing validated datasets from the literature. Algorithms adopted by different approaches allow defining the interpolation space in several ways, while defined thresholds contribute significantly to the extrapolations. For each dataset and approach implemented for this study, the comparison analysis was carried out by considering the model statistics and relative position of test set with respect to the training space.
Articolo in rivista - Articolo scientifico
QSAR;model validation;applicability domain;interpolation space
English
2012
17
5
4791
4810
open
Sahigara, F., Mansouri, K., Ballabio, D., Mauri, A., Consonni, V., Todeschini, R. (2012). Comparison of Different Approaches to Define the Applicability Domain of QSAR Models. MOLECULES, 17(5), 4791-4810 [10.3390/molecules17054791].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/30277
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