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Importance of Applicability Domain of QSAR Models

Importance of Applicability Domain of QSAR Models

Kunal Roy, Supratik Kar
Copyright: © 2017 |Pages: 32
ISBN13: 9781522517627|ISBN10: 1522517626|EISBN13: 9781522517634
DOI: 10.4018/978-1-5225-1762-7.ch039
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MLA

Roy, Kunal, and Supratik Kar. "Importance of Applicability Domain of QSAR Models." Pharmaceutical Sciences: Breakthroughs in Research and Practice, edited by Information Resources Management Association, IGI Global, 2017, pp. 1012-1043. https://doi.org/10.4018/978-1-5225-1762-7.ch039

APA

Roy, K. & Kar, S. (2017). Importance of Applicability Domain of QSAR Models. In I. Management Association (Ed.), Pharmaceutical Sciences: Breakthroughs in Research and Practice (pp. 1012-1043). IGI Global. https://doi.org/10.4018/978-1-5225-1762-7.ch039

Chicago

Roy, Kunal, and Supratik Kar. "Importance of Applicability Domain of QSAR Models." In Pharmaceutical Sciences: Breakthroughs in Research and Practice, edited by Information Resources Management Association, 1012-1043. Hershey, PA: IGI Global, 2017. https://doi.org/10.4018/978-1-5225-1762-7.ch039

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Abstract

Quantitative Structure-Activity Relationship (QSAR) models have manifold applications in drug discovery, environmental fate modeling, risk assessment, and property prediction of chemicals and pharmaceuticals. One of the principles recommended by the Organization of Economic Co-operation and Development (OECD) for model validation requires defining the Applicability Domain (AD) for QSAR models, which allows one to estimate the uncertainty in the prediction of a compound based on how similar it is to the training compounds, which are used in the model development. The AD is a significant tool to build a reliable QSAR model, which is generally limited in use to query chemicals structurally similar to the training compounds. Thus, characterization of interpolation space is significant in defining the AD. An attempt is made in this chapter to address the important concepts and methodology of the AD as well as criteria for estimating AD through training set interpolation in the descriptor space.

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