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Applicability Domain: A Step Toward Confident Predictions and Decidability for QSAR Modeling

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Computational Toxicology

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1800))

Abstract

In the context of human safety assessment through quantitative structure–activity relationship (QSAR) modeling, the concept of applicability domain (AD) has an enormous role to play. The Organization of Economic Co-operation and Development (OECD) for QSAR model validation recommended as principle 3 “A defined domain of applicability” to be present for a predictive QSAR model. The study of AD allows estimating the uncertainty in the prediction for a particular molecule based on how similar it is to the training compounds which are used in the model development. In the current scenario, AD represents an active research topic, and many methods have been designed to estimate the competence of a model and the confidence in its outcome for a given prediction task. Thus, characterization of interpolation space is significant in defining the AD. The diverse set of reported AD methods was constructed through different hypotheses and algorithms. These multiplicities of methodologies mystify the end users and make the comparison of the AD for different models a complex issue to address. We have attempted to summarize in this chapter the important concepts of AD including particulars of the available methods to compute the AD along with their thresholds and criteria for estimating AD through training set interpolation in the descriptor space. The idea about transparent domain and decision domain are also discussed. To help readers determine the AD in their projects, practical examples together with available open source software tools are provided.

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Acknowledgments

S.K. and J.L. thank the National Science Foundation (NSF/CREST HRD-1547754, and NSF/RISE HRD-1547836) for financial support. K.R. is thankful to the UGC, New Delhi for financial assistance under the UPE II scheme.

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Authors and Affiliations

  1. Interdisciplinary Center for Nanotoxicity, Department of Chemistry and Biochemistry, Jackson State University, Jackson, MS, USA

    Supratik Kar & Jerzy Leszczynski

  2. Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India

    Kunal Roy

Authors
  1. Supratik Kar
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  2. Kunal Roy
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  3. Jerzy Leszczynski
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Corresponding author

Correspondence to Kunal Roy .

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Editors and Affiliations

  1. Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, Bari, Italy

    Orazio Nicolotti

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Kar, S., Roy, K., Leszczynski, J. (2018). Applicability Domain: A Step Toward Confident Predictions and Decidability for QSAR Modeling. In: Nicolotti, O. (eds) Computational Toxicology. Methods in Molecular Biology, vol 1800. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7899-1_6

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  • DOI: https://doi.org/10.1007/978-1-4939-7899-1_6

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-7898-4

  • Online ISBN: 978-1-4939-7899-1

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