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Advances in Computational Toxicology pp 215–232Cite as

Applicability Domain: Towards a More Formal Framework to Express the Applicability of a Model and the Confidence in Individual Predictions

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Part of the book series: Challenges and Advances in Computational Chemistry and Physics ((COCH,volume 30))

Abstract

A common understanding of the concept of applicability domain (AD) is that it defines the scope in which a model can make a reliable prediction; in other words, it is the domain within which we can trust a prediction. However, in reality, the concept of confidence in a prediction is more complex and multi-faceted; the applicability of a model is only one aspect amongst others. In this chapter, we will look at these different perspectives and how existing AD methods contribute to them. We will also try to formalise a holistic approach in the context of decision-making.

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Abbreviations

3D:

Three Dimension

AD:

Applicability Domain

DD:

Decision Domain

kNN:

K-Nearest Neighbours

QSAR:

Quantitative Structure Activity Relationship

OECD:

Organization of Economic Co-operation and Development

PCA:

Principal Component Analysis

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Authors and Affiliations

  1. Lhasa Limited, Granary Wharf House, 2 Canal Wharf, Leeds, LS11 5PS, UK

    Thierry Hanser, Chris Barber, Sébastien Guesné, Jean François Marchaland & Stéphane Werner

Authors
  1. Thierry Hanser
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  2. Chris Barber
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  3. Sébastien Guesné
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  4. Jean François Marchaland
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  5. Stéphane Werner
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Corresponding author

Correspondence to Thierry Hanser .

Editor information

Editors and Affiliations

  1. National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA

    Huixiao Hong

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Hanser, T., Barber, C., Guesné, S., Marchaland, J.F., Werner, S. (2019). Applicability Domain: Towards a More Formal Framework to Express the Applicability of a Model and the Confidence in Individual Predictions. In: Hong, H. (eds) Advances in Computational Toxicology. Challenges and Advances in Computational Chemistry and Physics, vol 30. Springer, Cham. https://doi.org/10.1007/978-3-030-16443-0_11

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