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
References
OECD (2007) Guidance document on the validation of (quantitative) structure-activity relationship [(Q)SAR] models. In: OECD series on testing and assessment, No. 69. OECD Publishing, Paris. https://doi.org/10.1787/9789264085442-en. Accessed 10 Sept 2018
Mathea M et al (2016) Chemoinformatic classification methods and their applicability domain. Mol Inf 35(5):160–180
Eriksson L et al (2003) Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARs. Environ Health Perspect 111(10):1361–1375
Carrió P et al (2014) Applicability domain analysis (ADAN): a robust method for assessing the reliability of drug property predictions. J Chem Inf Model 54(5):1500–1511
Netzeva TI et al (2005) Current status of methods for defining the applicability domain of (quantitative) structure—activity relationships. Altern Lab Anim 32(2):155–173
Dragos H et al (2009) Predicting the predictability: a unified approach to the applicability domain problem of QSAR models. J Chem Inf Model 49(7):1762–1776
Sahigara F et al (2012) Comparison of different approaches to define the applicability domain of QSAR models. Molecules 17(5):4791–4810
Ochi S et al (2017) Structure modification toward applicability domain of a QSAR/QSPR model considering activity/property. Mol Inf 36(12):1700076
Sheridan RP (2012) Three useful dimensions for domain applicability in QSAR models using random forest. J Chem Inf Model 52(3):814–823
Sahigara F et al (2013) Defining a novel k-nearest neighbours approach to assess the applicability domain of a QSAR model for reliable predictions. J Cheminform 5(1):27
Sheridan RP et al (2004) Similarity to molecules in the training set is a good discriminator for prediction accuracy in QSAR. J Chem Inf Comput Sci 44(6):1912–1928
Toplak M et al (2014) Assessment of machine learning reliability methods for quantifying the applicability domain of QSAR regression models. J Chem Inf Model 54(2):431–441
Sheridan RP (2015) The relative importance of domain applicability metrics for estimating prediction errors in QSAR varies with training set diversity. J Chem Inf Model 55(6):1098–1107
Roy K et al (2015) On a simple approach for determining applicability domain of QSAR models. Chemom Intell Lab Syst 145:22–29
Gadaleta D (2016) Applicability domain for QSAR models: where theory meets reality. Intern J Quant Struct Prop Relat 1(1):45–63
Hanser T (2016) Applicability domain: towards a more formal definition. SAR QSAR Environ Res 27(11):893–909
Wikipedia https://en.wikipedia.org/wiki/Curse_of_dimensionality. Accessed 10 Sept 2018
Nikolova-Jeliazkova N, Jaworska J (2005) An approach to determining applicability domains for QSAR group contribution models: an analysis of SRC KOWWIN. Altern Lab Anim 33(5):461–470
Rogers D, Hahn M (2010) Extended-connectivity fingerprints. J Chem Inf Model 50(5):742–754
MACCS structural keys (2011) Accelrys, San Diego, CA
Carhart RE et al (1985) Atom pairs as molecular features in structure-activity studies: definition and applications. J Chem Inf Comput Sci 25(2):64–73
Gobbi A, Poppinger D (1998) Genetic optimization of combinatorial libraries. Biotech Bioeng 61(1):47–54
Nilakantan R et al (1987) Topological torsion: a new molecular descriptor for SAR applications. Comparison with other descriptors. J Chem Inf Comput Sci 27(2):82–85
Dimitrov S et al (2005) A stepwise approach for defining the applicability domain of SAR and QSAR models. J Chem Inf Model 45(4):839–849
Willett P et al (1998) Chemical similarity searching. J Chem Inf Comput Sci 38(6):983–996
Aniceto N et al (2016) A novel applicability domain technique for mapping predictive reliability across the chemical space of a QSAR: reliability-density neighbourhood. J Cheminformatics 8:69
Mussa HY et al (2015) The Parzen Window method: in terms of two vectors and one matrix. Pattern Recogn Lett 63:30–35
Philip N et al (2013) Assessing confidence in predictions made by knowledge-based systems. Toxicol Res 4(2):70–79
Norinder U et al (2016) Conformal prediction to define applicability domain—a case study on predicting ER and AR binding. SAR QSAR Environ Res 27(4):303–316
Forreryd A et al (2018) Predicting skin sensitizers with confidence—Using conformal prediction to determine applicability domain of GARD. Toxicol In Vitro 48:179–187
Wikipedia. https://en.wikipedia.org/wiki/TARDIS
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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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