Conformal prediction to define applicability domain - A case study on predicting ER and AR binding

A fundamental element when deriving a robust and predictive in silico model is not only the statistical quality of the model in question but, equally important, the estimate of its predictive boundaries. This work presents a new method, conformal prediction, for applicability domain estimation in th...

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Bibliographic Details
Published in:SAR and QSAR in environmental research 2016-01, Vol.27 (4), p.303-316
Main Authors: Norinder, U., Rybacka, A., Andersson, P.L.
Format: Article
Language:English
Subjects:
androgen receptor
Androgens - chemistry
Computer Simulation
Conformal prediction
Endocrine Disruptors - chemistry
Estrogens - chemistry
Medicin och hälsovetenskap
Molecular Conformation
oestrogen receptor
Protein Binding
Quantitative Structure-Activity Relationship
random forest
Receptors, Androgen - chemistry
Receptors, Estrogen - chemistry
signature descriptors
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Summary:A fundamental element when deriving a robust and predictive in silico model is not only the statistical quality of the model in question but, equally important, the estimate of its predictive boundaries. This work presents a new method, conformal prediction, for applicability domain estimation in the field of endocrine disruptors. The method is applied to binders and non-binders related to the oestrogen and androgen receptors. Ensembles of decision trees are used as statistical method and three different sets (dragon, rdkit and signature fingerprints) are investigated as chemical descriptors. The conformal prediction method results in valid models where there is an excellent balance in quality between the internally validated training set and the corresponding external test set, both in terms of validity and with respect to sensitivity and specificity. With this method the level of confidence can be readily altered by the user and the consequences thereof immediately inspected. Furthermore, the predictive boundaries for the derived models are rigorously defined by using the conformal prediction framework, thus no ambiguity exists as to the level of similarity needed for new compounds to be in or out of the predictive boundaries of the derived models where reliable predictions can be expected.
ISSN:1062-936X
1029-046X
1029-046X
DOI:10.1080/1062936X.2016.1172665