Abstract:
:One of the challenges with predictive modeling is how to quantify the reliability of the models' predictions on new objects. In this work we give an introduction to conformal prediction, a framework that sits on top of traditional machine learning algorithms and which outputs valid confidence estimates to predictions from QSAR models in the form of prediction intervals that are specific to each predicted object. For regression, a prediction interval consists of an upper and a lower bound. For classification, a prediction interval is a set that contains none, one, or many of the potential classes. The size of the prediction interval is affected by a user-specified confidence/significance level, and by the nonconformity of the predicted object; i.e., the strangeness as defined by a nonconformity function. Conformal prediction provides a rigorous and mathematically proven framework for in silico modeling with guarantees on error rates as well as a consistent handling of the models' applicability domain intrinsically linked to the underlying machine learning model. Apart from introducing the concepts and types of conformal prediction, we also provide an example application for modeling ABC transporters using conformal prediction, as well as a discussion on general implications for drug discovery.
journal_name
J Pharm Scijournal_title
Journal of pharmaceutical sciencesauthors
Alvarsson J,Arvidsson McShane S,Norinder U,Spjuth Odoi
10.1016/j.xphs.2020.09.055subject
Has Abstractpub_date
2021-01-01 00:00:00pages
42-49issue
1eissn
0022-3549issn
1520-6017pii
S0022-3549(20)30589-Xjournal_volume
110pub_type
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