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Predicting with confidence: Using conformal prediction in drug discovery
Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, Uppsala, Sweden.
Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, Uppsala, Sweden.
Örebro University, School of Science and Technology. Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, Uppsala, Sweden; Department of Computer and Systems Sciences, Stockholm University, Kista, Sweden. (MTM Research Centre)ORCID iD: 0000-0003-3107-331X
Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, Uppsala, Sweden.
2021 (English)In: Journal of Pharmaceutical Sciences, ISSN 0022-3549, E-ISSN 1520-6017, Vol. 110, no 1, p. 42-49Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
John Wiley & Sons, 2021. Vol. 110, no 1, p. 42-49
Keywords [en]
QSAR, applicability domain, confidence, conformal prediction, predictive modeling
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:oru:diva-86813DOI: 10.1016/j.xphs.2020.09.055ISI: 000600571800007PubMedID: 33075380Scopus ID: 2-s2.0-85094140468OAI: oai:DiVA.org:oru-86813DiVA, id: diva2:1479447
Funder
Swedish Research Council Formas, 2018-00924Swedish Foundation for Strategic Research , BD15-0008SB16-0046Available from: 2020-10-27 Created: 2020-10-27 Last updated: 2024-01-16Bibliographically approved

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Norinder, Ulf

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