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Predicting With Con...
Predicting With Confidence : Using Conformal Prediction in Drug Discovery
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- Alvarsson, Jonathan, 1981- (author)
- Uppsala universitet,Institutionen för farmaceutisk biovetenskap,Science for Life Laboratory, SciLifeLab,Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
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- Arvidsson McShane, Staffan (author)
- Uppsala universitet,Institutionen för farmaceutisk biovetenskap,Science for Life Laboratory, SciLifeLab,Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
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- Norinder, Ulf (author)
- Stockholms universitet,Örebro universitet,Uppsala universitet,Institutionen för farmaceutisk biovetenskap,Science for Life Laboratory, SciLifeLab,Department of Computer and Systems Sciences, Stockholm University; MTM Research Centre, School of Science and Technology, Örebro University,Spjuth,Institutionen för naturvetenskap och teknik,Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, Uppsala, Sweden; Department of Computer and Systems Sciences, Stockholm University, Kista, Sweden,Institutionen för data- och systemvetenskap,Uppsala University, Sweden; Örebro University, Sweden
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- Spjuth, Ola, Professor, 1977- (author)
- Uppsala universitet,Institutionen för farmaceutisk biovetenskap,Science for Life Laboratory, SciLifeLab,Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
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(creator_code:org_t)
- Elsevier, 2021
- 2021
- English.
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In: Journal of Pharmaceutical Sciences. - : Elsevier. - 0022-3549 .- 1520-6017. ; 110:1, s. 42-49
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Abstract
Subject headings
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- 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.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Bioinformatik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Bioinformatics (hsv//eng)
- NATURVETENSKAP -- Matematik -- Sannolikhetsteori och statistik (hsv//swe)
- NATURAL SCIENCES -- Mathematics -- Probability Theory and Statistics (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences (hsv//eng)
Keyword
- Applicability domain
- Confidence
- Conformal prediction
- Predictive modeling
- QSAR
- Bioinformatik
- Bioinformatics
Publication and Content Type
- ref (subject category)
- for (subject category)
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