Conformal Regression for Quantitative Structure–Activity Relationship ModelingQuantifying Prediction Uncertainty

Making predictions with an associated confidence is highly desirable as it facilitates decision making and resource prioritization. Conformal regression is a machine learning framework that allows the user to define the required confidence and delivers predictions that are guaranteed to be correct t...

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Bibliographic Details
Published in:Journal of chemical information and modeling 2018-05, Vol.58 (5), p.1132-1140
Main Authors: Svensson, Fredrik, Aniceto, Natalia, Norinder, Ulf, Cortes-Ciriano, Isidro, Spjuth, Ola, Carlsson, Lars, Bender, Andreas
Format: Article
Language:English
Subjects:
Biological activity
Confidence intervals
Decision making
Decision trees
Machine learning
Medicin och hälsovetenskap
Modelling
Molecular chains
Predictions
Regression models
Robustness (mathematics)
Statistical analysis
Uncertainty
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Summary:Making predictions with an associated confidence is highly desirable as it facilitates decision making and resource prioritization. Conformal regression is a machine learning framework that allows the user to define the required confidence and delivers predictions that are guaranteed to be correct to the selected extent. In this study, we apply conformal regression to model molecular properties and bioactivity values and investigate different ways to scale the resultant prediction intervals to create as efficient (i.e., narrow) regressors as possible. Different algorithms to estimate the prediction uncertainty were used to normalize the prediction ranges, and the different approaches were evaluated on 29 publicly available data sets. Our results show that the most efficient conformal regressors are obtained when using the natural exponential of the ensemble standard deviation from the underlying random forest to scale the prediction intervals, but other approaches were almost as efficient. This approach afforded an average prediction range of 1.65 pIC50 units at the 80% confidence level when applied to bioactivity modeling. The choice of nonconformity function has a pronounced impact on the average prediction range with a difference of close to one log unit in bioactivity between the tightest and widest prediction range. Overall, conformal regression is a robust approach to generate bioactivity predictions with associated confidence.
ISSN:1549-9596
1549-960X
1549-960X
DOI:10.1021/acs.jcim.8b00054