UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Conformal Regression for Quantitative Structure-Activity Relationship Modeling-Quantifying Prediction Uncertainty

Svensson, F; Aniceto, N; Norinder, U; Cortes-Ciriano, I; Spjuth, O; Carlsson, L; Bender, A; (2018) Conformal Regression for Quantitative Structure-Activity Relationship Modeling-Quantifying Prediction Uncertainty. Journal of Chemical Information and Modeling , 58 (5) pp. 1132-1140. 10.1021/acs.jcim.8b00054. Green open access

[thumbnail of Svensson_etal_r1.pdf]
Preview
Text
Svensson_etal_r1.pdf - Accepted Version

Download (1MB) | Preview

Abstract

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.

Type: Article
Title: Conformal Regression for Quantitative Structure-Activity Relationship Modeling-Quantifying Prediction Uncertainty
Open access status: An open access version is available from UCL Discovery
DOI: 10.1021/acs.jcim.8b00054
Publisher version: http://doi.org/10.1021/acs.jcim.8b00054
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Department of Neuromuscular Diseases
URI: https://discovery.ucl.ac.uk/id/eprint/10060460
Downloads since deposit
289Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item