title: Introducing conformal prediction in predictive modeling for regulatory purposes. A transparent and flexible alternative to applicability domain determination.
abstract: Conformal prediction is presented as a framework which fulfills the OECD principles on (Q)SAR. It offers an intuitive extension to the application of machine-learning methods to structure-activity data where focus is on predictions with pre-defined confidence levels. A conformal predictor will make correct predictions on new compounds corresponding to a user defined confidence level. The confidence level can be altered depending on the situation the predictor is being used in, which allows for flexibility and adaption to risks that the user is willing to take. We demonstrate the usefulness of conformal prediction by applying it to 2 publicly available CAESAR binary classification datasets.
authors.affiliation: Swedish Toxicology Sciences Research Center, SE-151 36 Södertälje, Sweden. Electronic address: ulf.norinder@swetox.se.
authors.forename: Ulf
authors.initials: U
authors.lastname: Norinder
authors.affiliation: AstraZeneca Research and Development, SE-431 83 Mölndal, Sweden.
authors.forename: Lars
authors.initials: L
authors.lastname: Carlsson
authors.affiliation: Swedish Toxicology Sciences Research Center, SE-151 36 Södertälje, Sweden.
authors.forename: Scott
authors.initials: S
authors.lastname: Boyer
authors.affiliation: AstraZeneca Research and Development, SE-431 83 Mölndal, Sweden; Dept. Surgery, University of California at San Francisco (UCSF), San Francisco, CA 94115, USA.
authors.forename: Martin
authors.initials: M
authors.lastname: Eklund
mesh_terms: D016208:Databases, Factual; D004335:Drug and Narcotic Control / Q000331:legislation & jurisprudence* / Q000379:methods; D005544:Forecasting; D008962:Models, Theoretical; D008968:Molecular Conformation; D021281:Quantitative Structure-Activity Relationship
keywords: Applicability domain; CAESAR; Confidence predictor; Conformal prediction; Conformity score; MOE descriptors; REACH; Random forest; Signature descriptors