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.

Keywords: Applicability domain; CAESAR; Confidence predictor; Conformal prediction; Conformity score; MOE descriptors; REACH; Random forest; Signature descriptors.