Predicting With Confidence: Using Conformal Prediction in Drug Discovery.

Abstract:

: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.

journal_name

J Pharm Sci

authors

Alvarsson J,Arvidsson McShane S,Norinder U,Spjuth O

doi

10.1016/j.xphs.2020.09.055

subject

Has Abstract

pub_date

2021-01-01 00:00:00

pages

42-49

issue

1

eissn

0022-3549

issn

1520-6017

pii

S0022-3549(20)30589-X

journal_volume

110

pub_type

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