Concepts and Applications of Conformal Prediction in Computational Drug Discovery

TL;DR

Conformal Prediction offers a reliable, interpretable, and computationally efficient method for estimating prediction errors in computational drug discovery, enhancing decision-making in preclinical and clinical settings.

Contribution

This review summarizes the concepts, applications, open source tools, limitations, and future prospects of Conformal Prediction in drug discovery.

Findings

CP guarantees prediction validity at specified confidence levels.

CP is versatile and easily integrated with various machine learning algorithms.

The review highlights current limitations and future opportunities in the field.

Abstract

Estimating the reliability of individual predictions is key to increase the adoption of computational models and artificial intelligence in preclinical drug discovery, as well as to foster its application to guide decision making in clinical settings. Among the large number of algorithms developed over the last decades to compute prediction errors, Conformal Prediction (CP) has gained increasing attention in the computational drug discovery community. A major reason for its recent popularity is the ease of interpretation of the computed prediction errors in both classification and regression tasks. For instance, at a confidence level of 90% the true value will be within the predicted confidence intervals in at least 90% of the cases. This so called validity of conformal predictors is guaranteed by the robust mathematical foundation underlying CP. The versatility of CP relies on its minimal computational footprint, as it can be easily coupled to any machine learning algorithm at little computational cost. In this review, we summarize underlying concepts and practical applications of CP with a particular focus on virtual screening and activity modelling, and list open source implementations of relevant software. Finally, we describe the current limitations in the field, and provide a perspective on future opportunities for CP in preclinical and clinical drug discovery.