Dynamic applicability domain (dAD): compound-target binding affinity estimates with local conformal prediction.

ABSTRACT:

Motivation

Increasing efforts are being made in the field of machine learning to advance the learning of robust and accurate models from experimentally measured data and enable more efficient drug discovery processes. The prediction of binding affinity is one of the most frequent tasks of compound bioactivity modelling. Learned models for binding affinity prediction are assessed by their average performance on unseen samples, but point predictions are typically not provided with a rigorous confidence assessment. Approaches, such as the conformal predictor framework equip conventional models with a more rigorous assessment of confidence for individual point predictions. In this article, we extend the inductive conformal prediction framework for interaction data, in particular the co

SUBMITTER: Orsolic D 

PROVIDER: S-EPMC10457664 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature