Relationship between prediction accuracy and uncertainty in compound potency prediction using deep neural networks and control models.

ABSTRACT: The assessment of prediction variance or uncertainty contributes to the evaluation of machine learning models. In molecular machine learning, uncertainty quantification is an evolving area of research where currently no standard approaches or general guidelines are available. We have carried out a detailed analysis of deep neural network variants and simple control models for compound potency prediction to study relationships between prediction accuracy and uncertainty. For comparably accurate predictions obtained with models of different complexity, highly variable prediction uncertainties were detected using different metrics. Furthermore, a strong dependence of prediction characteristics and uncertainties on potency levels of test compounds was observed, often leading to over- or under-

SUBMITTER: Roth JP 

PROVIDER: S-EPMC10950896 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature