Uncertainty Quantification in Molecular Machine Learning for Property Predictions under Data Shifts.

ABSTRACT: Drug discovery and medicinal chemistry efforts are increasingly influenced by machine learning (ML), with compound property prediction as a central application. ML models have demonstrated strong performance in predicting various compound properties from chemical structure. However, these models can exhibit varying levels of prediction error, making uncertainty quantification (UQ) essential for informed decisions. Standard UQ metrics include the distance to the molecules in the training set and prediction variance, obtained through methods such as model ensembles or Bayesian modeling. Although several UQ methodologies have been developed in recent years, no single approach consistently outperformed others. Herein, we present a comprehensive benchmark of UQ strategies for ML-based predictio

SUBMITTER: Parrondo-Pizarro R 

PROVIDER: S-EPMC12848971 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature