Explicit Applicability Domain Calculations Can Help Determine When Uncertainty Estimates Are Less Reliable.

ABSTRACT: Quantifying the uncertainty associated with a QSAR prediction is hugely valuable. Conformal regression and Venn-ABERS have emerged as state-of-the-art uncertainty estimation methods for regression and classification QSAR models, respectively. However, their performance is limited when they are applied to compounds sampled from a different distribution to the data used to train the model and/or calibrate their uncertainty estimates. Previous studies have evidenced this when applying these methods to nonrandom train/test splits, e.g., temporal validation, cluster or scaffold splits. Building on these previous studies, we demonstrate that explicit applicability domain calculations, using only structural similarity, can help determine when these uncertainty estimates are less reliable for mole

SUBMITTER: Hosni Z 

PROVIDER: S-EPMC12854501 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature