Assessing the calibration in toxicological in vitro models with conformal prediction.

ABSTRACT: Machine learning methods are widely used in drug discovery and toxicity prediction. While showing overall good performance in cross-validation studies, their predictive power (often) drops in cases where the query samples have drifted from the training data's descriptor space. Thus, the assumption for applying machine learning algorithms, that training and test data stem from the same distribution, might not always be fulfilled. In this work, conformal prediction is used to assess the calibration of the models. Deviations from the expected error may indicate that training and test data originate from different distributions. Exemplified on the Tox21 datasets, composed of chronologically released Tox21Train, Tox21Test and Tox21Score subsets, we observed that while internally valid models co

SUBMITTER: Morger A 

PROVIDER: S-EPMC8082859 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature