Prediction of the classification, labelling and packaging regulation H-statements with confidence using conformal prediction with N-grams and molecular fingerprints.

ABSTRACT: Effective chemical hazard labelling systems are essential for safeguarding human health and the environment as a result of widespread chemical use, and machine-learning models can be used to predict hazard labels efficiently and reduce the use of animal tests. This investigation shows the utility of N-grams and other fingerprint featurization procedures for predicting classification, labelling and packaging (CLP). Regulation H-statements, particularly in an ensemble (consensus) setting. Consensus modelling by class or Conformal Prediction median p-values seems to be particularly advantageous in order to obtain both high conformal prediction validity and efficiency as well as good balanced accuracy, sensitivity and specificity. Utilization of the N-grams allows handling of all symbols in SM

SUBMITTER: Norinder U 

PROVIDER: S-EPMC12163164 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature