Merging bioactivity predictions from cell morphology and chemical fingerprint models using similarity to training data.

ABSTRACT: The applicability domain of machine learning models trained on structural fingerprints for the prediction of biological endpoints is often limited by the lack of diversity of chemical space of the training data. In this work, we developed similarity-based merger models which combined the outputs of individual models trained on cell morphology (based on Cell Painting) and chemical structure (based on chemical fingerprints) and the structural and morphological similarities of the compounds in the test dataset to compounds in the training dataset. We applied these similarity-based merger models using logistic regression models on the predictions and similarities as features and predicted assay hit calls of 177 assays from ChEMBL, PubChem and the Broad Institute (where the required Cell Painti

SUBMITTER: Seal S 

PROVIDER: S-EPMC10236827 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature