Comparison of Cellular Morphological Descriptors and Molecular Fingerprints for the Prediction of Cytotoxicity- and Proliferation-Related Assays.
Chemical research in toxicology
American Chemical Society
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Seal, S., Yang, H., Vollmers, L., & Bender, A. (2021). Comparison of Cellular Morphological Descriptors and Molecular Fingerprints for the Prediction of Cytotoxicity- and Proliferation-Related Assays.. Chemical research in toxicology, 34 (2), 422-437. https://doi.org/10.1021/acs.chemrestox.0c00303
Cell morphology features, such as from the Cell Painting assay, can be generated at relatively low cost and represent versatile biological descriptors of a system, and thereby compound response. In this study, we explored cell morphology descriptors and molecular fingerprints, separately and in combination, for the prediction of cytotoxicity- and proliferation-related in vitro assay endpoints. We selected 135 compounds from the MoleculeNet ToxCast benchmark dataset which were annotated with Cell Painting readouts, where the relatively small size of the dataset is due to the overlap of required annotations. We trained Random Forest classification models using nested cross-validation and Cell Painting descriptors, Morgan and ErG fingerprints, and their combinations. When using leave-one-cluster-out cross validation (with clusters based on physicochemical descriptors), models using Cell Painting descriptors achieved higher average performance over all assays (Balanced Accuracy of 0.65, Matthews Correlation Coefficient of 0.28 and AUC-ROC of 0.71) compared to models using ErG fingerprints (BA 0.55, MCC 0.09 and AUC-ROC 0.60) and Morgan fingerprints alone (BA 0.54, MCC 0.06 and AUC-ROC: 0.56). When using random shuffle splits, the combination of Cell Painting descriptors with ErG and Morgan fingerprints further improved balanced accuracy on average by 8.9% (in 9 out of 12 assays) and 23.4% (in 8 out of 12 assays) compared to using only ErG and Morgan fingerprints, respectively. Regarding feature importance, Cell Painting descriptors related to nuclei texture, granularity of cells and cytoplasm, as well as cell neighbours and radial distributions were identified to be most contributing, which is plausible given the endpoint considered. We conclude that cell morphological descriptors contain complementary information to molecular fingerprints which can be used to improve the performance of predictive cytotoxicity models, in particular in areas of novel structural space.
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External DOI: https://doi.org/10.1021/acs.chemrestox.0c00303
This record's URL: https://www.repository.cam.ac.uk/handle/1810/316673
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