Comparison of Cellular Morphological Descriptors and Molecular Fingerprints for the Prediction of Cytotoxicity- and Proliferation-Related Assays

Cell morphology features, such as
those from the Cell Painting
assay, can be generated at relatively low costs 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 data set which were annotated with Cell Painting readouts,
where the relatively small size of the data set 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. While 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). While 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
neighbors 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.