Skin Doctor CP is a machine learning model for the classification of small organic compounds into skin sensitizers and non-sensitizers. More specifically, the core of Skin Doctor CP is a random forest binary classifier that is enveloped in an aggregated Mondrian conformal prediction framework. This allows users to define an error significance level (i.e. error rate) for classification. Predictions (i.e. sensitizer or non-sensitizer) are thus only reported for compounds for which the expected reliability reaches or exceeds the error rate defined by the user. The error significance level parameters of the framework can be adjusted, as described in the box below.
Skin Doctor CP is trained on a curated data set of 1285 compounds measured in the local lymph node assay (LLNA).
Skin Doctor CP has been developed in collaboration with Beiersdorf AG. The web service is free for non-commercial and academic research purposes only.
For more detail, see our publication on Skin Doctor CP.
Skin Doctor CP allows the selection of four different error significance levels. For each of the selected error significance levels the model will provide predictions with a maximum error equal to the error significance level. The default values correspond to the four error significance levels used in the Skin Doctor CP publication.
If you are using Skin Doctor Suite for your research, please cite all of the following publications:
Wilm, A.; Stork, C.; Bauer, C.; Schepky, A.; Kühnl, J.; Kirchmair, J.
Skin Doctor: Machine Learning Models for Skin Sensitization Prediction that Provide Estimates and Indicators of Prediction Reliability.
Int. J. Mol. Sci. 2019.
Wilm, A.; Norinder, U; Agea, M. I.; de Bruyn Kops, C.; Stork, C.; Kühnl, J.;
Skin Doctor CP: Conformal prediction of the skin sensitization potential
of small organic molecules.
Chem. Res. Tox. 2020.
Stork, C.; Embruch, G.; Šícho, M.; de Bruyn Kops, C.; Chen, Y.;
Svozil, D.; Kirchmair, J. NERDD: a web portal providing access to in
silico tools for drug discovery. Bioinformatics