Calculate distance to nearest neighbor in the training set:

Calculate fingerprint-based Tanimoto distance to the nearest neighbor in training set. The nearer a query molecule is to the training set the more reliable is the prediction. Users are advised to make use of this feature. Note, however, that this will substantially increase calculation time.

Provide input molecule(s):

Enter SMILES

Example: O=C(N=C(N)N)c1sc2c(c(C#N)ccc2)c1

or upload a file with a list of SMILES

Choose file

or upload an sdf file

Choose file

or draw your own molecule

CYPlebrity

CYPlebrity is a collection of machine learning models for the prediction of whether or not a small organic compound is an inhibitor of different human CYPs. Currently, CYPlebrity covers CYPs 1A2, 2C9, 2C19, 2D6 and 3A4. The models are characterized by their wide applicability domain, a result of the training on a comprehensive bioactivity database compiled from the PubChem Bioassay database (AIDs 1851, 410, 883, 884, 899 and 891), the ChEMBL database and the ADME Database (Fujitsu).

The research was funded by Fujitsu and the WEB SERVICE IS PROVIDED FREE OF CHARGE FOR ACADEMIC RESEARCH.

For more information, see the Documentation page.

How to cite

If you are using CYPlebrity for your research, please cite all of the following publications:

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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 2020.
doi:10.1093/bioinformatics/btz695

CYPlebrity Machine learning models for the prediction of inhibitors of cytochrome P450 enzymes