Hit Dexter 2.0
How Likely is my Compound a Frequent Hitter?

Provide input molecule(s):


Example: C(=NN=c1ccc2ccccc2[nH]1)c1ccccn1

or upload a file with a list of SMILES
or draw your own molecule

Hit Dexter 2.0

Hit Dexter is a machine learning approach to estimate how likely a small molecule is to trigger a positive response in biochemical assays. The models were derived from a dataset of 250,000 compounds with experimentally determined activity for at least 50 different protein groups.

For more information, see the Documentation page.

How to cite

If you are using Hit Dexter 2.0 for your research, please cite all of the following publications:

Stork, C.; Chen, Y.; Šícho, M.; Kirchmair, J. Hit Dexter 2.0: Machine-Learning Models for the Prediction of Frequent Hitters. J. Chem. Inf. Model. 2019.
doi: 10.1021/acs.jcim.8b00677

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.