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.
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.
doi:10.1093/bioinformatics/btz695