Hit Dexter is a machine learning approach to estimate how likely a small molecule is to trigger a positive response in biochemical and biological assays. The models were derived from a dataset of 250,000 compounds with experimentally determined activity for at least 100 different protein groups.
For more information, see the Documentation page.
If you are using Hit Dexter 3 for your research, please cite all of the following publications:
Stork, C.; Mathai, N.; Kirchmair, J. Computational prediction of frequent hitters in target-based and cell-based assays. Artificial Intelligence in the Life Sciences 2021.
doi: 10.1016/j.ailsci.2021.100007
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