license: cc-by-sa-3.0
task_categories:
- tabular-classification
- graph-ml
- text-classification
tags:
- chemistry
- biology
- medical
pretty_name: ChEMBL2971 Ki
size_categories:
- n<1K
configs:
- config_name: default
data_files:
- split: train
path: chembl2971_ki.csv
MoleculeACE ChEMBL2971 Ki
ChEMBL2971 dataset, originally part of ChEMBL database [1], processed in MoleculeACE [2] for activity cliff evaluation. It is intended to be use through scikit-fingerprints library.
The task is to predict the inhibitor constant (Ki) of molecules against the Tyrosine-protein kinase jak2 target.
| Characteristic | Description |
|---|---|
| Tasks | 1 |
| Task type | regression |
| Total samples | 976 |
| Recommended split | activity_cliff |
| Recommended metric | RMSE |
References
[1] B. Zdrazil et al., “The ChEMBL Database in 2023: a drug discovery platform spanning multiple bioactivity data types and time periods,” Nucleic Acids Research, vol. 52, no. D1, Nov. 2023, doi: https://doi.org/10.1093/nar/gkad1004.
[2] D. van Tilborg, A. Alenicheva, and F. Grisoni, “Exposing the Limitations of Molecular Machine Learning with Activity Cliffs,” Journal of Chemical Information and Modeling, vol. 62, no. 23, pp. 5938–5951, Dec. 2022, doi: https://doi.org/10.1021/acs.jcim.2c01073.