---
license: cc-by-sa-3.0
task_categories:
- tabular-classification
- graph-ml
- text-classification
tags:
- chemistry
- biology
- medical
pretty_name: ChEMBL2835 Ki
size_categories:
- n<1K
configs:
- config_name: default
  data_files:
  - split: train
    path: chembl2835_ki.csv
---
# MoleculeACE ChEMBL2835 Ki

ChEMBL2835 dataset, originally part of ChEMBL database [[1]](#1), processed in MoleculeACE [[2]](#2) for activity cliff evaluation. It is intended to be use through [scikit-fingerprints](https://github.com/scikit-fingerprints/scikit-fingerprints) library.

The task is to predict the inhibitor constant (Ki) of molecules against the Tyrosine-protein kinase jak1 target.

| **Characteristic** |      **Description**     |
|:------------------:|:------------------------:|
|        Tasks       |            1             |
|      Task type     |        regression        |
|    Total samples   |           615            |
|  Recommended split |      activity_cliff      |
| Recommended metric |           RMSE           |

## References
<a id="1">[1]</a>
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.
‌

<a id="2">[2]</a> 
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.
‌