---
license: cc-by-sa-3.0
task_categories:
- tabular-classification
- graph-ml
- text-classification
tags:
- chemistry
- biology
- medical
pretty_name: ChEMBL238 Ki
size_categories:
- 1K<n<10K
configs:
- config_name: default
  data_files:
  - split: train
    path: chembl238_ki.csv
---
# MoleculeACE ChEMBL238 Ki

ChEMBL238 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 Sodium-dependent dopamine transporter target.

| **Characteristic** |      **Description**     |
|:------------------:|:------------------------:|
|        Tasks       |            1             |
|      Task type     |        regression        |
|    Total samples   |           1052           |
|  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.
‌