Datasets:

scikit-fingerprints
/

MoleculeACE_chembl238_ki

+---
+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.
+‌

chembl238_ki.csv ADDED Viewed

	@@ -0,0 +1,1053 @@

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chembl238_ki_splits.json ADDED Viewed

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