scikit-fingerprints/MoleculeACE_chembl214_ki
MoleculeACE ChEMBL214 Ki ChEMBL214 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 5-hydroxytryptamine receptor 1a target. Characteristic Description Tasks 1 Task type regression Total samples 3317 Recommended split activity_cliff Recommended metric RMSE… See the full description on the dataset page: https://huggingface.co/datasets/scikit-fingerprints/MoleculeACE_chembl214_ki.
MoleculeACE ChEMBL214 Ki
ChEMBL214 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 library.
The task is to predict the inhibitor constant (Ki) of molecules against the 5-hydroxytryptamine receptor 1a target.
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.