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COC(=O)[C@@H]1C[C@H](OC(=O)C(C)C)C(=O)[C@H]2[C@@]1(C)CC[C@H]1C(=O)O[C@H](c3ccoc3)C[C@]21C
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COC(=O)[C@@H]1C[C@H](OC(=O)c2ccccc2)C(=O)[C@H]2[C@@]1(C)CC[C@H]1C(=O)O[C@H](c3ccoc3)C[C@]21C
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COC(=O)[C@@H]1C[C@H](OC(=O)c2cccnc2)C(=O)[C@H]2[C@@]1(C)CC[C@H]1C(=O)O[C@H](c3ccoc3)C[C@]21C
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COC(=O)[C@@H]1C[C@H](OS(C)(=O)=O)C(=O)[C@H]2[C@@]1(C)CC[C@H]1C(=O)O[C@H](c3ccoc3)C[C@]21C
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COC(=O)[C@@H]1C[C@H](OC(=O)Nc2ccccc2)C(=O)[C@H]2[C@@]1(C)CC[C@H]1C(=O)O[C@H](c3ccoc3)C[C@]21C
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CCS(=O)(=O)NCc1ccc(CC(=O)N(C)[C@H](CN2CC[C@H](O)C2)c2ccccc2)cc1
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CNC(=O)NCc1ccc(CC(=O)N(C)[C@H](CN2CCCC2)c2ccccc2)cc1
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CC(C)S(=O)(=O)NCc1ccc(CC(=O)N(C)[C@H](CN2CC[C@H](O)C2)c2ccccc2)cc1
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CN(C(=O)Cc1ccccc1CNS(C)(=O)=O)[C@H](CN1CCCC1)c1ccccc1
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CN(C(=O)Cc1ccc(CNS(=O)(=O)Cc2cccs2)cc1)[C@H](CN1CC[C@H](O)C1)c1ccccc1
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CN(C(=O)Cc1ccc(Cl)c(Cl)c1)[C@H](CN1CCCC1)c1ccccc1
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CN(C(=O)Cc1cccc(CNS(C)(=O)=O)c1)[C@H](CN1CCCC1)c1ccccc1
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CCCS(=O)(=O)NCc1ccc(CC(=O)N(C)[C@H](CN2CC[C@H](O)C2)c2ccccc2)cc1
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CN(C(=O)Cc1ccc(NS(C)(=O)=O)cc1)[C@H](CN1CC[C@H](O)C1)c1ccccc1
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CN(C(=O)Cc1ccccc1)[C@H](CN1CCCC1)c1ccccc1
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CCCCS(=O)(=O)NCc1ccc(CC(=O)N(C)[C@H](CN2CC[C@H](O)C2)c2ccccc2)cc1
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CN(C(=O)Cc1ccc(CCNS(C)(=O)=O)cc1)[C@H](CN1CC[C@H](O)C1)c1ccccc1
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CN(C(=O)Cc1ccccc1CN)[C@H](CN1CCCC1)c1ccccc1
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CN(C(=O)Cc1ccc(CNS(C)(=O)=O)cc1)[C@H](CN1CCCC1)c1ccccc1
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CN(C(=O)Cc1ccc(CNS(=O)(=O)c2ccccc2)cc1)[C@H](CN1CC[C@H](O)C1)c1ccccc1
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CN(C(=O)Cc1ccc(CCCNS(C)(=O)=O)cc1)[C@H](CN1CC[C@H](O)C1)c1ccccc1
| -0.755875
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CN(C(=O)Cc1cccc(CN)c1)[C@H](CN1CCCC1)c1ccccc1
| -1.176091
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CN(C(=O)Cc1ccc(CNS(=O)(=O)c2ccc(F)cc2)cc1)[C@H](CN1CC[C@H](O)C1)c1ccccc1
| -1.342423
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CN(C(=O)Cc1ccc2c(c1)CN(S(C)(=O)=O)C2)[C@H](CN1CCCC1)c1ccccc1
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CN(C(=O)Cc1ccc(CN)cc1)[C@H](CN1CCCC1)c1ccccc1
| -1.908485
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CN(C(=O)Cc1ccc(CNS(C)(=O)=O)cc1)[C@H](CN1CC[C@H](O)C1)c1ccccc1
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|
End of preview. Expand
in Data Studio
MoleculeACE ChEMBL237 EC50
ChEMBL237 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 half maximal effective concentration (EC50) of molecules against the Kappa-type opioid receptor target.
| Characteristic | Description |
|---|---|
| Tasks | 1 |
| Task type | regression |
| Total samples | 955 |
| 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.
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