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17
375
Ki
float64
-4.97
0.35
CN1CCC(O)(c2ccc(Cl)c(Cl)c2)C([C@@H](O)c2ccc(Cl)c(Cl)c2)C1
-3.617
CN1CCC(O)(c2ccc(Cl)c(Cl)c2)C(C(=O)c2ccc(Cl)c(Cl)c2)C1
-1.037426
Cc1ccc(C2OC(=O)OC3(c4ccc(C)cc4)CCN(C)CC23)cc1
-3.913284
Cc1ccc([C@H](O)C2CN(C)CCC2(O)c2ccc(C)cc2)cc1
-4.02735
CN1CCC(O)(c2ccc(F)cc2)C(C(=O)c2ccc(F)cc2)C1
-3.755875
CN1CCC(O)(c2ccc(Cl)cc2Cl)C([C@@H](O)c2ccc(Cl)cc2Cl)C1
-3.638489
CN1CCC(O)(c2ccc(Cl)cc2Cl)C(C(=O)c2ccc(Cl)cc2Cl)C1
-3.562293
CN(C)Cc1ccccc1Sc1ccc(C#N)cc1N
-3.153205
COCCCC/C(=N\OCCN)c1ccc(C(F)(F)F)cc1
-0.356026
CNCCC(Oc1ccccc1OC)c1ccccc1
-1.477121
CN(C)Cc1ccccc1Sc1ccc(C(F)(F)F)cc1N
-3.309204
COc1ccc(Sc2ccccc2CN(C)C)c(N)c1
-3.42341
CN(C)Cc1ccccc1Sc1ccc(Cl)cc1N
-2.060698
CN1C2CCC1[C@@H](/C=C/Cl)[C@@H](c1ccc(Cl)cc1)C2
0.259637
CN(CCOC(c1ccccc1)c1ccccc1)C1CCN(CCCc2ccccc2)CC1
-1.477121
OC1(c2ccc(Cl)cc2)c2cccc(F)c2C2=NCCN21
-1.363612
Fc1ccc(CCNC2CCN(CCOC(c3ccccc3)c3ccccc3)CC2)cc1
-2.041393
Oc1ccc(C2(O)c3ccccc3C3=NCCCN32)cc1
-1.940018
CN(CCCc1ccccc1)C1CCN(CCOC(c2ccccc2)c2ccccc2)CC1
-2.531479
c1ccc(CCCNC2CCN(CCOC(c3ccccc3)c3ccccc3)CC2)cc1
-2.20412
COc1cccc2c1C1=NCCN1C2(O)c1ccc(Cl)cc1
-0.462398
Fc1ccc(CCN2CCCC(CNCCOC(c3ccccc3)c3ccccc3)C2)cc1
-2
COc1ccc(C2(O)c3ccccc3C3=NCCCN32)cc1
-1.558709
OC1(c2ccccc2)c2ccccc2C2=NCCN21
-3.029384
c1ccc(CN2CCC(NCCOC(c3ccccc3)c3ccccc3)CC2)cc1
-1.919078
c1ccc(CCCN2CCCC(CNCCOC(c3ccccc3)c3ccccc3)C2)cc1
-1.255273
NCCc1ccc(O)c(O)c1
-3.80618
OC1(c2ccc(Cl)cc2)c2ccccc2C2=NCCCN21
-0.230449
OC1(c2ccc(Cl)cc2)c2cc(Cl)c(Cl)cc2C2=NCCN21
-0.021189
COC(=O)C1C(c2ccc(OC)c(OC)c2)CC2CCC1N2C
-3.186391
c1ccc(CCCNCC2CCCN(CCOC(c3ccccc3)c3ccccc3)C2)cc1
-1.845098
COc1ccc2c(c1)C(O)(c1ccc(Cl)cc1)N1CCN=C21
-0.778151
Fc1ccc(CCNCC2CCCN(CCOC(c3ccccc3)c3ccccc3)C2)cc1
-1.939519
CN(CCc1ccc(F)cc1)CC1CCCN(CCOC(c2ccccc2)c2ccccc2)C1
-1.954243
COc1cccc2c1C(O)(c1ccc(Cl)cc1)N1CCN=C21
-1.012837
Clc1ccc(C2c3ccccc3C3=NCCN32)cc1
-1.361728
OC1(c2ccc(Cl)cc2)c2ccc3ccccc3c2C2=NCCCN21
-0.303196
OC1(c2ccc3ccccc3c2)c2ccc3ccccc3c2C2=NCCN21
-0.451786
OC1(c2ccc(Cl)cc2)c2ccc3ccccc3c2C2=NCCN21
-1.434569
OC1(c2ccc(Cl)cc2)c2ccccc2C2=N[C@H]3CCCC[C@@H]3N21
-2.181844
OC1(c2ccc3ccccc3c2)c2ccccc2C2=NCCN21
-1.653213
Fc1ccc(C(OCCC2CCN(Cc3cc4ccccc4s3)CC2)c2ccc(F)cc2)cc1
-0.206826
Fc1ccc(C(OCCC2CCN(Cc3cc4ccccc4[nH]3)CC2)c2ccc(F)cc2)cc1
0.136677
Fc1ccc(C(OCCN2CCN(CCCc3ccccc3)CC2)c2ccc(F)cc2)cc1
-0.861634
Fc1ccc(C(OCCC2CCN(CCCc3ccccc3)CC2)c2ccc(F)cc2)cc1
-0.041393
Fc1ccc(C(OCCC2CCN(C/C=C/c3ccco3)CC2)c2ccc(F)cc2)cc1
0.004365
Fc1ccc(C(OCCC2CCN(Cc3cccc4ccccc34)CC2)c2ccc(F)cc2)cc1
-1.20412
Fc1ccc(C(OCCC2CCN(Cc3ccc4ccccc4c3)CC2)c2ccc(F)cc2)cc1
0.148742
Fc1ccc(C(OCCC2CCN(C/C=C/c3cccs3)CC2)c2ccc(F)cc2)cc1
-0.113943
Fc1ccc(C(OCCC2CCN(CC#Cc3ccccc3)CC2)c2ccc(F)cc2)cc1
-0.724276
Fc1ccc(C(OCCC2CCN(C/C=C/c3ccccc3)CC2)c2ccc(F)cc2)cc1
0.346787
Fc1ccc(C(OCCC2CCN(Cc3cc4ccccc4o3)CC2)c2ccc(F)cc2)cc1
-0.004321
CN1C2CCC1C(C(=O)Oc1ccccc1)C(c1ccc(Cl)cc1)C2
-0.720159
CN(C)CCCC1(c2ccc(F)cc2)OCc2cc(C#N)ccc21
-4.218536
Fc1ccc(C(OCCNCCCNCCc2ccccc2)c2ccc(F)cc2)cc1
-1.447158
O=C1CN(CCc2ccccc2)CCN1CCOC(c1ccc(F)cc1)c1ccc(F)cc1
-3.155336
CN(CCCc1ccccc1)CCCN(C)CCOC(c1ccccc1)c1ccccc1
-2.08636
COC(=O)C1C(c2ccc(/C=C\Br)cc2)CC2CCC1N2C
-0.491362
O=C(Cc1ccccc1)NCCCNCCOC(c1ccc(F)cc1)c1ccc(F)cc1
-1.78533
O=C(Cc1ccccc1)NCCNCCOC(c1ccc(F)cc1)c1ccc(F)cc1
-1.732394
CN(CCCN(C)CCc1ccccc1)CCOC(c1ccccc1)c1ccccc1
-2.507856
O=C(CCc1ccccc1)NCCNCCOC(c1ccc(F)cc1)c1ccc(F)cc1
-1.70757
COC(=O)C1C(c2ccc(/C=C\I)cc2)CC2CCC1N2C
-1.342423
CN(CCOC(c1ccccc1)c1ccccc1)CCN(C)CCc1ccc(F)cc1
-1.477121
CN(CCOC(c1ccc(F)cc1)c1ccc(F)cc1)CCN(C)CCOC(c1ccc(F)cc1)c1ccc(F)cc1
-1.414973
O=C(Cc1ccc(F)cc1)NCCNCCOC(c1ccc(F)cc1)c1ccc(F)cc1
-1.732394
O=C1CN(CCOC(c2ccccc2)c2ccccc2)CCN1CCCc1ccccc1
-3.913814
COC(=O)C1C2CCC(CC1c1ccc(C=C(Br)Br)cc1)N2
-0.748188
C[C@H]1CC[C@H](C)N1CCCOc1ccc(-c2ccc(C(=O)N3CCOCC3)cc2)cc1
-3.69
COC(=O)C1C(c2ccc(C=C(Br)Br)cc2)CC2CCC1N2C
-0.828015
O=C(CCc1ccc(Br)cc1)N/C=C/NCCOC(c1ccc(F)cc1)c1ccc(F)cc1
-2.70757
COC(=O)C1C2CCC(CC1c1ccc(/C=C\Br)cc1)N2
-0.591065
CN1C2CCC1C(C(=O)NCCCCCCNC(=O)C1C(c3ccc(Cl)cc3)CC3CCC1N3C)C(c1ccc(Cl)cc1)C2
-1.264818
CN1C2CCC1C(C(=O)NCCCCNC(=O)C1C(c3ccc(Cl)cc3)CC3CCC1N3C)C(c1ccc(Cl)cc1)C2
-1.33646
CN1C2CCC1C(C(=O)NCCCCCCCCNC(=O)C1C(c3ccc(Cl)cc3)CC3CCC1N3C)C(c1ccc(Cl)cc1)C2
-0.826075
CN1C2CCC1C(C(=O)NCCCNC(=O)C1C(c3ccc(Cl)cc3)CC3CCC1N3C)C(c1ccc(Cl)cc1)C2
-1.813581
CN([C@H]1C[C@@H](c2ccc(Cl)c(Cl)c2)c2ccccc21)C(C)(C)C
-3.447158
CC(C)N(C)[C@@H]1C[C@@H](c2ccc(Cl)c(Cl)c2)c2ccccc21
-2.267172
CC(C)N[C@@H]1C[C@@H](c2ccc(Cl)c(Cl)c2)c2ccccc21
-1.618048
COC(=O)[C@H]1C2CC[C@@H]3C[C@@H]1/C(=C/c1ccc(Cl)c(Cl)c1)CN23
-3.521138
COC(=O)C1C(c2cccc(-c3ccco3)c2)CC2CCC1N2C
-1.732394
COC(=O)C1C2CCC(CC1c1cccc(I)c1)N2
-2.641474
COC(=O)C1C(c2cccc(-c3ccccc3)c2)CC2CCC1N2C
-0.945961
COC(=O)C1C(c2cccc(-c3ccsc3)c2)CC2CCC1N2C
-1.474216
COC(=O)C1C(c2cccc(I)c2)CC2CCC1N2C
-2.260071
CC(C)OC(=O)C1C2CCC(C[C@@H]1c1ccc(Cl)cc1)CN2C
0.045757
COC(=O)C1C2CCC(C[C@@H]1c1ccc(I)cc1)CN2C
0.318759
CN(C)C[C@H]1C2CCC(C2)[C@@H]1c1ccc2cc(F)ccc2c1
-1.69897
CN(C)C[C@H]1C2CCC(C2)[C@@H]1c1ccc2cc(O)ccc2c1
-0.778151
CNC[C@H]1C2CCC(C2)[C@@H]1c1ccc2cc(F)ccc2c1
-1.857332
COc1ccc2cc([C@H]3C4CCC(C4)[C@@H]3CN(C)C)ccc2c1
-1.80618
CN(C)C[C@H]1C2CCC(CC2)[C@@H]1c1cccc(Cl)c1
-1.80618
CN(C)CC1C2CCC(C2)C1c1cccc2ccccc12
-2.633468
CNC[C@H]1C2CCC(C2)[C@@H]1c1ccc2cc(OC)ccc2c1
-1.778151
CSc1ccc([C@H]2CN3CCC[C@H]3c3ccccc32)cc1
-2.049218
CN1C2CCC1C(C(=O)OCCCF)C(c1ccc(Br)cc1)C2
-0.69897
O=C(OCCCF)C1C2CCC(CC1c1ccc(Br)cc1)N2
-0.69897
CN1C2CCC1C(C(=O)OCCF)C(c1ccc(Br)cc1)C2
-0.69897
O=C(OCCF)C1C2CCC(CC1c1ccc(Br)cc1)N2
-0.69897
O=C(OCCF)C1C2CCC(CC1c1ccc(I)cc1)N2
-0.69897
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MoleculeACE ChEMBL238 Ki

ChEMBL238 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 Sodium-dependent dopamine transporter target.

Characteristic Description
Tasks 1
Task type regression
Total samples 1052
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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