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SMILES
stringlengths
14
97
Ki
float64
-4.67
1.44
O=S1(=O)c2ccccc2CCC12CCN(Cc1ccccc1)CC2
-1.30103
COc1ccc(N2C[C@H](CN3CCC(O)(c4ccsc4)CC3)OC2=O)cc1
-1.531479
COc1ccc(N2C[C@H](CN3CCC(O)(c4ccc5c(c4)OCO5)CC3)OC2=O)cc1
-1.278754
CNC(=O)CC1Cc2ccccc2C2(CCN(Cc3ccccc3)CC2)O1
-2.230449
OCC1OC2(CCN(Cc3ccccc3)CC2)c2ccccc21
-0.752816
COC1Cc2ccccc2C2(CCN(Cc3ccccc3)CC2)O1
-0.11059
COc1ccc(N2C[C@H](CN3CCC(O)(c4ccc(OC)c(OC)c4)CC3)OC2=O)cc1
-1.255273
O=C1O[C@@H](CN2CCC(O)(c3ccc4c(c3)OCO4)CC2)CN1c1ccc(OC(F)(F)F)cc1
-0
CCOC(=O)CC1Cc2ccccc2C2(CCN(Cc3ccccc3)CC2)O1
-2.262451
CN1CCN(C2=Nc3cc(Cl)ccc3Nc3ccccc32)CC1
-3.929419
OCCC1OC2(CCN(Cc3ccccc3)CC2)c2ccccc21
-0.861534
OCC1Cc2ccccc2C2(CCN(Cc3ccccc3)CC2)O1
-0.854913
COc1ccc(N2C[C@H](CN3CCC(O)(c4ccc(C)cc4)CC3)OC2=O)cc1
-1.278754
COc1ccc(N2C[C@H](CN3CCC(O)(c4cccs4)CC3)OC2=O)cc1
-1.612784
COc1ccc(N2C[C@H](CN3CC=C(c4ccc5c(c4)OCO5)CC3)OC2=O)cc1
-2.230449
COc1ccc(N2C[C@H](CN3CCC(c4ccc5c(c4)OCO5)CC3)OC2=O)cc1
-1.278754
O=C1O[C@@H](CN2CCC(O)(c3ccc4c(c3)OCO4)CC2)CN1c1ccc(F)cc1
-1.20412
O=C1O[C@@H](CN2CCC(O)(c3ccc4c(c3)OCO4)CC2)CN1c1ccc(O)cc1
-1.380211
N#CC1OC2(CCN(Cc3ccccc3)CC2)c2ccccc21
-0.338456
N#CC1Cc2ccccc2C2(CCN(Cc3ccccc3)CC2)O1
-0.187521
CCN1CCC[C@H]1CNC(=O)c1c(OC)ccc(Br)c1OC
-1.740363
OC1Cc2ccccc2C2(CCN(Cc3ccccc3)CC2)O1
-0.33646
OCCC1Cc2ccccc2C2(CCN(Cc3ccccc3)CC2)O1
-1.103804
COc1ccc(N2C[C@H](CN3CCC(O)(c4ccc5c(c4)CCO5)CC3)OC2=O)cc1
-1.230449
O=C1O[C@@H](CN2CCC(O)(c3ccc4c(c3)OCO4)CC2)CN1c1ccccc1
-0.69897
O=C1O[C@@H](CN2CCC(O)(c3ccc4c(c3)OCO4)CC2)CN1c1ccc(OCC2CC2)cc1
-0.30103
CCC1Cc2ccccc2C2(CCN(Cc3ccccc3)CC2)O1
-1.068186
CC(=O)Oc1ccc(N2C[C@H](CN3CCC(O)(c4ccc5c(c4)OCO5)CC3)OC2=O)cc1
-2.39794
CCOC(=O)C1Cc2ccccc2C2(CCN(Cc3ccccc3)CC2)O1
-0.969882
c1ccc(CN2CCC3(CCc4ccccc4O3)CC2)cc1
0.208801
O=C(CCCN1CCC(O)(c2ccc(Cl)cc2)CC1)c1ccc(F)cc1
-0.729547
COc1ccc(N2C[C@H](CN3CCC(O)(c4ccc5c(c4)OCCO5)CC3)OC2=O)cc1
-1.491362
O=C1O[C@@H](CN2CCC(O)(c3ccc4c(c3)OCO4)CC2)CN1c1ccc(Cl)cc1
-0.477121
COC1OC2(CCN(Cc3ccccc3)CC2)c2ccccc21
-0.056905
N#CCC1Cc2ccccc2C2(CCN(Cc3ccccc3)CC2)O1
-1.201397
OC1OC2(CCN(Cc3ccccc3)CC2)c2ccccc21
-0.865104
CCOC(=O)C1OC2(CCN(Cc3ccccc3)CC2)c2ccccc21
-0.951823
COc1ccc(N2C[C@H](CN3CCC(O)(c4ccc(Cl)c(C(F)(F)F)c4)CC3)OC2=O)cc1
-1.176091
N#CCC1OC2(CCN(Cc3ccccc3)CC2)c2ccccc21
-0.334454
c1ccc(CN2CCC3(CCc4ccccc4S3)CC2)cc1
0.264082
O[S+]1c2ccccc2CCC12CCN(Cc1ccccc1)CC2
-1.39794
COc1ccc(N2C[C@H](CN3CCC(O)(c4ccc5c(c4)OC4(CCCC4)O5)CC3)OC2=O)cc1
-1.792392
CNCCC1Cc2ccccc2C2(CCN(Cc3ccccc3)CC2)O1
-2.170262
CCOC(=O)CC1OC2(CCN(Cc3ccccc3)CC2)c2ccccc21
-1.178977
O=CCC1Cc2ccccc2C2(CCN(Cc3ccccc3)CC2)O1
-1.741152
COc1ccc(N2C[C@H](CN3CCC(O)(c4ccc5c(c4)OC(C)(C)O5)CC3)OC2=O)cc1
-1.20412
CCOc1ccc(N2C[C@H](CN3CCC(O)(c4ccc5c(c4)OCO5)CC3)OC2=O)cc1
-1.176091
O=C1c2ccccc2CCC12CCN(Cc1ccccc1)CC2
-0.146128
O=C(Cc1c[nH]cn1)NC1CCN(Cc2ccccc2)CC1
-2.395082
O=C(Cc1ccccn1)NC1CCN(Cc2ccccc2)CC1
-2.371991
COC(=O)[C@H]1[C@@H](OC(=O)c2ccccc2)C[C@@H]2CC[C@H]1N2C
-3.945961
O=C(Cc1ccccc1)NC1CCN(Cc2cccc(C(F)(F)F)c2)CC1
-2.150327
O=C(Cc1ccccc1)NC1CCN(Cc2ccc([N+](=O)[O-])cc2)CC1
-1.280351
COc1ccc2c(c1)C(CC(=O)NC1CCN(Cc3ccccc3)CC1)CC2=O
-2.475032
O=C(Cc1ccccc1)NC1CCN(Cc2ccc(F)cc2)CC1
-0.818885
O=C(Cc1ccccc1)NC1CCN(Cc2ccc(I)cc2)CC1
-0.955688
O=C(Cc1cccc(Br)c1)NC1CCN(Cc2cccc(I)c2)CC1
-0.542825
C[C@@H]1CN(CCCN(c2ccc(F)cc2)c2ccc(F)cc2)C[C@@H](C)N1CCc1ccc(Cl)c(Cl)c1
-1.511883
O=C(Cc1ccc(I)cc1)NC1CCN(Cc2ccccc2)CC1
-1.140508
OC(CCN1CCN(CCCN(c2ccc(F)cc2)c2ccc(F)cc2)CC1)c1ccccc1
-2.570543
Fc1ccc(C(OCCN2CCN(CCCc3ccccc3)CC2)c2ccc(F)cc2)cc1
-2.502427
O=C(Nc1cccc2ccccc12)NC1CCN(Cc2ccccc2)CC1
-1.35526
O=C(Cc1c[nH]c2ccc(Br)cc12)NC1CCN(Cc2ccccc2)CC1
-1.715502
C[C@@H]1CN(CCCN(c2ccc(F)cc2)c2ccc(F)cc2)C[C@@H](C)N1CCCc1ccccc1
-1.045323
C[C@@H]1CN(CCCn2c3ccccc3c3ccccc32)C[C@@H](C)N1CCCc1ccccc1
-2.017033
C[C@@H]1CN(CCCN(c2ccccc2)c2ccccc2)C[C@@H](C)N1
-2.139879
O=C(Cc1ccc2ccccc2c1)NC1CCN(Cc2ccccc2)CC1
-1.790778
O=C(Cc1c[nH]c2ccccc12)NC1CCN(Cc2ccccc2)CC1
-1.037426
O=C(Cc1cccc2ccccc12)NC1CCN(Cc2ccccc2)CC1
-0.666518
O=C(Cc1cccc(Br)c1)NC1CCN(Cc2ccc(F)cc2)CC1
-0.082785
C[C@@H]1CN(CCCn2c3ccccc3c3ccccc32)C[C@@H](C)N1C
-2.741939
O=C(Cc1ccccc1)NC1CCN(Cc2ccc(F)c(F)c2)CC1
-0.799341
O=C(Cc1cccc(F)c1)NC1CCN(Cc2cccc(I)c2)CC1
-0.632457
O=C(Cc1ccncc1)NC1CCN(Cc2ccccc2)CC1
-2.474901
CCN(CC)CCOCCOC(=O)C1(c2ccccc2)CCCC1
-1.290035
O=C(Cc1cccs1)NC1CCN(Cc2ccccc2)CC1
-0.594393
O=C(Cc1ccccc1F)NC1CCN(Cc2ccc(I)cc2)CC1
-0.623249
O=C(Cc1cccc(F)c1)NC1CCN(Cc2ccc(F)cc2)CC1
-0.741152
O=C(Cc1ccccc1F)NC1CCN(Cc2ccc(F)cc2)CC1
-0.498311
O=C(Cc1ccccc1)NC1CCN(Cc2ccc(C(F)(F)F)cc2)CC1
-1.939769
O=C(Cc1ccccc1)NC1CCN(Cc2ccc3c(c2)OCO3)CC1
-0.598791
C[C@@H]1CN(CCCN(c2ccc(F)cc2)c2ccc(F)cc2)C[C@@H](C)N1
-2.093422
COc1ccc2[nH]cc(CC(=O)NC3CCN(Cc4ccccc4)CC3)c2c1
-2.098332
O=C(Cc1ccccc1)NC1CCN(Cc2ccc3ccccc3c2)CC1
-1.378034
O=C(Cc1ccsc1)NC1CCN(Cc2ccccc2)CC1
-0.857332
O=C(Cc1ccccc1F)NC1CCN(Cc2cccc(I)c2)CC1
-1.187803
O=C(Cc1cccc(Cl)c1)NC1CCN(Cc2ccc(I)cc2)CC1
-0.506505
C[C@@H]1CN(CCCN(c2ccccc2)c2ccccc2)C[C@@H](C)N1CCCc1ccccc1
-1.987666
C[C@@H]1CN(CCCn2c3ccccc3c3cc([N+](=O)[O-])ccc32)C[C@@H](C)N1
-2.775246
Fc1ccc(N(CCCN2CCN(CCCc3ccccc3)CC2)c2ccc(F)cc2)cc1
-1.820858
O=C(Cc1cccc(Cl)c1)NC1CCN(Cc2ccc(F)cc2)CC1
-0.060698
Fc1ccc(N(CCCN2CCN(Cc3ccccc3)CC2)c2ccc(F)cc2)cc1
-1.117271
O=C(Cc1ccccc1)NC1CCN(Cc2cccc(I)c2)CC1
-0.829304
O=C(Cc1ccccc1)NC1CCN(Cc2ccccc2F)CC1
-1.006894
O=C(Cc1ccccc1)NC1CCN(Cc2ccccc2I)CC1
-2.540204
O=C(OCCN1CCOCC1)C1(c2ccccc2)CCCCC1
-1.667453
O=C(Cc1cccnc1)NC1CCN(Cc2ccccc2)CC1
-2.429994
O=C(Cc1ccccc1)NC1CCN(Cc2cccc(F)c2)CC1
-0.914872
O=C(Cc1cccc(Br)c1)NC1CCN(Cc2ccc(I)cc2)CC1
-0.25042
O=C(Cc1ccccc1)NC1CCN(Cc2ccccc2)CC1
-0.591065
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MoleculeACE ChEMBL287 Ki

ChEMBL287 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 Sigma non-opioid intracellular receptor 1 target.

Characteristic Description
Tasks 1
Task type regression
Total samples 1328
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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