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SMILES
stringlengths
18
150
Ki
float64
-4.86
1.89
CN(C)Cc1ccccc1Sc1ccc(C#N)cc1N
-0.041393
CN(C)Cc1ccccc1Sc1ccc(C(F)(F)F)cc1N
0.481486
COc1ccc(Sc2ccccc2CN(C)C)c(N)c1
-0.276462
CN(C)Cc1ccccc1Sc1ccc(Cl)cc1N
0.568636
Fc1ccc([C@@H]2CCNC[C@H]2COc2ccc3c(c2)OCO3)cc1
0.661986
c1ccc(CC(c2ccccc2)c2ccncc2)cc1
-2.959041
c1ccc(C(c2ccccc2)c2ccccn2)cc1
-4.544068
c1ccc(C(c2ccccc2)c2ccncc2)cc1
-3.064458
c1ccc(C(c2ccccc2)c2cccnc2)cc1
-4.100371
CN(CCOC(c1ccccc1)c1ccccc1)C1CCN(CCCc2ccccc2)CC1
-3.146128
OC1(c2ccc(Cl)cc2)c2cccc(F)c2C2=NCCN21
-1.580925
OC1(c2ccc(Cl)cc2)c2ccccc2C2=NCCN21
-1.684247
Fc1ccc(CCNC2CCN(CCOC(c3ccccc3)c3ccccc3)CC2)cc1
-2.342423
Oc1ccc(C2(O)c3ccccc3C3=NCCCN32)cc1
-1.886491
CN(CCCc1ccccc1)C1CCN(CCOC(c2ccccc2)c2ccccc2)CC1
-1.939519
c1ccc(CCCNC2CCN(CCOC(c3ccccc3)c3ccccc3)CC2)cc1
-1.740363
COc1cccc2c1C1=NCCN1C2(O)c1ccc(Cl)cc1
-2.939519
Fc1ccc(CCN2CCCC(CNCCOC(c3ccccc3)c3ccccc3)C2)cc1
-2.544068
COc1ccc(C2(O)c3ccccc3C3=NCCCN32)cc1
-1.747412
OC1(c2ccccc2)c2ccccc2C2=NCCN21
-3.245513
c1ccc(CN2CCC(NCCOC(c3ccccc3)c3ccccc3)CC2)cc1
-3.334454
CN1CCC(=C2c3ccccc3C=Cc3ccccc32)CC1
-3.612784
CN(C)CCCN1c2ccccc2CCc2ccccc21
-0.78665
c1ccc(CCCN2CCCC(CNCCOC(c3ccccc3)c3ccccc3)C2)cc1
-2.322219
c1ccc2c(c1)Cc1ccccc1C21CCNC1
-3.591065
OC1(c2ccc(Cl)cc2)c2ccccc2C2=NCCCN21
-1.591065
OC1(c2ccc(Cl)cc2)c2cc(Cl)c(Cl)cc2C2=NCCN21
-3.129368
COC(=O)C1C(c2ccc(OC)c(OC)c2)CC2CCC1N2C
-1.662758
c1ccc(CCCNCC2CCCN(CCOC(c3ccccc3)c3ccccc3)C2)cc1
-2.50515
COc1ccc2c(c1)C(O)(c1ccc(Cl)cc1)N1CCN=C21
-3.012837
Fc1ccc(CCNCC2CCCN(CCOC(c3ccccc3)c3ccccc3)C2)cc1
-2.94939
CN(CCc1ccc(F)cc1)CC1CCCN(CCOC(c2ccccc2)c2ccccc2)C1
-2.724276
COc1cccc2c1C(O)(c1ccc(Cl)cc1)N1CCN=C21
-3.298853
Clc1ccc(C2c3ccccc3C3=NCCN32)cc1
-1.414973
OC1(c2ccc(Cl)cc2)c2ccc3ccccc3c2C2=NCCCN21
-0.740363
COc1ccc2[nH]c(-c3ccccc3)c(CCN(C)C)c2c1
-3.672098
COc1cccc2c1O[C@@H](CN1C3C=C(n4ccc5cc(F)ccc54)CC1CC3)CO2
-0.929419
OC1(c2ccc3ccccc3c2)c2ccc3ccccc3c2C2=NCCN21
-1.041393
OC1(c2ccc(Cl)cc2)c2ccc3ccccc3c2C2=NCCN21
-2.350248
COc1cccc2c1O[C@@H](CN1C3C=C(c4ccc(Cl)c(Cl)c4)CC1CC3)CO2
-0.671173
COc1cccc2c1O[C@@H](CN1C3C=C(c4ccc5ccccc5c4)CC1CC3)CO2
-0.146128
OC1(c2ccc(Cl)cc2)c2ccccc2C2=N[C@H]3CCCC[C@@H]3N21
-2.778151
OC1(c2ccc3ccccc3c2)c2ccccc2C2=NCCN21
-0.361728
COc1cccc2c1O[C@@H](CN1C3C=C(c4cccc5ccccc45)CC1CC3)CO2
-1.414973
COc1cccc2c1O[C@@H](CN1C3C=C(c4cccc5cccnc45)CC1CC3)CO2
-1.518514
Fc1ccc(C(OCCC2CCN(Cc3cc4ccccc4s3)CC2)c2ccc(F)cc2)cc1
-2.390935
Fc1ccc(C(OCCC2CCN(Cc3cc4ccccc4[nH]3)CC2)c2ccc(F)cc2)cc1
-1.944483
Fc1ccc(C(OCCN2CCN(CCCc3ccccc3)CC2)c2ccc(F)cc2)cc1
-2.10721
Fc1ccc(C(OCCC2CCN(CCCc3ccccc3)CC2)c2ccc(F)cc2)cc1
-1.832509
Fc1ccc(C(OCCC2CCN(C/C=C/c3ccco3)CC2)c2ccc(F)cc2)cc1
-1.612784
Fc1ccc(C(OCCC2CCN(Cc3cccc4ccccc34)CC2)c2ccc(F)cc2)cc1
-2.568202
Fc1ccc(C(OCCC2CCN(Cc3ccc4ccccc4c3)CC2)c2ccc(F)cc2)cc1
-2.359835
Fc1ccc(C(OCCC2CCN(C/C=C/c3cccs3)CC2)c2ccc(F)cc2)cc1
-1.653213
Fc1ccc(C(OCCC2CCN(CC#Cc3ccccc3)CC2)c2ccc(F)cc2)cc1
-2.214844
Fc1ccc(C(OCCC2CCN(C/C=C/c3ccccc3)CC2)c2ccc(F)cc2)cc1
-1.672098
Fc1ccc(C(OCCC2CCN(Cc3cc4ccccc4o3)CC2)c2ccc(F)cc2)cc1
-1.929419
CN1C2CCC1C(C(=O)Oc1ccccc1)C(c1ccc(Cl)cc1)C2
-2.591065
Fc1ccc(C(OCCNCCCNCCc2ccccc2)c2ccc(F)cc2)cc1
-2.09691
O=C1CN(CCc2ccccc2)CCN1CCOC(c1ccc(F)cc1)c1ccc(F)cc1
-2.056905
CN(CCCc1ccccc1)CCCN(C)CCOC(c1ccccc1)c1ccccc1
-2.641474
COC(=O)C1C(c2ccc(/C=C\Br)cc2)CC2CCC1N2C
1.09691
O=C(Cc1ccccc1)NCCCNCCOC(c1ccc(F)cc1)c1ccc(F)cc1
-3.060698
O=C(Cc1ccccc1)NCCNCCOC(c1ccc(F)cc1)c1ccc(F)cc1
-2.745075
CN(CCCN(C)CCc1ccccc1)CCOC(c1ccccc1)c1ccccc1
-3.184691
O=C(CCc1ccccc1)NCCNCCOC(c1ccc(F)cc1)c1ccc(F)cc1
-2.718502
COC(=O)C1C(c2ccc(/C=C\I)cc2)CC2CCC1N2C
0.958607
CN(CCOC(c1ccccc1)c1ccccc1)CCN(C)CCc1ccc(F)cc1
-2.740363
CN(CCOC(c1ccc(F)cc1)c1ccc(F)cc1)CCN(C)CCOC(c1ccc(F)cc1)c1ccc(F)cc1
-2.886491
O=C(Cc1ccc(F)cc1)NCCNCCOC(c1ccc(F)cc1)c1ccc(F)cc1
-2.545307
O=C1CN(CCOC(c2ccccc2)c2ccccc2)CCN1CCCc1ccccc1
-3.770852
COC(=O)C1C2CCC(CC1c1ccc(C=C(Br)Br)cc1)N2
1.09691
C[C@H]1CC[C@H](C)N1CCCOc1ccc(-c2ccc(C(=O)N3CCOCC3)cc2)cc1
-3.55
COC(=O)C1C(c2ccc(C=C(Br)Br)cc2)CC2CCC1N2C
-0.130334
O=C(CCc1ccc(Br)cc1)N/C=C/NCCOC(c1ccc(F)cc1)c1ccc(F)cc1
-2.770852
COC(=O)C1C2CCC(CC1c1ccc(/C=C\Br)cc1)N2
1.39794
CN1C2CCC1C(C(=O)NCCCCCCNC(=O)C1C(c3ccc(Cl)cc3)CC3CCC1N3C)C(c1ccc(Cl)cc1)C2
-2.009876
CN1C2CCC1C(C(=O)NCCCCNC(=O)C1C(c3ccc(Cl)cc3)CC3CCC1N3C)C(c1ccc(Cl)cc1)C2
-3.496335
CN1C2CCC1C(C(=O)NCCCCCCCCNC(=O)C1C(c3ccc(Cl)cc3)CC3CCC1N3C)C(c1ccc(Cl)cc1)C2
-0.897627
CN1C2CCC1C(C(=O)NCCCNC(=O)C1C(c3ccc(Cl)cc3)CC3CCC1N3C)C(c1ccc(Cl)cc1)C2
-2.552668
CNCCC(Oc1cccc2ccccc12)c1cccc(OC)c1
-0.342423
COc1cc2c(cc1OC)C1=NO[C@@H](CN3CCN(C/C=C(\C)c4ccc(F)cc4F)CC3)[C@@H]1CO2
-0.732394
COc1cc2c(cc1OC)C1=NO[C@@H](CN3CCN(C/C(C)=C/c4cccnc4)CC3)[C@@H]1CO2
-1.799341
CNCCC(Oc1cccc2ccccc12)c1ccc(Br)cc1
-0.612784
CNCCC(Oc1cccc2ccccc12)c1nccs1
-0.80618
COc1cc2c(cc1OC)C1=NO[C@@H](CN3CCN(C/C=C/c4cccc(F)c4)CC3)[C@@H]1CO2
-0.39794
CNCCC(Oc1cccc2ccccc12)c1cccc(C)c1
-0.60206
CNCC[C@H](Oc1cccc2c(OC)c(O)ccc12)c1cccs1
-3.563481
CNCCC(Oc1cccc2ccccc12)c1cccc(C(F)(F)F)c1
-1.278754
COc1cc2c(cc1OC)C1=NO[C@@H](CN3CCN(C/C=C/c4ccc(Cl)cc4)CC3)[C@@H]1CO2
-0.113943
COc1cc2c(cc1OC)C1=NO[C@@H](CN3CCN(C/C=C(\C)c4ccccc4OC)CC3)[C@@H]1CO2
-1.70757
COc1cc2c(cc1OC)C1=NO[C@@H](CN3CCN(C/C(C)=C/c4cccc(F)c4F)CC3)[C@@H]1CO2
-0.851258
CNCCC(Oc1cccc2ccccc12)c1ccccc1
-0.380211
COc1cc2c(cc1OC)C1=NO[C@@H](CN3CCN(C/C(C)=C/c4ccco4)CC3)[C@@H]1CO2
-1.322219
CNCCC(Oc1cccc2ccccc12)c1ccccc1C(F)(F)F
-1
COc1cc2c(cc1OC)C1=NO[C@@H](CN3CCN(C/C(C)=C/c4cccs4)CC3)[C@@H]1CO2
-0.431364
COc1cc2c(cc1OC)C1=NO[C@@H](CN3CCN(C/C(C)=C/c4ccsc4)CC3)[C@@H]1CO2
-0.278754
COc1cc2c(cc1OC)C1=NO[C@@H](CN3CCN(C/C=C(\C)c4ccsc4)CC3)[C@@H]1CO2
-0.681241
COc1cc2c(cc1OC)C1=NO[C@@H](CN3CCN(C/C=C/c4ccccc4F)CC3)[C@@H]1CO2
-1.113943
CNCCC(Oc1cccc2ccccc12)c1cccc(Br)c1
-0.880814
Cc1ccc(CN2CCN(CC(=O)N3c4ccccc4C[C@H]3C)CC2)cc1
-2.725912
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MoleculeACE ChEMBL228 Ki

ChEMBL228 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 serotonin transporter target.

Characteristic Description
Tasks 1
Task type regression
Total samples 1704
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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