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SMILES
stringlengths
16
130
Ki
float64
-4.95
1.74
COc1ccccc1N1CCN(Cc2ccn(-c3ccccc3)c2)CC1
-0.113943
c1ccc(N2CCN(Cc3ccn(-c4ccccc4)c3)CC2)cc1
-0.60206
CC1Cc2cccc3c2N1C(=O)C(N1CCN(Cc2ccc(Cl)cc2)CC1)CC3
-0.954243
CC1(C)Cc2cccc3c2N1C(=O)C(N1CCN(Cc2ccc(Cl)cc2)CC1)CC3
-1.278754
Cc1ccc(CN2CCN(C3CCc4cccc5c4N(CC5)C3=O)CC2)cc1
-0.60206
O=C(NCCCN1CCN(c2cccc(Cl)c2Cl)CC1)c1cccc2c1-c1ccccc1C2=O
-3.600973
Oc1nc2c(N3CCN(Cc4ccccc4)CC3)cccc2[nH]1
-1.158362
O=C(NCCCN1CCN(c2ccccc2)CC1)c1cccc2c1-c1ccccc1C2=O
-3.139879
O=C(NCCCN1CCN(c2cccc(Cl)c2Cl)CC1)c1cccc2c1Cc1ccccc1-2
-2.513218
CCN1C(=O)C(N2CCN(Cc3ccc(Cl)cc3)CC2)CCc2ccccc21
-3.179264
O=C1Cc2c(ccc(Cl)c2N2CCN(Cc3ccccc3)CC2)N1
-0.944483
Fc1ccc(C(OC2CC3CCC(C2)N3Cc2ccc(Cl)c(Cl)c2)c2ccc(F)cc2)cc1
-2.542825
O=C(NCCCCN1CCN(c2cccc(Cl)c2Cl)CC1)c1cccc2c1-c1ccccc1C2=O
-3.267172
CCN1CCC[C@H]1CNC(=O)c1c(OC)ccc(Br)c1OC
-3.587935
O=C1C(N2CCN(Cc3ccc(Cl)cc3)CC2)CCc2cccc3c2N1CC3
-0.60206
CN1C2CCC1CC(OC(c1ccccc1)c1ccc(Cl)cc1)C2
-3
O=C(NCCCN1CCN(c2ccccc2)CC1)c1ccc2c(c1)Cc1ccccc1-2
-2.83187
O=C(NCCCN1CCN(c2cccc(Cl)c2Cl)CC1)c1ccc2c(c1)Cc1ccccc1-2
-2.544068
O=C1Cc2c(cccc2N2CCN(Cc3ccccc3)CC2)N1
-0.477121
Cc1ccc(CN2CCN(C3CCc4cccc5c4N(C3=O)C(C)(C)C5)CC2)cc1
-1.079181
COc1cc(C)ccc1CN1CCN(C2CCc3cccc4c3N(C2=O)C(C)(C)C4)CC1
-1.531479
c1ccc(CN2CCN(c3cccc4[nH]cnc34)CC2)cc1
-1.672098
COc1ccc(Cl)cc1CN1CCN(C2CCc3cccc4c3N(C2=O)C(C)(C)C4)CC1
-1.462398
OC1(c2cccc(C(F)(F)F)c2)CCN(Cc2ccn(-c3ccccc3)c2)CC1
-2.691965
Fc1ccc(C(OCCN2C3CCC2CC(OC(c2ccc(F)cc2)c2ccc(F)cc2)C3)c2ccc(F)cc2)cc1
-2.93852
CCN1C(=O)C(N2CCN(Cc3ccc(Cl)cc3)CC2)Cc2ccccc21
-0.60206
O=C(CN1CCN(Cc2ccc(Cl)cc2)CC1)N1CCc2ccccc21
-0.20412
O=C(NCCCCN1CCN(c2cccc(Cl)c2Cl)CC1)C1c2ccccc2-c2ccccc21
-2.592177
O=C1c2ccccc2C(=O)N1CCCCCN1CCN(c2cccc(Cl)c2Cl)CC1
-2.021189
Cc1ccc(CN2CCN(C3CCc4cccc5c4N(C3=O)C(C)C5)CC2)cc1
-1
O=C1c2ccccc2C(=O)N1CCCCN1CCN(c2cccc(Cl)c2Cl)CC1
-2.565848
O=C(NCCCN1CCN(c2ccccc2)CC1)C1c2ccccc2-c2ccccc21
-3.33646
c1ccc(CN2CCN(c3cccc4[nH]ccc34)CC2)cc1
-1.167317
c1ccc(C2CCN(Cc3ccn(-c4ccccc4)c3)CC2)cc1
-0.954243
Oc1nc2ccccc2n1C1CCN(Cc2ccn(-c3ccccc3)c2)CC1
-2.810233
C1=C(c2ccccc2)CCN(Cc2ccn(-c3ccccc3)c2)C1
-0.60206
CN1C2CCC1CC(OC1c3ccccc3Cc3ccccc31)C2
-2.733197
Fc1ccc(C(O[C@@H]2C[C@@H]3CC[C@H](C2)N3CCCCc2ccccc2)c2ccc(F)cc2)cc1
-1.833147
O=C(NCCCN1CCN(c2cccc(Cl)c2Cl)CC1)C1c2ccccc2-c2ccccc21
-3.056905
Brc1ccc(N2CCN(Cc3ccccc3)CC2)c2cc[nH]c12
-0.414973
FC(F)(F)c1nc2c(N3CCN(Cc4ccccc4)CC3)cccc2[nH]1
-0.462398
Clc1cccc(C(OC2CC3CCC(C2)N3CCCc2ccccc2)c2ccccc2)c1
-2.346353
COc1ccc(C)cc1CN1CCN(C2CCc3cccc4c3N(C2=O)C(C)(C)C4)CC1
-1.812913
c1ccc(-n2ccc(CN3CCN(c4ccccn4)CC3)c2)cc1
-0.20412
O=C(NCCCCCN1CCN(c2cccc(Cl)c2Cl)CC1)c1cccc2c1-c1ccccc1C2=O
-2.724276
Clc1ccc2[nH]ccc2c1N1CCN(Cc2ccccc2)CC1
-0.230449
O=C1NCN(c2ccccc2)C12CCN(Cc1ccn(-c3ccccc3)c1)CC2
-2.303196
OC1(c2ccc(Cl)cc2)CCN(Cc2ccn(-c3ccccc3)c2)CC1
-2.511883
O=C(NCCCN1CCN(c2ccccc2)CC1)c1cccc2c1Cc1ccccc1-2
-2.898176
Fc1ccc(C(OC2CC3CCC(C2)N3Cc2cccc3ccccc23)c2ccc(F)cc2)cc1
-2.079181
Fc1ccc(C(OC2CC3CCC(C2)N3Cc2cc3ccccc3[nH]2)c2ccc(F)cc2)cc1
-2.235528
Fc1ccc(C(OC2CC3CCC(C2)N3Cc2ccccc2)c2ccc(F)cc2)cc1
-2.80618
COc1cccc(C(=O)CCCN2CCN(c3ccc(Cl)cc3)CC2)c1
-0.60206
COc1cccc(CNCCN2CCN(c3ccc(Cl)cc3)CC2)c1
-1.361728
COc1ccccc1N1CCN(CNC(=O)c2ccc(Cl)cc2)CC1
-1.14814
COc1ccccc1N1CCN(CCCNC(=O)C2CCCc3cccc(OC)c32)CC1
-1.690196
O=C(NCN1CCN(c2ccccc2Cl)CC1)c1ccccc1
-1.507586
COc1cccc(C(=O)CCCN2CCN(c3ccccc3OC)CC2)c1
-0.230449
COc1cccc(C(=O)NCCCCN2CCN(c3ccc(Cl)cc3)CC2)c1
-1.39794
COc1cccc(C(=O)NCCCN2CCN(c3ccc(Cl)cc3)CC2)c1
-0.732394
COc1cccc(C(=O)CCCCN2CCN(c3ccc(Cl)cc3)CC2)c1
-1.322219
COc1ccc2c(c1)C(NCCN1CCN(c3ccc(Cl)cc3)CC1)CCC2
-1.662758
Cc1ccc(C(=O)NCN2CCN(c3ccccc3C#N)CC2)cc1
-0.811474
N#Cc1ccccc1N1CCN(CNC(=O)c2ccc(Cl)cc2)CC1
-0.740165
Cc1cccc(C(=O)NCN2CCN(c3ccccc3C#N)CC2)c1
-0.960851
Cc1cccc(C(=O)NCN2CCN(c3ccccc3Cl)CC2)c1
-1.43072
C1=C(c2ccccc2)CCN(C[C@@H]2CCC=C(c3ccccc3)C2)C1
-1.958564
COc1cccc(NC(=O)CCN2CCN(c3ccc(Cl)cc3)CC2)c1
-1.623249
COc1ccc2c(c1)C(=O)N(CCN1CCN(c3ccc(Cl)cc3)CC1)CC2
-2.149219
COc1cccc(C(=O)CCCCCN2CCN(c3ccc(Cl)cc3)CC2)c1
-2.447158
Oc1ccc(C2=CCN(C[C@@H]3CCC=C(c4ccc(O)cc4)C3)CC2)cc1
-1.832509
COc1cccc2c1CCN(CCN1CCN(c3ccc(Cl)cc3)CC1)C2=O
-1.623249
N#Cc1ccccc1N1CCN(CNC(=O)c2ccccc2)CC1
-1.43072
COc1cccc(C(=O)CCCCN2CCN(c3ccccc3OC)CC2)c1
-1.633468
COc1cc(N)c(Cl)cc1C(=O)NCCN1CCN(c2ccccc2OC)CC1
-1.278754
COc1cccc(C(=O)NCCN2CCN(c3ccccc3OC)CC2)c1
-0.079181
COc1cccc(CCCCN2CCN(c3ccc(Cl)cc3)CC2)c1
-2.30963
Cc1ccc(C(=O)NCN2CCN(c3ccccc3Cl)CC2)cc1
-1.079543
COc1ccccc1N1CCN(CCNC(=O)C2CCCc3c(OC)cccc32)CC1
-1.342423
COc1cccc(C(=O)N(C)CCN2CCN(c3ccc(Cl)cc3)CC2)c1
-2.255273
COc1ccccc1N1CCN(CNC(=O)c2ccc(C)cc2)CC1
-0.888236
COc1ccccc1N1CCN(CCCC2CCCc3c(OC)cccc32)CC1
-1.568202
COc1ccccc1N1CCN(CCC(=O)NC2CCCc3c(OC)cccc32)CC1
-1.826075
COc1cccc(C(=O)NCCCCCN2CCN(c3ccc(Cl)cc3)CC2)c1
-2.382017
COc1cccc2c1CCCC2CCCN1CCN(c2ccc(Cl)cc2)CC1
-2.619093
COc1cccc(C(=O)NCCN2CCN(c3ccc(Cl)cc3)CC2)c1
1.39794
COc1ccccc1N1CCN(CNC(=O)c2cccc(C)c2)CC1
-0.828047
N#Cc1ccccc1N1CCN(CNC(=O)c2cccc(Cl)c2)CC1
-1.530712
O=C(NCN1CCN(c2ccccc2Cl)CC1)c1ccc(Cl)cc1
-0.72214
COc1ccccc1N1CCN(CCCCC2CCCc3c(OC)cccc32)CC1
-1.633468
COc1ccccc1N1CCN(CNC(=O)c2cccc(Cl)c2)CC1
-1.180126
O=C(NCN1CCN(c2ccccc2Cl)CC1)c1cccc(Cl)c1
-1.611405
COc1ccccc1N1CCN(Cc2cccc(-c3ccccc3)c2)CC1
-0.90309
Oc1ccc(C2=CCC[C@@H](CN3CC=C(c4ccccc4)CC3)C2)cc1
-1.788168
COc1ccc2c(c1)C(CCCN1CCN(c3ccc(Cl)cc3)CC1)CCC2
-2.592177
COc1ccccc1N1CCN(CNC(=O)c2ccccc2)CC1
-1.341237
Clc1ccc(N2CCN(Cc3cccn4nccc34)CC2)cc1
-1.80618
Clc1ccc(N2CCN(Cc3cnn4ccccc34)CC2)cc1
-0.483098
Clc1ccc(N2CCN(Cc3cccc4ccnn34)CC2)cc1
-3.113943
Clc1ccc(N2CCN(Cc3cc4ccccn4n3)CC2)cc1
-0.379901
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MoleculeACE ChEMBL219 Ki

ChEMBL219 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 D(4) dopamine receptor target.

Characteristic Description
Tasks 1
Task type regression
Total samples 1865
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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