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SMILES
stringlengths
14
100
Ki
float64
-5
2
FC(F)(F)c1cccc(-c2nnc3ccc(NC4CCCCC4)cn23)c1
-1.041393
Cc1ccc2[nH]c(=O)c(CC(=O)O)c(-c3ccccc3)c2c1
-3.653213
O=C(O)c1cccc(Nc2nc(-c3ccc(O)cc3O)cs2)c1
-3.531479
O=C(O)c1cccc2c(-c3ccccc3)c(-c3ccccc3)[nH]c12
-2.740363
CCc1ccc(C2C(C(C)=O)=C(O)C(=O)N2CCc2c[nH]c3ccccc23)cc1
-3.322219
CC(=O)C1=C(O)C(=O)N(CCc2c[nH]c3ccccc23)C1c1ccc(O)cc1
-3.342423
CCOc1ccc(C2C(C(C)=O)=C(O)C(=O)N2CCc2c[nH]c3ccccc23)cc1
-3.623249
COc1cc(C2C(C(C)=O)=C(O)C(=O)N2CCc2c[nH]c3ccccc23)ccc1O
-3.653213
CCn1c2cc(Cl)c(F)cc2c(=O)c2c(=O)o[nH]c21
-1.340444
CC(C)n1c2cc(Cl)c(F)cc2c(=O)c2c(=O)o[nH]c21
-1.089905
CCCCn1c2cc(Cl)c(F)cc2c(=O)c2c(=O)o[nH]c21
-1.176091
O=c1o[nH]c2c1c(=O)c1cc(F)c(Cl)cc1n2Cc1ccccc1
-1.562293
CCn1c2cc(Cl)c(F)cc2c(=O)c2c(=O)on(Cc3ccccc3)c21
-3.104146
CCn1c2cc(Cl)c(F)cc2c(=O)c2c(=O)on(Cc3cccc(O)c3)c21
-2.432969
CCn1c2cc(Cl)c(F)cc2c(=O)c2c(=O)on(CCOCc3ccccc3)c21
-3.689664
CCn1c2cc(Cl)c(F)cc2c(=O)c2c(=O)on(CCO)c21
-3.065206
CCn1c2cc(Cl)c(OC)cc2c(=O)c2c(=O)o[nH]c21
-3.431042
CCn1c2cc(Cl)c(O)cc2c(=O)c2c(=O)o[nH]c21
-0.643946
CN1CCC(N(C)c2ccc3nnc(-c4cccc(C(F)(F)F)c4)n3n2)CC1
-2
CN(c1ccc2nnc(-c3cccc(C(F)(F)F)c3)n2n1)C1CCCCC1
-1.690196
CN1CCC(Nc2ccc3nnc(-c4cccc(C(F)(F)F)c4)n3n2)CC1
-2.20412
FC(F)(F)c1cccc(-c2nnc3ccc(NC4CCNCC4)nn23)c1
-1.322219
FC(F)(F)c1cccc(-c2nnc3ccc(NC4CCCNC4)nn23)c1
-1.973128
FC(F)(F)c1cccc(-c2nnc3ccc(NC4CCOCC4)nn23)c1
-1.643453
FC(F)(F)c1cccc(-c2nnc3ccc(NC4CCCCC4)nn23)c1
-1.041393
FC(F)(F)c1cccc(-c2nnc3ccc(NC4CCC4)nn23)c1
-1.732394
FC(F)(F)c1cccc(-c2nnc3ccc(NCC4CC4)nn23)c1
-1.69897
COc1ccc(-c2nnc3ccc(NC4CC4)nn23)cc1
-2.991226
COc1cccc(-c2nnc3ccc(NC4CC4)nn23)c1
-2.612784
Fc1ccc(-c2nnc3ccc(NC4CC4)nn23)cc1
-2.322219
Fc1cccc(-c2nnc3ccc(NC4CC4)nn23)c1
-2.633468
FC(F)(F)c1cccc(-c2nnc3ccc(NC4CC4)nn23)c1
-1.255273
c1ccc(-c2nnc3ccc(NC4CC4)nn23)cc1
-3.255273
Cc1nc2c(sc3ccc(Cl)cc32)c(=O)[nH]1
-2.553883
CN(C)Cc1nc2c(sc3ccc(Cl)cc32)c(=O)[nH]1
-1.146128
O=c1[nH]c(CN2CCCCC2)nc2c1sc1ccc(Cl)cc12
-1.079181
O=c1[nH]c(CN2CCCNC2)nc2c1sc1ccc(Cl)cc12
-1.041393
O=c1[nH]c(CN2CCOCC2)nc2c1sc1ccc(Cl)cc12
-2.521138
O=c1[nH]c(CN2CCCC(O)C2)nc2c1sc1ccc(Cl)cc12
-1.518514
O=c1[nH]c(CN2CCC(O)CC2)nc2c1sc1ccc(Cl)cc12
-1.623249
CNCc1nc2c(sc3ccc(Cl)cc32)c(=O)[nH]1
-1.851258
CN(C)CCc1nc2c(sc3ccc(Cl)cc32)c(=O)[nH]1
-1.230449
CN(C)CCCc1nc2c(sc3ccc(Cl)cc32)c(=O)[nH]1
-1.041393
O=c1[nH]c(CNc2ccccc2)nc2c1sc1ccc(Cl)cc12
-1.643453
O=c1[nH]c(CNc2ccccc2O)nc2c1sc1ccc(Cl)cc12
-1.963788
O=c1[nH]c(CNc2cccc(O)c2)nc2c1sc1ccc(Cl)cc12
0.045757
O=c1[nH]c(CNc2ccc(O)cc2)nc2c1sc1ccc(Cl)cc12
-2.029384
COc1cccc(NCc2nc3c(sc4ccc(Cl)cc43)c(=O)[nH]2)c1
-1.963788
O=c1[nH]c(CNc2cc(O)cc(O)c2)nc2c1sc1ccc(Cl)cc12
-1.819544
Cc1c(O)cccc1NCc1nc2c(sc3ccc(Cl)cc32)c(=O)[nH]1
-1.579784
Cc1ccc(NCc2nc3c(sc4ccc(Cl)cc43)c(=O)[nH]2)cc1O
-1.255273
O=c1[nH]c(CNCCc2cccc(O)c2)nc2c1sc1ccc(Cl)cc12
-1.690196
O=c1[nH]c(COc2cccc(O)c2)nc2c1sc1ccc(Cl)cc12
-1.20412
CN(C)Cc1nc2c(sc3ccc(Br)cc32)c(=O)[nH]1
-0.69897
CN(C)Cc1nc2c(sc3ccccc32)c(=O)[nH]1
-2.471292
Cc1ccc2sc3c(=O)[nH]c(CN(C)C)nc3c2c1
-1.50515
CN(C)Cc1nc2c(sc3ccc(C(F)(F)F)cc32)c(=O)[nH]1
-1.041393
CCc1ccc2sc3c(=O)[nH]c(CN(C)C)nc3c2c1
-1.556303
CC(C)c1ccc2sc3c(=O)[nH]c(CN(C)C)nc3c2c1
-1.30103
C=Cc1ccc2sc3c(=O)[nH]c(CN(C)C)nc3c2c1
-0.954243
CN(C)Cc1nc2c(sc3ccc(/C=C/C4CC4)cc32)c(=O)[nH]1
-0
C#Cc1ccc2sc3c(=O)[nH]c(CN(C)C)nc3c2c1
-0.69897
CN(C)CC#Cc1ccc2sc3c(=O)[nH]c(CN(C)C)nc3c2c1
-1.60206
CN(C)Cc1nc2c(sc3ccc(C#CCCCCN4CCCCC4)cc32)c(=O)[nH]1
-0.477121
CN(C)Cc1nc2c(sc3ccc(-c4ccccc4)cc32)c(=O)[nH]1
-0.60206
CC(c1ccccc1)c1ccc2sc3c(=O)[nH]c(CN(C)C)nc3c2c1
-1.812913
CN(C)Cc1nc2c(sc3ccc(-c4cccc(F)c4)cc32)c(=O)[nH]1
-0.778151
CN(C)Cc1nc2c(sc3ccc(-c4cccc(Cl)c4)cc32)c(=O)[nH]1
-0.477121
CN(C)Cc1nc2c(sc3ccc(-c4cccc(OC(F)(F)F)c4)cc32)c(=O)[nH]1
-0.69897
CN(C)Cc1nc2c(sc3ccc(-c4cccc(O)c4)cc32)c(=O)[nH]1
-0
Cc1ccccc1-c1ccc2sc3c(=O)[nH]c(CN(C)C)nc3c2c1
-1.146128
CCc1ccc(-c2ccc3sc4c(=O)[nH]c(CN(C)C)nc4c3c2)cc1
-0.69897
CN(C)Cc1nc2c(sc3ccc(-c4ccc(C#N)cc4)cc32)c(=O)[nH]1
-0.69897
CN(C)Cc1nc2c(sc3ccc(-c4ccc(O)cc4)cc32)c(=O)[nH]1
-0.30103
CN(C)Cc1nc2c(sc3ccc(-c4ccc(N)cc4)cc32)c(=O)[nH]1
-0.30103
CN(C)Cc1nc2c(sc3ccc(-c4ccc(OC(F)(F)F)cc4)cc32)c(=O)[nH]1
-1.079181
CN(C)Cc1nc2c(sc3ccc(-c4ccc(O)c(F)c4)cc32)c(=O)[nH]1
-0
CN(C)Cc1nc2c(sc3ccc(-c4ccc(N(C)C)cc4)cc32)c(=O)[nH]1
-0.60206
CN(C)Cc1nc2c(sc3ccc(-c4ccc(Cl)c(F)c4)cc32)c(=O)[nH]1
-0.90309
CN(C)Cc1nc2c(sc3ccc(-c4cc(F)cc(Cl)c4)cc32)c(=O)[nH]1
-0.477121
CN(C)Cc1nc2c(sc3ccc(-c4cc(Cl)cc(Cl)c4)cc32)c(=O)[nH]1
-0.60206
CN(C)Cc1nc2c(sc3ccc(-c4ccsc4)cc32)c(=O)[nH]1
-0.60206
CN(C)Cc1nc2c(sc3ccc(-c4ccc[nH]4)cc32)c(=O)[nH]1
-0.90309
CN(C)Cc1nc2c(sc3ccc(-c4ccoc4)cc32)c(=O)[nH]1
-0.90309
Cc1cscc1-c1ccc2sc3c(=O)[nH]c(CN(C)C)nc3c2c1
-1.041393
CN(C)Cc1nc2c(sc3ccc(-c4cccnc4)cc32)c(=O)[nH]1
-0.30103
CN(C)Cc1nc2c(sc3ccc(-c4cncnc4)cc32)c(=O)[nH]1
-1.462398
CN(C)Cc1nc2c(sc3ccc(N4CCCC4)cc32)c(=O)[nH]1
-0.60206
CN(C)Cc1nc2c(sc3ccc(N4CCCCC4)cc32)c(=O)[nH]1
-2.432969
CN(C)Cc1nc2c(sc3ccc(-c4ccc(OCC(O)CO)cc4)cc32)c(=O)[nH]1
-0
CN(C)Cc1nc2c(sc3ccc(-c4ccc(OCCCN)cc4)cc32)c(=O)[nH]1
0.221849
O=c1[nH]c(CNc2cccc(O)c2)nc2c1sc1ccc(Br)cc12
0.09691
O=c1[nH]c(CNc2cccc(O)c2)nc2c1sc1ccc(-c3ccsc3)cc12
-0.30103
O=c1[nH]c(CNc2cccc(O)c2)nc2c1sc1ccc(-c3ccccc3)cc12
-0.30103
O=c1[nH]c(CN2CC[C@H](O)C2)nc2c1sc1ccc(-c3ccsc3)cc12
-0.69897
O=c1[nH]c(CN2CC[C@H](O)C2)nc2c1sc1ccc(-c3ccccc3)cc12
-0.477121
O=c1[nH]c(CN2CC[C@H](O)C2)nc2c1sc1ccc(-c3ccc(O)cc3)cc12
0.154902
N#Cc1ccc(-c2ccc3sc4c(=O)[nH]c(CN5CC[C@H](O)C5)nc4c3c2)cc1
-0.60206
O=c1[nH]c(CN2CC[C@H](O)C2)nc2c1sc1ccc(/C=C/C3CC3)cc12
0.09691
O=c1[nH]cnc2c1sc1c(Cl)ccc(Cl)c12
-2.107193
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MoleculeACE ChEMBL2147 Ki

ChEMBL2147 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 Serine/threonine-protein kinase pim-1 target.

Characteristic Description
Tasks 1
Task type regression
Total samples 1456
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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