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SMILES
stringlengths
26
103
Ki
float64
-3.66
1
C[C@@H]1CCN(C(=O)CC#N)C[C@@H]1N(C)c1ncnc2[nH]ccc12
0.154902
C[C@@H]1CCN(C(=O)CC#N)C[C@@H]1n1cnc2cnc3[nH]ccc3c21
0.30103
C[C@@H]1CCN(Cc2ccccc2)C[C@@H]1N(C)c1ncnc2[nH]ccc12
-2.78533
C[C@@H]1CCN(Cc2ccccc2)C[C@@H]1n1cnc2cnc3[nH]ccc3c21
-1.079181
N#CCC(=O)N1CCC[C@@H](n2cnc3cnc4[nH]ccc4c32)C1
0.39794
c1ccc(CN2CC[C@@H](n3cnc4cnc5[nH]ccc5c43)C2)cc1
-1.176091
c1ccc(CN2CC(n3cnc4cnc5[nH]ccc5c43)C2)cc1
-1.792392
c1ccc(CN2CCC(n3cnc4cnc5[nH]ccc5c43)CC2)cc1
-0.380211
c1cc2c(ncc3ncn(C4CCNCC4)c32)[nH]1
-1.658011
c1ncc(CN2CCC(n3cnc4cnc5[nH]ccc5c43)CC2)cn1
-0.30103
N#CCC(=O)N1CCC(n2cnc3cnc4[nH]ccc4c32)CC1
-1.278754
O=C(c1ccccc1)N1CCC(n2cnc3cnc4[nH]ccc4c32)CC1
-1.255273
CC(C)CC(=O)N1CCC(n2cnc3cnc4[nH]ccc4c32)CC1
-1.447158
O=S(=O)(c1ccccc1)N1CCC(n2cnc3cnc4[nH]ccc4c32)CC1
-0.982271
CS(=O)(=O)N1CCC(n2cnc3cnc4[nH]ccc4c32)CC1
-0.079181
CCN1CCC(n2cnc3cnc4[nH]ccc4c32)CC1
-1.69897
FC(F)(F)CN1CCC(n2cnc3cnc4[nH]ccc4c32)CC1
-0.462398
N#CCCN1CCC(n2cnc3cnc4[nH]ccc4c32)CC1
0.221849
CN(C)CC(=O)N1CCC(n2cnc3cnc4[nH]ccc4c32)CC1
-1.50515
CC(C)N1CCC[C@H]1C(=O)N1CCC(n2cnc3cnc4[nH]ccc4c32)CC1
-1.342423
CN1CCCC[C@H]1C(=O)N1CCC(n2cnc3cnc4[nH]ccc4c32)CC1
-1.041393
CC1(n2cnc3cnc4[nH]ccc4c32)CCNCC1
-1.982271
CC1(n2cnc3cnc4[nH]ccc4c32)CCN(Cc2cncnc2)CC1
-0.919078
CC1(n2cnc3cnc4[nH]ccc4c32)CCN(S(C)(=O)=O)CC1
-0.230449
CC1(n2cnc3cnc4[nH]ccc4c32)CCN(CC(F)(F)F)CC1
-1.518514
CC1(n2cnc3cnc4[nH]ccc4c32)CCN(CCC#N)CC1
-1.69897
CC(C)C(=O)N[C@H]1CC[C@H](n2cnc3cnc4[nH]ccc4c32)CC1
-1.041393
c1cc2c(ncc3ncn([C@H]4CC[C@H](NCC5CC5)CC4)c32)[nH]1
-0.968483
N#CCCN[C@H]1CC[C@H](n2cnc3cnc4[nH]ccc4c32)CC1
-0.653213
N#Cc1cccc(CN[C@H]2CC[C@H](n3cnc4cnc5[nH]ccc5c43)CC2)c1
-0.770852
CS(=O)(=O)N[C@H]1CC[C@H](n2cnc3cnc4[nH]ccc4c32)CC1
-0.431364
COC(=O)N[C@H]1CC[C@H](n2cnc3cnc4[nH]ccc4c32)CC1
-0.977724
COC(=O)N(C)[C@H]1CC[C@H](n2cnc3cnc4[nH]ccc4c32)CC1
-1.113943
COC(=O)N[C@@H]1CC[C@@H](n2cnc3cnc4[nH]ccc4c32)C1
-0.50515
Cc1nc2cnc3[nH]ccc3c2n1[C@@H]1CCCC[C@H]1O
-1.954243
COC(=O)N[C@H]1CC[C@H](n2c(C)nc3cnc4[nH]ccc4c32)CC1
-0.414973
CC(=O)N[C@H]1CC[C@H](n2c(C)nc3cnc4[nH]ccc4c32)CC1
-0.78533
Cc1nc2cnc3[nH]ccc3c2n1[C@H]1CC[C@H](NS(C)(=O)=O)CC1
-0.653213
Cc1nc2cnc3[nH]ccc3c2n1[C@H]1CC[C@H](NCCC#N)CC1
-0.176091
Cc1nc2cnc3[nH]ccc3c2n1C1CCNCC1(F)F
-0.30103
Cc1nc2cnc3[nH]ccc3c2n1[C@H]1CCCNC1
-2.176091
Cc1nc2cnc3[nH]ccc3c2n1[C@H]1CCCOC1
-0.447158
Cc1nc2cnc3[nH]ccc3c2n1C1CCOCC1
-1.255273
Cc1nc2cnc3[nH]ccc3c2n1C1CCCCC1
-0.113943
Cc1nc2cnc3[nH]ccc3c2n1C1CCN(CCC#N)CC1
0.045757
Cc1nc2cnc3[nH]ccc3c2n1C1CCN(Cc2ccccc2)CC1
-0.113943
Cc1nc2cnc3[nH]ccc3c2n1C1CCNCC1
-1
N#CC[C@H](C1CCCC1)n1cc(-c2ncnc3[nH]ccc23)cn1
0.69897
CS(=O)(=O)NCCc1nc2cnc3[nH]ccc3c2n1C1CC2CCC1C2
0.221849
CC(=O)NCc1nc2cnc3[nH]ccc3c2n1C1CC2CCC1C2
-0.477121
CS(=O)(=O)NCc1nc2cnc3[nH]ccc3c2n1C1CCCCCC1
-0.518514
CS(=O)(=O)NCc1nc2cnc3[nH]ccc3c2n1C1CCCC1
-0.724276
CS(=O)(=O)NCCc1nc2cnc3[nH]ccc3c2n1C1CCCCC1
-0.322219
CS(=O)(=O)NCc1nc2cnc3[nH]ccc3c2n1C1CCCCC1
-0.414973
CC(=O)NCCc1nc2cnc3[nH]ccc3c2n1C1CCCCC1
-0.414973
CC(=O)NCc1nc2cnc3[nH]ccc3c2n1C1CCCCC1
-1
CS(=O)(=O)NCc1nc2cnc3[nH]ccc3c2n1C1CC2CCC1O2
-1.447158
CS(=O)(=O)NCc1nc2cnc3[nH]ccc3c2n1C1CCCSC1
-1.002166
CS(=O)(=O)NCc1nc2cnc3[nH]ccc3c2n1[C@@H]1CCC[C@@H](O)C1
-0.623249
CS(=O)(=O)NCc1nc2cnc3[nH]ccc3c2n1C1CCN(CC(F)(F)F)CC1
-0.69897
CC(=O)NCc1nc2cnc3[nH]ccc3c2n1C1CCN(CC(F)(F)F)CC1
-1.079181
CS(=O)(=O)NCCc1nc2cnc3[nH]ccc3c2n1C1CCN(CCC#N)CC1
-0.230449
CC(=O)NCCc1nc2cnc3[nH]ccc3c2n1C1CCN(CCC#N)CC1
-0.568202
CC(C)CS(=O)(=O)NCc1nc2cnc3[nH]ccc3c2n1C1CCN(CCC#N)CC1
-0.732394
CS(=O)(=O)NCc1nc2cnc3[nH]ccc3c2n1C1CCN(CCC#N)CC1
-0.863323
CCOC(=O)NCc1nc2cnc3[nH]ccc3c2n1C1CCN(CCC#N)CC1
-0.924279
CC(=O)NCc1nc2cnc3[nH]ccc3c2n1C1CCN(CCC#N)CC1
-1.20412
Cn1ccc(CCc2nc3cnc4[nH]ccc4c3n2C2CCN(CCC#N)CC2)n1
-1.113943
N#CCCN1CCC(n2c(CCn3cccn3)nc3cnc4[nH]ccc4c32)CC1
-1.146128
Cn1ccnc1CCc1nc2cnc3[nH]ccc3c2n1C1CCN(CCC#N)CC1
-0.968483
N#CCCN1CCC(n2c(CC3CCOCC3)nc3cnc4[nH]ccc4c32)CC1
-1.633468
N#CCCN1CCC(n2c(CCC3CCCO3)nc3cnc4[nH]ccc4c32)CC1
-0.939519
N#CCCN1CCC(n2c(CC3CCCO3)nc3cnc4[nH]ccc4c32)CC1
-1.380211
N#CCCN1CCC(n2c(CC3CCCC3)nc3cnc4[nH]ccc4c32)CC1
-1.20412
N#CCCN1CCC(n2c(C3CCC3)nc3cnc4[nH]ccc4c32)CC1
-0.633468
CC(C)c1nc2cnc3[nH]ccc3c2n1C1CCN(CCC#N)CC1
-0.959041
N#CCCN1CCC(n2c(C3CC3)nc3cnc4[nH]ccc4c32)CC1
-0.462398
CCCc1nc2cnc3[nH]ccc3c2n1C1CCN(CCC#N)CC1
-0.662758
O=C(Nc1ccnc(NC(=O)C2CC2)c1)c1c(Cl)cccc1Cl
-1.923762
C[C@@H](O)c1nc2cnc3[nH]ccc3c2n1[C@H]1CC[C@H](CCC#N)CC1
0.522879
C[C@@H](O)c1nc2cnc3[nH]ccc3c2n1[C@H]1CC[C@H](CC#N)CC1
1
C[C@@H](O)c1nc2cnc3[nH]ccc3c2n1[C@H]1CC[C@H](NCC(F)F)CC1
-0.041393
CO[C@H]1CC[C@H](n2c([C@@H](C)O)nc3cnc4[nH]ccc4c32)CC1
-0.690196
C[C@@H](O)c1nc2cnc3[nH]ccc3c2n1[C@@H]1CCC[C@@H](O)C1
-0.431364
C[C@@H](O)c1nc2cnc3[nH]ccc3c2n1C1CCOCC1
-1.491362
C[C@@H](O)c1nc2cnc3[nH]ccc3c2n1[C@@H]1CCCN(C(=O)CC(F)(F)F)C1
-1.633468
C[C@@H](O)c1nc2cnc3[nH]ccc3c2n1[C@@H]1CCCN(C(=O)CC#N)C1
-0.716003
CCN1CCC(n2c([C@@H](C)O)nc3cnc4[nH]ccc4c32)C(F)(F)C1
-0.30103
C[C@@H](O)c1nc2cnc3[nH]ccc3c2n1C1CCN(CCC#N)CC1
-0.531479
C[C@@H](O)c1nc2cnc3[nH]ccc3c2n1C1CCN(CC(F)(F)F)CC1
-0.414973
CC(C)(O)c1nc2cnc3[nH]ccc3c2n1C1CCCCC1
-1.113943
OCCc1nc2cnc3[nH]ccc3c2n1C1CCCCC1
-0.544068
OCc1nc2cnc3[nH]ccc3c2n1C1CCCCC1
-0.041393
c1ccc(CN2CCC[C@@H](n3nnc4cnc5[nH]ccc5c43)C2)cc1
-1.39794
c1cc2c(ncc3nnn(CC4CCNCC4)c32)[nH]1
-2.872156
c1cc2c(ncc3nnn(C4CCNCC4)c32)[nH]1
-2.322219
CC1(n2nnc3cnc4[nH]ccc4c32)CCN(CCC#N)CC1
-2.193125
CN1CCC[C@H]1C(=O)N1CCC(n2nnc3cnc4[nH]ccc4c32)CC1
-2.082785
CC(C)(C)OC(=O)N[C@H]1CC[C@H](n2nnc3cnc4[nH]ccc4c32)CC1
-2.079181
N[C@H]1CC[C@H](n2nnc3cnc4[nH]ccc4c32)CC1
-1.322219
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MoleculeACE ChEMBL2835 Ki

ChEMBL2835 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 Tyrosine-protein kinase jak1 target.

Characteristic Description
Tasks 1
Task type regression
Total samples 615
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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