SMILES
stringlengths 11
267
| label
float64 -1.5
4.5
|
---|---|
Cn1c(CN2CCN(CC2)c3ccc(Cl)cc3)nc4ccccc14 | 3.54 |
COc1cc(OC)c(cc1NC(=O)CSCC(=O)O)S(=O)(=O)N2C(C)CCc3ccccc23 | -1.18 |
COC(=O)[C@@H](N1CCc2sccc2C1)c3ccccc3Cl | 3.69 |
OC[C@H](O)CN1C(=O)C(Cc2ccccc12)NC(=O)c3cc4cc(Cl)sc4[nH]3 | 3.37 |
Cc1cccc(C[C@H](NC(=O)c2cc(nn2C)C(C)(C)C)C(=O)NCC#N)c1 | 3.1 |
OC1(CN2CCC1CC2)C#Cc3ccc(cc3)c4ccccc4 | 3.14 |
COc1cc(OC)c(cc1NC(=O)CCC(=O)O)S(=O)(=O)NCc2ccccc2N3CCCCC3 | -0.72 |
CNc1cccc(CCOc2ccc(C[C@H](NC(=O)c3c(Cl)cccc3Cl)C(=O)O)cc2C)n1 | 0.34 |
COc1ccc(cc1)C2=COc3cc(OC)cc(OC)c3C2=O | 3.05 |
Oc1ncnc2scc(c3ccsc3)c12 | 2.25 |
CS(=O)(=O)c1ccc(Oc2ccc(cc2)C#C[C@]3(O)CN4CCC3CC4)cc1 | 1.51 |
C[C@H](Nc1nc(Nc2cc(C)[nH]n2)c(C)nc1C#N)c3ccc(F)cn3 | 2.61 |
O=C1CCCCCN1 | -0.08 |
CCCSc1ncccc1C(=O)N2CCCC2c3ccncc3 | 1.95 |
CC1CCCCC1NC(=O)c2cnn(c2NS(=O)(=O)c3ccc(C)cc3)c4ccccc4 | 1.34 |
Nc1ccc(cc1)c2nc3ccc(O)cc3s2 | 3.2 |
COc1ccc(cc1)N2CCN(CC2)C(=O)[C@@H]3CCCC[C@H]3C(=O)NCC#N | 1.6 |
CCC(COC(=O)c1cc(OC)c(OC)c(OC)c1)(N(C)C)c2ccccc2 | 3.77 |
COc1cc(ccc1N2CC[C@@H](O)C2)N3N=Nc4cc(sc4C3=O)c5ccc(Cl)cc5 | 3.15 |
CO[C@H]1CN(CCN2C(=O)C=Cc3ccc(cc23)C#N)CC[C@H]1NCc4ccc5OCC(=O)Nc5n4 | 0.32 |
CC(C)(CCCCCOCCc1ccccc1)NCCc2ccc(O)c3nc(O)sc23 | 2.92 |
Clc1ccc(cc1)C(=O)Nc2oc(nn2)C(=O)Nc3ccc(cc3)N4CCOCC4 | 1.92 |
COc1ccc(Oc2cccc(CN3CCCC(C3)N4C=C(C)C(=O)NC4=O)c2)cc1 | 3.17 |
OC(=O)c1cccc(c1)N2CCC(CN3CCC(CC3)Oc4ccc(Cl)c(Cl)c4)CC2 | 2.17 |
CNCC[C@@H](Oc1ccccc1C)c2ccccc2 | 1.2 |
Clc1ccc(N2CCN(CC2)C(=O)CCCc3ccncc3)c(Cl)c1 | 3.93 |
COc1cnc(nc1N(C)C)c2ccccn2 | 1.9 |
C(CCCCNc1cc(nc2ccccc12)c3ccccc3)CCCNc4cc(nc5ccccc45)c6ccccc6 | 2.27 |
CSc1c(cnn1c2ccc(cc2)C(=O)O)C(=O)NC3C4CC5CC(CC3C5)C4 | 1.2 |
CNC1=Nc2ncccc2C(=NC1c3cccs3)c4occn4 | 1.14 |
CS(=O)(=O)C1(CC1)c2cc(nc(n2)c3cccc4[nH]ccc34)N5CC6CCC(C5)O6 | 2.6 |
CN([C@@H]1CCN(Cc2ccc(cc2)C(F)(F)F)C[C@@H]1F)C(=O)Cc3ccc(cc3)n4cnnn4 | 3.3 |
CC(=O)[C@H]1CC[C@H]2[C@@H]3CCC4=CC(=O)CC[C@]4(C)[C@H]3CC[C@]12C | 3.94 |
CS(=O)(=O)c1ccccc1C(=O)NC[C@@H](O)CN2CCC(CC2)Oc3ccc(Cl)c(Cl)c3 | 2.34 |
O=C(NCc1ccncc1)c2ccc(Oc3ccccc3C#N)cc2 | 2.57 |
CN(C)c1ccnc2sc(C(=O)NCc3ccccc3)c(N)c12 | 3.62 |
CN1CCN(CC1)c2ccc3N=CN(C(=O)c3c2)c4cc(NC(=O)c5cscn5)ccc4C | 2.06 |
Cn1cncc1c2c3C(=O)N(CC4CC4)C(=O)N(CC5CC5)c3nn2Cc6ccnc7ccc(Cl)cc67 | 4.33 |
COc1ccc2ncc(C#N)c(CCN3CCC(CC3)NCc4cc5SCOc5cn4)c2c1 | 2.55 |
CNC(=O)C1(CCN(CC[C@H](CN(C)C(=O)c2c(OC)c(cc3ccccc23)C#N)c4ccc(Cl)c(Cl)c4)CC1)N5CCCCC5=O | 2.78 |
OB1N(C(=O)Nc2ccccc12)c3ccccc3 | 1.4 |
CC(C)N(CCC(C(=O)N)(c1ccccc1)c2ccccn2)C(C)C | -0.54 |
NC(=NC#N)c1sc(Nc2ccccc2)nc1N | 2.91 |
CCS(=O)(=O)c1ccc(c(C)c1)c2cc(ccc2O[C@H](C)C(=O)O)C(F)(F)F | -0.4 |
OC(=O)COc1ccc(cc1c2cc(ccc2F)C#N)C(F)(F)F | -0.16 |
COc1ccc(cn1)C2=Cc3c(C)nc(N)nc3N([C@@H]4CC[C@H](CC4)OCCO)C2=O | 2.2 |
CC(Nc1ncnc2ccccc12)c3ccccc3 | 3.4 |
CC(C)c1ccc2Oc3nc(N)c(cc3C(=O)c2c1)C(=O)O | 1.1 |
O[C@@H](CNCCCOCCOCCc1cccc2ccccc12)c3ccc(O)c4NC(=O)Sc34 | 2.28 |
COc1ccccc1Cn2c(C)nc3ccccc23 | 3.47 |
OC(=O)c1ccc(NC(=O)c2cc(OCc3ccccc3F)cc(OCc4ccccc4F)c2)nc1 | 3 |
NC(Cc1c[nH]c2ccccc12)C(=O)O | -1.17 |
OC(=O)CCC[C@H]1[C@@H](Cc2ccccc12)NC(=O)c3cc4cc(F)ccc4[nH]3 | 1.95 |
CCNC(=O)c1cc2c(c(cnc2[nH]1)c3cncc(c3)C(=O)O)n4ccc(n4)C(F)(F)F | -0.99 |
C[C@H](NC(=O)c1c(C)nn(C2CCCC2)c1NS(=O)(=O)c3ccc(C)cc3)C(C)(C)C | 2 |
N(c1ccccc1)c2cc(Nc3ccccc3)[nH]n2 | 3.8 |
COCCNC(=O)c1cccc(Nc2ncc3cc(ccc3n2)c4ccncc4)c1 | 3.21 |
CCC(CC)NC(=O)c1cnn(C)c1NS(=O)(=O)c2ccc(C)cc2 | 0.36 |
NC(=O)c1cc(F)cc(O[C@H]2C[C@H]3CC[C@@H](C2)N3Cc4ccccc4)c1 | 2.14 |
O=C1NC(=NC(=C1C#N)c2ccccc2)SCCc3ccccc3 | 1.71 |
OC(C(=O)OC1CN2CCC1CC2)(c3ccccc3)c4ccccc4 | 1.19 |
Cc1ccccc1NC(=O)CCS(=O)(=O)c2ccc(Br)s2 | 2.7 |
CC(C)n1c(C)ncc1c2nc(Nc3ccc(cc3)C(=O)N(C)C)ncc2F | 2.77 |
COc1cccc(c1)c2c[nH]c(n2)c3ccccc3 | 3.8 |
O=C(COc1ccccc1)c2ccccc2 | 2.87 |
COc1cc2ncc(C(=O)N)c(Nc3ccc(F)cc3F)c2cc1NCCN(C)C | 1.91 |
CO[C@@H]1CC[C@@]2(CC1)Cc3ccc(OCC(C)C)cc3C24N=C(C)C(=N4)N | 3.4 |
COc1cc2ncnc(Nc3ccc(F)c(Cl)c3)c2cc1OCCCN4CCCC4 | 3.13 |
O=C1CCOc2cc(COc3ccccc3)ccc12 | 3 |
Clc1cccc2cn[nH]c12 | 2.33 |
CNC(=O)c1ccc(CC(=O)N(C)C2CCN(Cc3ccc(cc3)C(F)(F)F)CC2)cc1 | 2.8 |
COCCNCc1ccc(CCNC[C@H](O)c2ccc(O)c3NC(=O)Sc23)cc1 | -0.54 |
Cn1cncc1c2c3C(=O)N(CC#C)C(=O)N(CC4CC4)c3nn2Cc5ccnc6ccc(Cl)cc56 | 3.16 |
C[C@H](NC(=O)c1cccnc1Oc2ccccc2)c3ccccc3 | 2.91 |
Clc1ccc(CN2CC3CNCC(C2)O3)cc1C(=O)NCC45CC6CC(CC(C6)C4)C5 | 1.55 |
COc1cc(NS(=O)(=O)c2ccc(N)cc2)nc(OC)n1 | 0.2 |
Cc1cc(CCC2CCN(CC2)S(=O)(=O)CC3(CCOCC3)N(O)C=O)c(C)cn1 | 1.43 |
C[C@H](Nc1ncc(F)c(Nc2cc([nH]n2)C3CC3)n1)c4ncc(F)cn4 | 2.47 |
CC(=O)Nc1ccc2c(c1)c(cn2CCCO)c3cc(NC4CC4)n5ncc(C#N)c5n3 | 2.48 |
CC1COc2c(N3CCN(C)CC3)c(F)cc4C(=O)C(=CN1c24)C(=O)O | -0.45 |
CC1(CC1)c2nc(ncc2C(=O)N[C@@H]3C4CC5CC3C[C@@](O)(C5)C4)N6CCOCC6 | 2 |
COC(=O)c1ccc(C)c(NS(=O)(=O)c2ccc3N(C)SC(=O)c3c2)c1 | 2.6 |
COc1ccc(cc1)C2=COc3cc(O)cc(O)c3C2=O | 3.5 |
CNCCCC12CCC(c3ccccc13)c4ccccc24 | 0.89 |
Oc1cc(nc2ccnn12)c3ccccc3 | 1.3 |
Fc1cc(cc(F)c1C2=CCN(CC2)C=O)N3C[C@H](COc4ccon4)OC3=O | 2.01 |
CC(C#C)N1C(=O)N(CC2CC2)c3nn(Cc4ccnc5ccc(Cl)cc45)c(c3C1=O)c6cncn6C | 3.59 |
C[C@H]1CN(Cc2cc(Cl)ccc2OCC(=O)O)CCN1C(=O)Cc3ccccc3 | 0.18 |
COc1cc(Nc2nc(N[C@@H](C)c3ncc(F)cn3)ncc2Br)n[nH]1 | 2.6 |
Cc1nc(C)c(nc1C(=O)N)c2ccc([C@@H]3CC[C@@H](CC(=O)O)CC3)c(F)c2 | 1.3 |
COc1ccnc(CCc2nc3c(C)ccnc3[nH]2)c1 | 2.1 |
Cc1cc(CCCOc2c(Cl)cc(cc2Cl)C3=NCCO3)on1 | 3.72 |
CN(C)C(=O)c1ccc(CN2CCc3cc4nc(N)sc4cc3CC2)cc1 | 1.72 |
COC(=O)[C@H]1[C@@H](O)CC[C@H]2CN3CCc4c([nH]c5ccccc45)[C@@H]3C[C@H]12 | 1.65 |
CCN1CCN(CC1)c2ccc(Nc3cc(ncn3)N(C)C(=O)Nc4c(Cl)c(OC)cc(OC)c4Cl)cc2 | 3.7 |
CC(C)N(CCCNC(=O)Nc1ccc(cc1)C(C)(C)C)C[C@H]2O[C@H]([C@H](O)[C@@H]2O)n3cnc4c(N)ncnc34 | 2.2 |
CCN(CC)CCCCNc1ncc2CN(C(=O)N(Cc3cccc(NC(=O)C=C)c3)c2n1)c4c(Cl)c(OC)cc(OC)c4Cl | 2.04 |
CCSc1c(Cc2ccccc2C(F)(F)F)sc3N(CC(C)C)C(=O)N(C)C(=O)c13 | 4.49 |
COc1ccc(Cc2c(N)n[nH]c2N)cc1 | 0.2 |
CCN(CCN(C)C)S(=O)(=O)c1ccc(cc1)c2cnc(N)c(n2)C(=O)Nc3cccnc3 | 2 |
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MoleculeNet Lipophilicity
Lipophilicity dataset, part of MoleculeNet [1] benchmark. It is intended to be used through scikit-fingerprints library.
The task is to predict octanol/water distribution coefficient (logD) at pH 7.4. Targets are already log transformed, and are a unitless ratio.
Characteristic | Description |
---|---|
Tasks | 1 |
Task type | regression |
Total samples | 4200 |
Recommended split | scaffold |
Recommended metric | RMSE |
References
[1] Wu, Zhenqin, et al. "MoleculeNet: a benchmark for molecular machine learning." Chemical Science 9.2 (2018): 513-530 https://pubs.rsc.org/en/content/articlelanding/2018/sc/c7sc02664a
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