SMILES
stringlengths 1
98
| label
float64 -11.6
1.58
|
---|---|
OCC3OC(OCC2OC(OC(C#N)c1ccccc1)C(O)C(O)C2O)C(O)C(O)C3O | -0.77 |
Cc1occc1C(=O)Nc2ccccc2 | -3.3 |
CC(C)=CCCC(C)=CC(=O) | -2.06 |
c1ccc2c(c1)ccc3c2ccc4c5ccccc5ccc43 | -7.87 |
c1ccsc1 | -1.33 |
c2ccc1scnc1c2 | -1.5 |
Clc1cc(Cl)c(c(Cl)c1)c2c(Cl)cccc2Cl | -7.32 |
CC12CCC3C(CCc4cc(O)ccc34)C2CCC1O | -5.03 |
ClC4=C(Cl)C5(Cl)C3C1CC(C2OC12)C3C4(Cl)C5(Cl)Cl | -6.29 |
COc5cc4OCC3Oc2c1CC(Oc1ccc2C(=O)C3c4cc5OC)C(C)=C | -4.42 |
O=C1CCCN1 | 1.07 |
Clc1ccc2ccccc2c1 | -4.14 |
CCCC=C | -2.68 |
CCC1(C(=O)NCNC1=O)c2ccccc2 | -2.64 |
CCCCCCCCCCCCCC | -7.96 |
CC(C)Cl | -1.41 |
CCC(C)CO | -0.47 |
N#Cc1ccccc1 | -1 |
CCOP(=S)(OCC)Oc1cc(C)nc(n1)C(C)C | -3.64 |
CCCCCCCCCC(C)O | -2.94 |
Clc1ccc(c(Cl)c1)c2c(Cl)ccc(Cl)c2Cl | -7.43 |
O=c2[nH]c1CCCc1c(=O)n2C3CCCCC3 | -4.594 |
CCOP(=S)(OCC)SCSCC | -4.11 |
CCOc1ccc(NC(=O)C)cc1 | -2.35 |
CCN(CC)c1c(cc(c(N)c1N(=O)=O)C(F)(F)F)N(=O)=O | -5.47 |
CCCCCCCO | -1.81 |
Cn1c(=O)n(C)c2nc[nH]c2c1=O | -1.39 |
CCCCC1(CC)C(=O)NC(=O)NC1=O | -1.661 |
ClC(Cl)=C(c1ccc(Cl)cc1)c2ccc(Cl)cc2 | -6.9 |
CCCCCCCC(=O)OC | -3.17 |
CCc1ccc(CC)cc1 | -3.75 |
CCOP(=S)(OCC)SCSC(C)(C)C | -4.755 |
COC(=O)Nc1cccc(OC(=O)Nc2cccc(C)c2)c1 | -4.805 |
ClC(=C)Cl | -1.64 |
Cc1cccc2c1Cc3ccccc32 | -5.22 |
CCCCC=O | -0.85 |
N(c1ccccc1)c2ccccc2 | -3.504 |
CN(C)C(=O)SCCCCOc1ccccc1 | -3.927 |
CCCOP(=S)(OCCC)SCC(=O)N1CCCCC1C | -4.15 |
CCCCCCCI | -4.81 |
c1c(Cl)cccc1c2ccccc2 | -4.88 |
OCCCC=C | -0.15 |
O=C2NC(=O)C1(CCC1)C(=O)N2 | -1.655 |
CC(C)C1CCC(C)CC1O | -2.53 |
CC(C)OC=O | -0.63 |
CCCCCC(C)O | -1.55 |
CC(=O)Nc1ccc(Br)cc1 | -3.083 |
c1ccccc1n2ncc(N)c(Br)c2(=O) | -3.127 |
COC(=O)C1=C(C)NC(=C(C1c2ccccc2N(=O)=O)C(=O)OC)C | -4.76 |
c2c(C)cc1nc(C)ccc1c2 | -1.94 |
CCCCCCC#C | -3.66 |
CCC1(C(=O)NC(=O)NC1=O)C2=CCCCC2 | -2.17 |
c1ccc2c(c1)ccc3c4ccccc4ccc23 | -8.057 |
CCC(C)n1c(=O)[nH]c(C)c(Br)c1=O | -2.523 |
Clc1cccc(c1Cl)c2c(Cl)c(Cl)cc(Cl)c2Cl | -8.6 |
Cc1ccccc1O | -0.62 |
CC(C)CCC(C)(C)C | -5.05 |
Cc1ccc(C)c2ccccc12 | -4.14 |
Cc1cc2c3ccccc3ccc2c4ccccc14 | -6.57 |
CCCC(=O)C | -0.19 |
Clc1cc(Cl)c(Cl)c(c1Cl)c2c(Cl)c(Cl)cc(Cl)c2Cl | -9.15 |
CCCOC(=O)CC | -0.82 |
CC34CC(O)C1(F)C(CCC2=CC(=O)C=CC12C)C3CC(O)C4(O)C(=O)CO | -3.68 |
Nc1ccc(O)cc1 | -0.8 |
O=C(Cn1ccnc1N(=O)=O)NCc2ccccc2 | -2.81 |
OC4=C(C1CCC(CC1)c2ccc(Cl)cc2)C(=O)c3ccccc3C4=O | -5.931 |
CCNc1nc(Cl)nc(n1)N(CC)CC | -4.06 |
NC(=O)c1cnccn1 | -0.667 |
CCC(Br)(CC)C(=O)NC(N)=O | -2.68 |
Clc1ccccc1c2ccccc2Cl | -5.27 |
O=C2CN(N=Cc1ccc(o1)N(=O)=O)C(=O)N2 | -3.38 |
Clc2ccc(Oc1ccc(cc1)N(=O)=O)c(Cl)c2 | -5.46 |
CC1(C)C2CCC1(C)C(=O)C2 | -1.96 |
O=C1NC(=O)NC(=O)C1(CC=C)c1ccccc1 | -2.369 |
CCCCC(=O)OCC | -2.25 |
CC(C)CCOC(=O)C | -1.92 |
O=C1N(COC(=O)CCCCC)C(=O)C(N1)(c2ccccc2)c3ccccc3 | -5.886 |
Clc1cccc(c1)c2cc(Cl)ccc2Cl | -6.01 |
CCCBr | -1.73 |
CCCC1COC(Cn2cncn2)(O1)c3ccc(Cl)cc3Cl | -3.493 |
COP(=S)(OC)SCC(=O)N(C)C=O | -1.995 |
Cc1ncnc2nccnc12 | -0.466 |
NC(=S)N | 0.32 |
Cc1ccc(C)cc1 | -2.77 |
CCc1ccccc1CC | -3.28 |
ClC(Cl)(Cl)C(Cl)(Cl)Cl | -3.67 |
CC(C)C(C(=O)OC(C#N)c1cccc(Oc2ccccc2)c1)c3ccc(OC(F)F)cc3 | -6.876 |
CCCN(=O)=O | -0.8 |
CC(C)C1CCC(C)CC1=O | -2.35 |
CCN2c1cc(Cl)ccc1NC(=O)c3cccnc23 | -5.36 |
O=N(=O)c1c(Cl)c(Cl)ccc1 | -3.48 |
CCCC(C)C1(CC=C)C(=O)NC(=S)NC1=O | -3.46 |
c1ccc2c(c1)c3cccc4cccc2c34 | -6 |
CCCOC(C)C | -1.34 |
Cc1cc(C)c2ccccc2c1 | -4.29 |
CCC(=C(CC)c1ccc(O)cc1)c2ccc(O)cc2 | -4.07 |
c1(C#N)c(Cl)c(C#N)c(Cl)c(Cl)c(Cl)1 | -5.64 |
Clc1ccc(Cl)c(c1)c2ccc(Cl)c(Cl)c2 | -7.25 |
C1OC1c2ccccc2 | -1.6 |
CC(C)c1ccccc1 | -3.27 |
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MoleculeNet ESOL
ESOL (Estimated SOLubility) dataset [1], part of MoleculeNet [2] benchmark. It is intended to be used through scikit-fingerprints library.
The task is to predict aqueous solubility. Targets are log-transformed, and the unit is log mols per litre (log Mol/L).
Characteristic | Description |
---|---|
Tasks | 1 |
Task type | regression |
Total samples | 1128 |
Recommended split | scaffold |
Recommended metric | RMSE |
References
[1] John S. Delaney "ESOL: Estimating Aqueous Solubility Directly from Molecular Structure" J. Chem. Inf. Comput. Sci. 2004, 44, 3, 1000–1005 https://pubs.acs.org/doi/10.1021/ci034243x
[2] 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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