rk-random/PACT-Net
Graph Machine Learning
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Updated
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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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Number of rows:
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Models trained or fine-tuned on scikit-fingerprints/MoleculeNet_ESOL