rk-random/PACT-Net
Graph Machine Learning
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Updated
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SMILES
stringlengths 1
82
| label
float64 -25.47
3.43
|
|---|---|
CN(C)C(=O)c1ccc(cc1)OC
| -11.01
|
CS(=O)(=O)Cl
| -4.87
|
CC(C)C=C
| 1.83
|
CCc1cnccn1
| -5.45
|
CCCCCCCO
| -4.21
|
Cc1cc(cc(c1)O)C
| -6.27
|
CC(C)C(C)C
| 2.34
|
CCCC(C)(C)O
| -3.92
|
C[C@@H]1CCCC[C@@H]1C
| 1.58
|
CC[C@H](C)O
| -4.62
|
C(Br)Br
| -1.96
|
CC[C@H](C(C)C)O
| -3.88
|
CCc1ccccn1
| -4.33
|
CCCCC(=O)OCC
| -2.49
|
c1ccc(cc1)S
| -2.55
|
CC(=CCC/C(=C\CO)/C)C
| -4.78
|
c1ccc2c(c1)CCC2
| -1.46
|
CCOc1ccccc1
| -2.22
|
c1cc(ccc1O)Br
| -5.85
|
CCCC(C)(C)C
| 2.88
|
CC(=O)OCCOC(=O)C
| -6.34
|
CCOP(=S)(OCC)SCSP(=S)(OCC)OCC
| -6.1
|
C1CCCC(CC1)O
| -5.48
|
COC(=O)C1CC1
| -4.1
|
c1ccc(cc1)C#N
| -4.1
|
CCCCC#N
| -3.52
|
CC(C)(C)O
| -4.47
|
CC(C)C(=O)C(C)C
| -2.74
|
CCC=O
| -3.43
|
CN(C)C=O
| -7.81
|
Cc1ccc(cc1)C
| -0.8
|
C=CCC=C
| 0.93
|
Cc1cccc(c1C)Nc2ccccc2C(=O)O
| -6.78
|
CN(C)C(=O)c1ccccc1
| -9.29
|
CCNCC
| -4.07
|
CC(C)(C)c1ccc(cc1)O
| -5.91
|
CC(C)CCOC=O
| -2.13
|
CCCCCCCCCCO
| -3.64
|
CCC(=O)OCC
| -2.68
|
CCCCCCCCC
| 3.13
|
CC(=O)NC
| -10
|
CCCCCCCC=C
| 2.06
|
c1ccc2cc(ccc2c1)O
| -8.11
|
c1cc(c(cc1Cl)Cl)Cl
| -1.12
|
C([C@H]([C@H]([C@@H]([C@@H](CO)O)O)O)O)O
| -23.62
|
CCCC(=O)OC
| -2.83
|
c1ccc(c(c1)C=O)O
| -4.68
|
C1CNC1
| -5.56
|
CCCNCCC
| -3.65
|
c1ccc(cc1)N
| -5.49
|
C(F)(F)(F)F
| 3.12
|
CC[C@@H](C)CO
| -4.42
|
c1ccc(c(c1)O)I
| -6.2
|
COc1cccc(c1O)OC
| -6.96
|
CCC#C
| -0.16
|
c1ccc(cc1)C(F)(F)F
| -0.25
|
NN
| -9.3
|
Cc1ccccn1
| -4.63
|
CCNc1nc(nc(n1)Cl)NCC
| -10.22
|
c1ccc2c(c1)Oc3cc(c(cc3O2)Cl)Cl
| -3.56
|
CCCCCCCCN
| -3.65
|
N
| -4.29
|
c1ccc(c(c1)C(F)(F)F)C(F)(F)F
| 1.07
|
COC(=O)c1ccc(cc1)O
| -9.51
|
CCCCCc1ccccc1
| -0.23
|
CC(F)F
| -0.11
|
c1ccc(cc1)n2c(=O)c(c(cn2)N)Cl
| -16.43
|
C=CC=C
| 0.56
|
CN(C)C
| -3.2
|
CCCCCC(=O)N
| -9.31
|
CC(C)CO[N+](=O)[O-]
| -1.88
|
c1ccc2c(c1)C(=O)c3cccc(c3C2=O)NCCO
| -14.21
|
C(CO[N+](=O)[O-])O
| -8.18
|
CCCCCCC(=O)C
| -2.88
|
CN1CCNCC1
| -7.77
|
CCN
| -4.5
|
C1C=CC=CC=C1
| -0.99
|
c1ccc2c(c1)Cc3ccccc3C2
| -3.78
|
CC(Cl)Cl
| -0.84
|
COc1cccc(c1)O
| -7.66
|
c1cc2cccc3c2c(c1)CC3
| -3.15
|
CCCCCCCCBr
| 0.52
|
c1ccc(cc1)CO
| -6.62
|
c1c(c(=O)[nH]c(=O)[nH]1)Br
| -18.17
|
CCCC
| 2.1
|
CCl
| -0.55
|
CC(C)CBr
| -0.03
|
CC(C)SC(C)C
| -1.21
|
CCCCCCC
| 2.67
|
c1cnc[nH]1
| -9.63
|
c1cc2c(cc1Cl)Oc3cc(c(c(c3O2)Cl)Cl)Cl
| -3.84
|
CC[C@H](C)n1c(=O)c(c([nH]c1=O)C)Br
| -9.73
|
C(I)I
| -2.49
|
CCCN(CCC)C(=O)SCCC
| -4.13
|
C[N+](=O)[O-]
| -4.02
|
CCOC
| -2.1
|
COC(CCl)(OC)OC
| -4.59
|
CC(C)C
| 2.3
|
CC(C)CC(=O)O
| -6.09
|
CCOP(=O)(OCC)O/C(=C/Cl)/c1ccc(cc1Cl)Cl
| -7.07
|
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MoleculeNet FreeSolv
FreeSolv (Free Solvation Database) dataset [1], part of MoleculeNet [2] benchmark. It is intended to be used through scikit-fingerprints library.
The task is to predict hydration free energy of small molecules in water. Targets are in kcal/mol.
| Characteristic | Description |
|---|---|
| Tasks | 1 |
| Task type | regression |
| Total samples | 642 |
| Recommended split | scaffold |
| Recommended metric | RMSE |
References
[1] Mobley, D.L., Guthrie, J.P. "FreeSolv: a database of experimental and calculated hydration free energies, with input files" J Comput Aided Mol Des 28, 711–720 (2014) https://link.springer.com/article/10.1007/s10822-014-9747-x
[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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Models trained or fine-tuned on scikit-fingerprints/MoleculeNet_FreeSolv