PeptideCLM-2
Collection
Official models and data for the PeptideCLM-2 paper, featuring MLM, MTR, and Hybrid architectures.
•
10 items
•
Updated
source
stringclasses 2
values | split
stringclasses 1
value | smiles
stringlengths 4
2.5k
| Chi0
float64 -0.56
10.9
| Chi0n
float64 -0.57
11.7
| Chi0v
float64 -0.56
10.9
| Chi1
float64 -0.56
2.62
| Chi1n
float64 -0.01
2.42k
| FpDensityMorgan2
float64 -2.16
11.4
| FpDensityMorgan3
float64 -2.43
6.93
| Kappa1
float64 -2.71
4.96
| Kappa2
float64 -0.55
3.41
| Kappa3
float64 -0.56
3.28
| ExactMolWt
float64 -0.57
15.3
| MaxAbsPartialCharge
float64 -0.55
3.87
| MaxPartialCharge
float64 -0.57
5.15
| MinAbsPartialCharge
float64 -0.58
158
| MinPartialCharge
float64 -0.57
6.9
| MolLogP
float64 -0.55
1.15k
| MolMR
float64 -8.85
32.1
| MolWt
float64 -0.55
5.27
| RingCount
float64 -0.84
10.7
| HeavyAtomCount
float64 -0.84
7.42k
| HeavyAtomMolWt
float64 -0.56
3.67
| FractionCSP3
float64 -0.5
4.79
| HallKierAlpha
float64 -0.53
7.08
| LabuteASA
float64 -0.79
66.2
| TPSA
float64 -0.47
5.03
| NHOHCount
float64 -0.73
160
| NOCount
float64 -0.49
164
| NumAliphaticCarbocycles
float64 -0.48
3.77
| NumAliphaticHeterocycles
float64 -0.84
78.7
| NumAliphaticRings
float64 -0.47
55.5
| NumAromaticCarbocycles
float64 -0.97
199
| NumAromaticHeterocycles
float64 -0.62
13.9
| NumAromaticRings
float64 -0.65
16.5
| NumHAcceptors
float64 -0.64
55.4
| NumHDonors
float64 -0.66
31.4
| NumHeteroatoms
float64 -0.56
7.76
| NumRadicalElectrons
float64 -0.55
20.6
| NumRotatableBonds
float64 -0.44
52.6
| NumSaturatedCarbocycles
float64 -0.52
8.26
| NumSaturatedHeterocycles
float64 -0.55
21.4
| NumSaturatedRings
float64 -0.54
4.68
| NumValenceElectrons
float64 -0.63
25.2
| PEOE_VSA1
float64 -0.83
17.3
| PEOE_VSA10
float64 0
0
| PEOE_VSA11
float64 -0.68
434
| PEOE_VSA12
float64 -0.5
7.73
| PEOE_VSA13
float64 -0.76
161
| PEOE_VSA14
float64 -0.56
86.3
| PEOE_VSA2
float64 -0.73
128
| PEOE_VSA3
float64 -0.53
9.34
| PEOE_VSA4
float64 -0.56
7.96
| PEOE_VSA5
float64 -0.63
130
| PEOE_VSA6
float64 -0.57
7.82
| PEOE_VSA7
float64 -0.84
19.7
| PEOE_VSA8
float64 -1.47
329
| PEOE_VSA9
float64 -0.52
59.9
| SMR_VSA1
float64 0
0
| SMR_VSA10
float64 -0.5
3.31
| SMR_VSA2
float64 -1.97
2.4
| SMR_VSA3
float64 -0.56
3.62
| SMR_VSA4
float64 -0.47
4.88
| SMR_VSA5
float64 -0.51
3.75
| SMR_VSA6
float64 -0.36
509
| SMR_VSA7
float64 -0.65
63.1
| SMR_VSA8
float64 -0.76
227
| SMR_VSA9
float64 -0.7
35.5
| SlogP_VSA1
float64 -0.65
78.6
| SlogP_VSA10
float64 -0.73
43.1
| SlogP_VSA11
float64 -0.55
6.09
| SlogP_VSA12
float64 -0.47
5.42
| SlogP_VSA2
float64 -0.53
4.64
| SlogP_VSA3
float64 -0.49
4.39
| SlogP_VSA4
float64 -0.3
593
| SlogP_VSA5
float64 -0.57
64.3
| SlogP_VSA6
float64 -0.64
235
| SlogP_VSA7
float64 -0.83
66.2
| SlogP_VSA8
float64 -19.04
19.2
| SlogP_VSA9
float64 -0.58
4.4
| fr_amide
float64 -0.44
41.4
| fr_NH0
float64 -0.43
26.9
| fr_NH1
float64 -0.47
3.78
| fr_NH2
float64 -0.8
79.9
| fr_COO
float64 -0.46
8.03
| fr_priamide
float64 -0.45
16.8
| fr_guanido
float64 -0.27
258
| fr_imidazole
float64 -0.46
7.65
| fr_phenol
float64 -0.51
144
| fr_Al_OH
float64 -0.7
35.2
| fr_C_O
float64 -0.29
255
| fr_ether
float64 -0.51
76.8
| fr_alkyl_halide
float64 -0.36
20.4
| fr_unbrch_alkane
float64 -0.37
24.1
| fr_aryl_methyl
float64 -0.25
304
| fr_benzene
float64 -0.39
177
| fr_ester
float64 -0.36
46.6
| fr_ketone
float64 -0.39
39.7
| fr_methoxy
float64 -0.4
64.6
| fr_sulfide
float64 -0.25
119
| fr_sulfonamd
float64 -0.31
66.2
| __index_level_0__
int64 0
100k
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
LMSD_clean.smiles
|
train
|
CC/C=C\C/C=C\C/C=C\C/C=C\CCCCC(=O)OC[C@H](COP(=O)(O)O[C@@H]1[C@H](O)[C@H](O)[C@@H](O)[C@H](O)[C@H]1O)OC(=O)CCCCCCCCCCCCCC
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| -0.386244
| -0.357579
| -0.392679
| -0.403323
| -0.246117
| 2.528616
| 4,677
|
LMSD_clean.smiles
|
train
|
CC1(C)[C@@H](O)CC[C@]2(C)[C@H]3CC=C4[C@@H]5C[C@@](C)(CO)CC[C@]5(C(=O)O)CC[C@@]4(C)[C@]3(C)CC[C@@H]12
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| 0
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| 0
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| 6.021877
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| 2.097926
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| 5.646757
| -0.565721
| 1.720816
| 0.006962
| 0.627337
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| 0.193207
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| -0.701811
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| -0.386244
| -0.357579
| -0.392679
| -0.403323
| -0.246117
| -0.306224
| 9,502
|
LMSD_clean.smiles
|
train
|
CCCCCCCCCCCCC/C=C/[C@@H](O)[C@H](CO[C@@H]1O[C@H](CO)[C@@H](O[C@@H]2O[C@H](CO)[C@H](O[C@@H]3O[C@H](CO)[C@H](O)[C@H](O[C@@H]4O[C@H](CO)[C@H](O)[C@H](O[C@H]5O[C@H](CO)[C@H](O)[C@H](O)[C@H]5O)[C@H]4O)[C@H]3NC(C)=O)[C@H](O)[C@H]2O)[C@H](O)[C@H]1O)NC(=O)CCCCCCCCCCCCCCCCCCCCC
| -0.043683
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| -1.314722
| -1.315872
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| 0.022345
| 0.001773
| 0.00602
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| 0.42803
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| 0.04471
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| 0.175268
| 0.35715
| 2.132807
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| 0
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| 2.156276
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| 0.175894
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| 0.128986
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| 2.613358
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| 0.006962
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| 4.659872
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| -0.386244
| -0.357579
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| -0.403323
| -0.246117
| 6.01765
| 9,667
|
LMSD_clean.smiles
|
train
|
CC(C)CCC[C@@H](C)[C@H]1CCC2=C3C=C[C@@]4(O)C[C@@H](O)CC[C@]4(C)[C@H]3CC[C@@]21C
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| 0.23987
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| -0.306224
| 17,767
|
LMSD_clean.smiles
|
train
|
CCCCCC/C=C\CCCCCCCC(=O)OC[C@H](COP(=O)([O-])OCC[N+](C)(C)C)OC(C)=O
| -0.378237
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| -0.357579
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| -0.403323
| -0.246117
| 1.656358
| 18,691
|
LMSD_clean.smiles
|
train
|
CO[C@H]1[C@H](O[C@H]2CC[C@]3(C=O)[C@H]4CC[C@]5(C)[C@@H](C6=CC(=O)OC6)CC[C@]5(O)[C@@H]4CC[C@]3(O)C2)O[C@H](C)[C@@H](O)[C@@H]1OC
| -0.362889
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| 0.083722
| 0.257136
| 0.32975
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| 0.477825
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| 0
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| 1.83531
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| 4.746359
| 0.595632
| 2.668885
| -0.701582
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| -0.733045
| -0.293152
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| -0.38713
| -0.420309
| 5.646757
| 0.070095
| 2.312128
| 0.174979
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| 0.512255
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| 1.770103
| 2.336006
| -0.363788
| -0.368013
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| 3.310256
| -0.357579
| -0.392679
| -0.403323
| -0.246117
| -0.306224
| 23,839
|
LMSD_clean.smiles
|
train
|
CCCCCC/C=C\CCCCCCCCCCOCC(=O)COP(=O)(O)O
| -0.419294
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| -0.41922
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| 0.523471
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LMSD_clean.smiles
|
train
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LMSD_clean.smiles
|
train
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LMSD_clean.smiles
|
train
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LMSD_clean.smiles
|
train
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LMSD_clean.smiles
|
train
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LMSD_clean.smiles
|
train
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LMSD_clean.smiles
|
train
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CC[C@@H](O)/C=C/[C@H]1[C@@H](O)CC(=O)[C@@H]1C/C=C\C/C=C\C/C=C\CCC(=O)O
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LMSD_clean.smiles
|
train
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CCCCCCCCCCCCCCCCCCCC[C@H](C)C[C@H](C)/C=C(\C)C(=O)OC[C@H]1O[C@H](O[C@H]2O[C@H](CO)[C@@H](OC(=O)/C(C)=C/[C@@H](C)C[C@@H](C)CCCCCCCCCCCCCCCCCCCC)[C@H](O)[C@H]2OC(=O)/C(C)=C/[C@@H](C)C[C@@H](C)CCCCCCCCCCCCCCCCCCCC)[C@H](OC(=O)CCCCCCCCCCCCCCCCC)[C@@H](OC(=O)/C(C)=C/[C@@H](C)C[C@@H](C)CCCCCCCCCCCCCCCCCCCC)[C@@H]1O
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LMSD_clean.smiles
|
train
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CCCC/C=C\C=C/C/C=C/C=C/C#CCCCCCCC(=O)CCCOC(C)=O
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LMSD_clean.smiles
|
train
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LMSD_clean.smiles
|
train
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LMSD_clean.smiles
|
train
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LMSD_clean.smiles
|
train
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CC/C=C\C/C=C\C/C=C\C/C=C\C/C=C\C/C=C\CCC(=O)OC[C@H](COC(OCC[N+](C)(C)C)C(=O)[O-])OC(=O)CCC/C=C\C/C=C\C/C=C\C/C=C\C/C=C\CC
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LMSD_clean.smiles
|
train
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LMSD_clean.smiles
|
train
|
CCCCCCCCCCCCC/C=C/[C@@H](O)[C@H](CO[C@@H]1O[C@H](CO)[C@@H](O[C@@H]2O[C@H](CO)[C@H](O)[C@H](O[C@@H]3O[C@H](CO)[C@@H](O[C@@H]4O[C@H](CO[C@@H]5O[C@H](CO)[C@@H](O[C@@H]6O[C@H](CO)[C@H](O)[C@H](O[C@H]7O[C@H](CO)[C@H](O)[C@H](O)[C@H]7O)[C@H]6O[C@H]6O[C@H](C)[C@@H](O)[C@@H](O)[C@@H]6O)[C@H](O)[C@H]5NC(C)=O)[C@H](O)[C@H](O[C@@H]5O[C@H](CO)[C@@H](O[C@@H]6O[C@H](CO)[C@H](O)[C@H](O[C@]7(C(=O)O)C[C@H](O)[C@@H](NC(C)=O)[C@H]([C@H](O)[C@H](O)CO)O7)[C@H]6O)[C@H](O)[C@H]5NC(C)=O)[C@H]4O)[C@H](O)[C@H]3NC(C)=O)[C@H]2O)[C@H](O)[C@H]1O)NC(=O)CCCCCCCCCCCCCCCCCCCCCCCCC
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|
LMSD_clean.smiles
|
train
|
CC/C=C\C/C=C\C/C=C\C/C=C\C/C=C\CCCC(=O)O[C@@H](CO)COC(=O)CCCCCCC/C=C\CCCCCCC
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LMSD_clean.smiles
|
train
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Cc1c2cc3c(c1O)C(=O)C[C@H](O3)c1ccc(O)c(c1)-c1ccc(O)c(c1)C(=O)OC[C@H]1O[C@@H](O2)[C@H](O)[C@@H](O)[C@@H]1O
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train
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LMSD_clean.smiles
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train
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LMSD_clean.smiles
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train
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LMSD_clean.smiles
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train
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LMSD_clean.smiles
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train
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LMSD_clean.smiles
|
train
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LMSD_clean.smiles
|
train
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COc1c(O)cc(-c2oc3cc(O)cc(O)c3c(=O)c2O[C@@H]2O[C@@H](C)[C@H](OC(C)=O)[C@@H](O)[C@H]2O)cc1O
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LMSD_clean.smiles
|
train
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LMSD_clean.smiles
|
train
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train
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LMSD_clean.smiles
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train
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LMSD_clean.smiles
|
train
|
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|
LMSD_clean.smiles
|
train
|
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LMSD_clean.smiles
|
train
|
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|
LMSD_clean.smiles
|
train
|
CCCCC/C=C\C/C=C\CCCCCCCC(=O)OC[C@H](COP(=O)([O-])OCC[N+](C)(C)C)OC(=O)CCCCCCC/C=C\CCCCCC
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LMSD_clean.smiles
|
train
|
CCCCCCCCCCCCCCCCC(=O)OC(CCCCCCCCC)CCCCCCCC(=O)O
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LMSD_clean.smiles
|
train
|
O=C(O)CCCCCCCCCCCCc1ccccc1
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| 40,355
|
LMSD_clean.smiles
|
train
|
CC(C)=CCC/C(C)=C/Cc1c2c(c(O)c3c(=O)c4c(oc13)-c1c(O)cc(O)c3c1C(C4)C(C)(C)O3)C=CC(C)(C)O2
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| 1.987851
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| 40,806
|
LMSD_clean.smiles
|
train
|
CCCC/C=C\C/C=C\CCCCCCCC(=O)OC[C@H](COP(=O)([O-])OCC[N+](C)(C)C)OC(=O)CCCCCCCCCCCCCCCCCCCC
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| 43,264
|
LMSD_clean.smiles
|
train
|
CCCCCCCC/C=C\CCCCCCCC(=O)OC[C@H](COP(=O)(O)O[C@H]1[C@@H](O)[C@@H](O)[C@H](O)[C@@H](O)[C@@H]1O[C@H]1O[C@H](CO)[C@@H](O)[C@H](O)[C@@H]1O)OC(=O)CCCCCCCCCCCCC
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LMSD_clean.smiles
|
train
|
CCCCC/C=C\C[C@H]1[C@H](/C=C/[C@@H](O)CCCC(=O)O)[C@H](O)C[C@@H]1O
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|
LMSD_clean.smiles
|
train
|
COc1cc(/C=C/C(=O)c2c(O)cc3c(c2OC)OCO3)c(OC)c(OC)c1OC
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End of preview. Expand
in Data Studio
PeptideMTR Training Data
This repository contains the dataset for the PeptideMTR paper. It is designed for SMILES encoder models trained by masked-language modeling (MLM) and/or multi-target regression (MTR) tasks, focusing on mapping peptide sequences to biochemical properties.
Link to the manuscript will be added here when available.
Dataset Summary
The dataset includes peptide sequences paired with 99 RDKit-derived descriptors representing various physicochemical properties (e.g., molecular weight, LogP, surface area, and charge descriptors).
Data Structure
SMILES: The SMILES representation of the molecule.descriptors: 99 continuous numerical features generated via RDKit.
Usage
To use this dataset with the Hugging Face datasets library:
from datasets import load_dataset
ds = load_dataset("your-username/PeptideMTR_training_data")
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Size of downloaded dataset files:
34.3 GB
Size of the auto-converted Parquet files:
34.3 GB
Number of rows:
117,908,328