SMILES
stringlengths 2
174
| Y
float64 -0.34
10.2
|
|---|---|
[O-][N+](=Nc1ccccc1)c1ccccc1
| 2.505
|
BrC(Br)Br
| 2.343
|
C=CBr
| 2.33
|
Brc1ccc(-c2ccc(Br)c(Br)c2Br)c(Br)c1Br
| 1.465
|
S=C=Nc1ccc(Br)cc1
| 2.729
|
O=c1[nH]c2ccc(Br)cc2o1
| 2.33
|
Brc1ccccc1
| 1.765
|
O=C1CN=C(c2ccccn2)c2cc(Br)ccc2N1
| 2.21
|
Brc1nc(Br)c(Br)[nH]1
| 3.952
|
BrCBr
| 3.207
|
O=P(OCC(Br)CBr)(OCC(Br)CBr)OCC(Br)CBr
| 2.839
|
BrCC=CCBr
| 3.455
|
BrCCBr
| 3.24
|
O=C(CCBr)N1CCN(C(=O)CCBr)CC1
| 3.209
|
C#CCCCC#C
| 1.603
|
C#CCOCC#C
| 2.242
|
C(CC1CO1)OCCC1CO1
| 2.17
|
C(CCC1CO1)CC1CO1
| 2.124
|
c1ccc(COCc2ccccc2)cc1
| 1.899
|
C(OCC1CO1)C1CO1
| 2.461
|
C[Si](C)(C)OP(=O)(O[Si](C)(C)C)O[Si](C)(C)C
| 1.961
|
C[Si](Cl)(Cl)CCCC#N
| 1.809
|
C=C1CC(=O)N(c2cccc([N+](=O)[O-])c2)C1=O
| 1.968
|
C=C1CC(=O)O1
| 2.176
|
C=CC#N
| 2.833
|
C=CC(=O)NCNC(=O)C=C
| 2.597
|
C=CC(=O)OC1CC2CCC1C2
| 1.468
|
C=CC(=O)OCC1CO1
| 2.785
|
C=CC(=O)OCCC#N
| 2.842
|
C=CC(=O)OCCOC(=O)C=C
| 2.754
|
C=CC(=O)OCCOc1ccccc1
| 1.569
|
C=CC(=O)OCCOCCC#N
| 2.179
|
C=CC(=O)OCCOCCOC(=O)C=C
| 2.729
|
C=CC=C
| 0.994
|
C=CC=CC=C
| 2.582
|
C=CC=NNc1ccccc1
| 2.32
|
C=CC=O
| 3.086
|
C=Cc1ccccc1
| 1.319
|
C=Cc1ccccn1
| 3.022
|
C=Cc1ccncc1
| 3.022
|
C=CC1CC2C=CC1C2
| 1.44
|
C=CC1CCC(C=C)OC1
| 1.75
|
C=CC1CC=CCC1
| 1.625
|
C=CC1CCC2OC2C1
| 1.793
|
C=CC1OCC2(CO1)COC(C=C)OC2
| 1.718
|
C=CCC#N
| 2.766
|
C=CCc1ccc2c(c1)OCO2
| 1.92
|
C=CCc1cccc(CC=C)c1OCC1CO1
| 1.71
|
C=CCC1CC(=O)OC1=O
| 2.117
|
C=CCCC=O
| 2.132
|
C=CCN(CC=C)CC=C
| 2.125
|
C=CCN(CC=C)N=O
| 2.198
|
C=CCN=C=S
| 2.947
|
C=CCNCC=C
| 2.226
|
C=CCOC(=O)C=CC(=O)OCC=C
| 2.816
|
C=CCOC(=O)c1ccccc1C(=O)OCC=C
| 2.505
|
C=CCOC(=O)Cc1ccccc1
| 2.433
|
C=CCOC(=O)CC1CCCCC1
| 2.306
|
C=CCOC(=O)CCC1CCCCC1
| 2.526
|
C=CCOC(OCC=C)C(OCC=C)OCC=C
| 2.256
|
C=CCOC=C
| 2.185
|
C=CCOC=O
| 2.842
|
C=CCOc1ccccc1C(=O)NC1CCCCC1
| 2.335
|
C=CCOC1CC2CC1CC2OCC=C
| 1.747
|
C=CCOCC=C
| 2.487
|
C=CCOCCC#N
| 1.932
|
C=CN1CC1
| 2.895
|
C=CN1CCCC1=O
| 1.879
|
C=COC(=O)c1ccccc1
| 1.659
|
C=COC=O
| 1.407
|
C=COCCOCCOC=C
| 1.627
|
C=CS(=O)(=O)C=C
| 3.567
|
C=O
| 1.574
|
c1cc(-c2ccncc2)ccn1
| 2.958
|
c1ccc(-c2ccc(C(c3ccccc3)n3ccnc3)cc2)cc1
| 2.327
|
c1ccc(-c2ccccc2)cc1
| 1.808
|
c1ccc(N=Nc2ccccc2)cc1
| 2.261
|
c1ccc(P(c2ccccc2)c2ccccc2)cc1
| 2.574
|
c1ccc(-c2ccccn2)nc1
| 3.194
|
c1ccc2cnccc2c1
| 2.555
|
c1ccccc1
| 1.373
|
c1ccncc1
| 1.948
|
c1ccc2c(c1)-c1cccc3cccc-2c13
| 2.005
|
c1ccc2ncccc2c1
| 2.591
|
c1cc2ccc3cccc4ccc(c1)c2c34
| 1.875
|
c1ccc2ccccc2c1
| 2.418
|
C1=CC=CCC=C1
| 3.209
|
C1=CC2C3C=CC(C3)C2C1
| 2.573
|
C1=CCC2CC=CC2C1
| 1.508
|
C1=CC2C=CC1C2
| 2.015
|
C1C2CC3OC3C2CC2OC12
| 1.852
|
C1C2OC2C2C1C1CC2C2OC12
| 2.893
|
C1=CCC(C2CC3C=CC2C3)CC1
| 1.609
|
C1=CCCC1
| 1.614
|
C1CC2CC1C1SSSC21
| 2.802
|
C1CC2CC1CC2CNCC1CC2CCC1C2
| 2.219
|
C1CC2OC2C1OC1CCC2OC12
| 1.93
|
C1CC2OC2CC1C1CO1
| 1.818
|
C1CCC(OC2CCCC2)C1
| 2.516
|
c1ccc(Nc2ccc(NC3CCCCC3)cc2)cc1
| 2.124
|
TDC Acute Toxicity LD50
Acute Toxicity LD50 dataset dataset [1], part of TDC [2] benchmark. It is intended to be used through scikit-fingerprints library.
Acute toxicity LD50 measures the most conservative dose that can lead to lethal adverse effects. The regression task is to predict the acute toxicity of drugs.
| Characteristic | Description |
|---|---|
| Tasks | 1 |
| Task type | regression |
| Total samples | 7385 |
| Recommended split | scaffold |
| Recommended metric | MAE |
References
[1] Zhu, Hao, et al. "Quantitative Structure−Activity Relationship Modeling of Rat Acute Toxicity by Oral Exposure" Chemical Research in Toxicology 22.12 (2009): 1913-1921. https://doi.org/10.1021/tx900189p
[2] Huang, Kexin, et al. "Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development" Proceedings of Neural Information Processing Systems, NeurIPS Datasets and Benchmarks, 2021 https://openreview.net/forum?id=8nvgnORnoWr
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