Datasets:

scikit-fingerprints
/

TDC_ld50_zhu

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

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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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