smiles
stringlengths 15
56
|
|---|
CCCS(=O)c1ccc2[nH]c(=NC(=O)OC)[nH]c2c1 |
CC(C)(C)C(=O)C(Oc1ccc(Cl)cc1)n1ccnc1 |
CC1C2CCC(C2)C1CN(CCO)C(=O)c1ccc(Cl)cc1 |
Cn1cnc2c1c(=O)n(CC(O)CO)c(=O)n2C |
CC1Oc2ccc(Cl)cc2N(CC(O)CO)C1=O |
CCOC(=O)c1cncn1C1CCCc2ccccc21 |
COc1ccccc1OC(=O)c1ccccc1OC(C)=O |
O=C1Nc2ccc(Cl)cc2C(c2ccccc2Cl)=NC1O |
CCC(=O)c1ccc(OCC(O)CO)c(OC)c1 |
Cc1nc2c([nH]1)c(=O)n(C)c(=O)n2CC1CC=CCC1 |
COc1cc2c(cc1O)N=CC1CCC(O)N1C2=O |
COc1cc(C)c(Cc2cnc(N)nc2N)cc1OC |
O=C1Nc2ccc(Cl)cc2C(c2ccccc2)=NC1O |
CC1CC(OC(=O)CN2CCCC2=O)CC(C)(C)C1 |
COc1ccc(OC)c(Cc2cnc3nc(N)nc(N)c3c2C)c1 |
COC(=O)c1c[nH]c2cc(OC(C)C)c(OC(C)C)cc2c1=O |
CCC1NC(=O)c2cc(S(N)(=O)=O)c(Cl)cc2N1 |
COc1cc(C(=O)N2CCOCC2)cc(OC)c1OC |
COc1ccc(C=C2CCCN=C2c2cccnc2)c(OC)c1 |
CC(=O)Nc1ccc(S(=O)(=O)c2ccc(NC(C)=O)cc2)cc1 |
CC1(C)C(=O)Nc2cc3nc(-c4ccncc4)[nH]c3cc21 |
Cc1nnc2n1-c1ccc(Cl)cc1C(c1ccccc1)=NC2 |
CC(C)(C#N)c1cc(Cn2cncn2)cc(C(C)(C)C#N)c1 |
CNC(=O)Oc1ccccc1C(=O)Nc1ccccc1 |
COc1ccc(-c2nnc(C)nc2-c2ccc(OC)cc2)cc1 |
Cc1cc(Cc2cnc(N)nc2N)c2cccnc2c1N(C)C |
CC(=O)Nc1ccc(OC(=O)c2ccccc2OC(C)=O)cc1 |
CC1(C)C=C(n2ccccc2=O)c2cc(C#N)ccc2O1 |
O=C1CN=C(c2ccccn2)c2cc(Br)ccc2N1 |
O=c1[nH]c(=O)n(C2CC(O)C(CO)O2)cc1Br |
Oc1c(Br)cc(Br)c2cccnc12 |
CN(CCO)c1nc2c(c(=O)n(C)c(=O)n2C)n1C |
CCC1(O)C(=O)OCc2c1cc1n(c2=O)Cc2cc3ccccc3nc2-1 |
NS(=O)(=O)c1cc2c(cc1Cl)CN(C1CCCCC1)C2=O |
Nc1nc2c(ncn2C2CC(CO)C2CO)c(=O)[nH]1 |
CC12CCC3=C(CCc4cc(O)ccc43)C1CCC2=O |
CNC(=O)c1ccc(Cl)c(S(=O)(=O)NC)c1 |
NC(=O)c1cc(Br)cc(Br)c1O |
OCCN(CCO)c1nc(-c2ccccc2)c(-c2ccccc2)o1 |
CCCN(CCC)S(=O)(=O)c1ccc(C)cc1 |
O=C1OCC(Cc2cccc(O)c2)C1Cc1cccc(O)c1 |
Clc1ccc2c(c1)C(c1ccccc1)=NCc1nncn1-2 |
CCc1ccccc1-n1c(C)nc2ccccc2c1=O |
CCc1cc2c(s1)-n1c(C)nnc1CN=C2c1ccccc1Cl |
CCc1nc(N)nc(N)c1-c1ccc(Cl)c(Cl)c1 |
COc1ccc(-c2cc(=O)c3c(O)c(OC)c(OC)cc3o2)cc1O |
CN1C(=O)CN=C(c2ccccc2F)c2cc(Cl)ccc21 |
CCOC(=O)c1ncn2c1CN(C)C(=O)c1cc(F)ccc1-2 |
Cc1cn(C2OC(CO)C(O)C2F)c(=O)[nH]c1=O |
Nc1nc2c(ncn2COC(CO)CO)c(=O)[nH]1 |
Cc1nc2ccccc2c(=O)n1-c1ccccc1Cl |
CCOc1ccc(-n2c(C)nc3ccccc3c2=O)cc1 |
Cc1nc2ccccc2c(=O)n1-c1ccc(Cl)cc1 |
Cc1cc2c(cc1S(N)(=O)=O)S(=O)(=O)CCC2 |
CC(=O)Nc1ccc(OC(=O)c2cccs2)cc1 |
COc1ccc(C2Cc3cccc(O)c3C(=O)O2)cc1O |
Clc1ccccc1-c1nc(-c2ccncc2)no1 |
C#CCN1C(=O)CN=C(c2ccccc2)c2cc(Cl)ccc21 |
CC(C)(Oc1ccc(Cl)cc1)C(=O)OCc1cccc(CO)n1 |
Cc1c2c(cn1C)NC(=O)CN=C2c1ccccc1 |
CCn1ccc(NS(=O)(=O)c2ccc(N)cc2)nc1=O |
Nc1ccc(S(=O)(=O)Nc2cnccn2)cc1 |
CC(=O)N(c1onc(C)c1C)S(=O)(=O)c1ccc(N)cc1 |
NS(=O)(=O)c1ccc(N2CCCCS2(=O)=O)cc1 |
Cc1ncc(Cn2c(C)c(CCO)sc2=O)c(N)n1 |
Cc1nnc2n1-c1ccc(Cl)cc1C(c1ccccc1Cl)=NC2 |
CN1C(=O)CC(=O)N(c2ccccc2)c2cc(C(F)(F)F)ccc21 |
O=c1[nH]c(=O)n(C2CC(O)C(CO)O2)cc1C(F)(F)F |
Oc1c(Br)cc(Cl)c2cccnc12 |
CS(=O)(=O)c1ccc(-c2cn3ccccc3n2)cc1 |
Cc1nn(C)c2c1C(c1cccc(Cl)c1)=NCCN2 |
NS(=O)(=O)c1cc(C(=O)c2ccc(O)cc2)cs1 |
COC1C(O)C(CO)OC1n1cnc2c(N)ncnc21 |
Nc1nc(N)nc(-c2cc(Cl)ccc2Cl)n1 |
N#Cc1ccc(Nc2ncnc3c2CCC3O)cc1 |
CN(C)c1ncnc2c1ncn2Cc1ccccc1 |
O=C1c2cccnc2CN1Cc1c(F)cccc1F |
COc1ccc(CN2Cc3ncccc3C2=O)cc1 |
O=C1c2cccnc2CN1Cc1ccc(Cl)cc1 |
O=C1c2cccnc2CN1Cc1ccccc1Cl |
O=C1c2cccnc2CN1Cc1ccccc1C(F)(F)F |
CCc1nc(C#N)c(N2CCc3ccccc3CC2)nc1C |
COc1cccc(-c2csc(-c3cccnc3)n2)c1 |
O=C(NC1CCc2ccccc2C1)c1ccncc1 |
CC1(F)OC(n2cc(F)c(=O)[nH]c2=O)C(O)C1O |
CSc1nc2ccccc2n1Cc1ccccn1 |
Cn1c(=O)c2c(ncn2CC2OCCO2)n(C)c1=O |
CC(=O)c1ccc(NS(C)(=O)=O)c(Oc2ccc(F)cc2F)c1 |
Cn1c(=O)c2c(ncn2CC(=O)N2CCOCC2)n(C)c1=O |
CN1Cc2c(C(=O)OC(C)(C)C)ncn2-c2ccsc2C1=O |
Nc1nc2c(ncn2C2OC(CO)C(O)C2F)c(=O)[nH]1 |
COc1ccc(O)cc1Cc1cnc2nc(N)nc(N)c2c1C |
CNC(=O)OCc1nc(SC)n(C)c1COC(=O)NC |
OC(Cn1cncn1)(Cn1cncn1)c1ccc(F)cc1F |
O=c1[nH]c(=O)n(C2CC(F)C(CO)O2)cc1Cl |
CC(C)n1c(=O)c2c(-c3noc(C4CC4)n3)ncn2c2ccccc21 |
O=C1CCCN1S(=O)(=O)c1ccc(Cl)cc1 |
Cc1[nH]cnc1Cc1nc(-c2ccccc2)cs1 |
COc1ccc(-c2nc3n(c2-c2ccncc2)CCC3)cc1 |
Nc1nc(NC2CC2)c2ncn(C3C=CC(CO)C3)c2n1 |
End of preview. Expand
in Dataset Viewer.
YAML Metadata
Warning:
empty or missing yaml metadata in repo card
(https://huggingface.co/docs/hub/datasets-cards)
MOSES Molecule Generation Dataset
Dataset Description
Molecular Sets (MOSES) is a benchmark platform for distribution learning based molecule generation. Within this benchmark, MOSES provides a cleaned dataset of molecules that are ideal of optimization. It is processed from the ZINC Clean Leads dataset.
Task Description
For both distribution learning-based and goal-oriented molecule generation. That is to generate new molecules that has desirable properties measured by some oracles.
Dataset Statistics
1,936,962 molecules, including:
- 1,355,874 in train
- 193,696 in validation
- 387,392 in test
The random split has been made by Therapeutics Data Commons.
Reference
- Downloads last month
- 80