IMPPAT Phytochemical information: 
Betulinic acid
Betulinic acid
Summary

SMILES: CC(=C)[C@@H]1CC[C@]2([C@H]1[C@H]1CC[C@H]3[C@@]([C@]1(C)CC2)(C)CC[C@@H]1[C@]3(C)CC[C@@H](C1(C)C)O)C(=O)O
InChI: InChI=1S/C30H48O3/c1-18(2)19-10-15-30(25(32)33)17-16-28(6)20(24(19)30)8-9-22-27(5)13-12-23(31)26(3,4)21(27)11-14-29(22,28)7/h19-24,31H,1,8-17H2,2-7H3,(H,32,33)/t19-,20+,21-,22+,23-,24+,27-,28+,29+,30-/m0/s1
InChIKey: QGJZLNKBHJESQX-FZFNOLFKSA-N
DeepSMILES: CC=C)[C@@H]CC[C@][C@H]5[C@H]CC[C@H][C@@][C@]6C)CC%10)))C)CC[C@@H][C@]6C)CC[C@@H]C6C)C))O))))))))))))))C=O)O
Scaffold Graph/Node/Bond level: C1CCC2C(C1)CCC1C2CCC2C3CCCC3CCC21
Scaffold Graph/Node level: C1CCC2C(C1)CCC1C2CCC2C3CCCC3CCC21
Scaffold Graph level: C1CCC2C(C1)CCC1C2CCC2C3CCCC3CCC21
Functional groups: C=C(C)C; CC(=O)O; CO
Chemical classification
ClassyFire Kingdom: Organic compounds
ClassyFire Superclass: Lipids and lipid-like molecules
ClassyFire Class: Prenol lipids
ClassyFire Subclass: Triterpenoids
NP Classifier Biosynthetic pathway: Terpenoids
NP Classifier Superclass: Triterpenoids
NP Classifier Class: Lupane triterpenoids
Synonymous chemical names:
betulic acid, betulic-acid, betulinic acid, betulinic acid, betulinic-acid, betulinicacid, betullinic acid, brtulinic acid
External chemical identifiers:
CID:64971; ChEMBL:CHEMBL269277; ChEBI:3087; ZINC:ZINC000004097714; FDASRS:4G6A18707N; SureChEMBL:SCHEMBL61767; MolPort-003-939-279
Chemical structure download
2D:2D MOL2D MOL22D SDF
3D:3D MOL3D MOL23D SDF3D PDB3D PDBQT
Betulinic acid
Physicochemical properties
Property name Tool Property value
Molecular weight (g/mol) RDKit 456.71
Log P RDKit 7.09
Topological polar surface area (Å2) RDKit 57.53
Number of hydrogen bond acceptors RDKit 2
Number of hydrogen bond donors RDKit 2
Number of carbon atoms RDKit 30
Number of heavy atoms RDKit 33
Number of heteroatoms RDKit 3
Number of nitrogen atoms RDKit 0
Number of sulfur atoms RDKit 0
Number of chiral carbon atoms RDKit 10
Stereochemical complexity RDKit 0.33
Number of sp hybridized carbon atoms RDKit 0
Number of sp2 hybridized carbon atoms RDKit 3
Number of sp3 hybridized carbon atoms RDKit 27
Shape complexity RDKit 0.9
Number of rotatable bonds RDKit 2
Number of aliphatic carbocycles RDKit 5
Number of aliphatic heterocycles RDKit 0
Number of aliphatic rings RDKit 5
Number of aromatic carbocycles RDKit 0
Number of aromatic heterocycles RDKit 0
Number of aromatic rings RDKit 0
Total number of rings RDKit 5
Number of saturated carbocycles RDKit 5
Number of saturated heterocycles RDKit 0
Number of saturated rings RDKit 5
Number of Smallest Set of Smallest Rings (SSSR) RDKit 5
Betulinic acid
Drug-likeness properties
Property nameToolProperty value
Number of Lipinski’s rule of 5 violations RDKit 1
Lipinski’s rule of 5 filter RDKit Passed
Number of Ghose filter violations RDKit 3
Ghose filter RDKit Failed
Veber filter RDKit Good
Pfizer 3/75 filter RDKit Bad
GSK 4/400 filter RDKit Bad
Weighted quantitative estimate of drug-likeness (QEDw) score RDKit 0.4361
Betulinic acid
ADMET properties
Property nameToolProperty value
Bioavailability score SwissADME 0.85
Solubility class [ESOL] SwissADME Poorly soluble
Solubility class [Silicos-IT] SwissADME Moderately soluble
Blood Brain Barrier permeation SwissADME No
Gastrointestinal absorption SwissADME Low
Log Kp (Skin permeation, cm/s) SwissADME -3.26
Number of PAINS structural alerts SwissADME 0.0
Number of Brenk structural alerts SwissADME 1.0
CYP1A2 inhibitor SwissADME No
CYP2C19 inhibitor SwissADME No
CYP2C9 inhibitor SwissADME Yes
CYP2D6 inhibitor SwissADME No
CYP3A4 inhibitor SwissADME No
P-glycoprotein substrate SwissADME No
Betulinic acid
Predicted human target proteins
Protein identifierHGNC symbolCombined score from STITCH database
ENSP00000215832MAPK1700
ENSP00000261366LMNB1800
ENSP00000263025MAPK3700
ENSP00000270202AKT1725
ENSP00000275493EGFR700
ENSP00000297494NOS3800
ENSP00000301633BIRC5724
ENSP00000307786CYCS824
ENSP00000311032CASP3739
ENSP00000329357SP1800
ENSP00000354522TOP1721
ENSP00000358223PNLIP800
ENSP00000358327CASP7700
ENSP00000379213PTHLH700
ENSP00000411532TOP2A700
The human target proteins were predicted using STITCH, a database of Chemical-Protein interaction networks.