Structure

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Physi-Chem Properties

Molecular Weight:  358.21
Volume:  385.377
LogP:  3.879
LogD:  3.347
LogS:  -4.486
# Rotatable Bonds:  3
TPSA:  63.6
# H-Bond Aceptor:  4
# H-Bond Donor:  1
# Rings:  3
# Heavy Atoms:  4

MedChem Properties

QED Drug-Likeness Score:  0.588
Synthetic Accessibility Score:  4.362
Fsp3:  0.636
Lipinski Rule-of-5:  Accepted
Pfizer Rule:  Rejected
GSK Rule:  Accepted
BMS Rule:  0
Golden Triangle Rule:  Accepted
Chelating Alert:  0
PAINS Alert:  1

ADMET Properties (ADMETlab2.0)

ADMET: Absorption

Caco-2 Permeability:  -4.75
MDCK Permeability:  2.6471590899745934e-05
Pgp-inhibitor:  0.973
Pgp-substrate:  0.004
Human Intestinal Absorption (HIA):  0.005
20% Bioavailability (F20%):  0.809
30% Bioavailability (F30%):  0.074

ADMET: Distribution

Blood-Brain-Barrier Penetration (BBB):  0.019
Plasma Protein Binding (PPB):  94.24066162109375%
Volume Distribution (VD):  0.861
Pgp-substrate:  3.456778049468994%

ADMET: Metabolism

CYP1A2-inhibitor:  0.747
CYP1A2-substrate:  0.792
CYP2C19-inhibitor:  0.504
CYP2C19-substrate:  0.793
CYP2C9-inhibitor:  0.606
CYP2C9-substrate:  0.359
CYP2D6-inhibitor:  0.887
CYP2D6-substrate:  0.111
CYP3A4-inhibitor:  0.787
CYP3A4-substrate:  0.241

ADMET: Excretion

Clearance (CL):  2.637
Half-life (T1/2):  0.136

ADMET: Toxicity

hERG Blockers:  0.021
Human Hepatotoxicity (H-HT):  0.776
Drug-inuced Liver Injury (DILI):  0.121
AMES Toxicity:  0.025
Rat Oral Acute Toxicity:  0.94
Maximum Recommended Daily Dose:  0.898
Skin Sensitization:  0.948
Carcinogencity:  0.888
Eye Corrosion:  0.064
Eye Irritation:  0.052
Respiratory Toxicity:  0.969

Download Data

Data Type Select
General Info & Identifiers & Properties  
Structure MOL file  
Source Organisms  
Biological Activities  
Similar NPs/Drugs  

  Natural Product: NPC8518

Natural Product ID:  NPC8518
Common Name*:   Isospongiaquinone
IUPAC Name:   3-[[(1R,2S,4aS,8aS)-1,2,4a,5-tetramethyl-2,3,4,7,8,8a-hexahydronaphthalen-1-yl]methyl]-2-hydroxy-5-methoxycyclohexa-2,5-diene-1,4-dione
Synonyms:   Isospongiaquinone
Standard InCHIKey:  LKNAVZKSKJJHQH-YVUMSICPSA-N
Standard InCHI:  InChI=1S/C22H30O4/c1-13-7-6-8-18-21(13,3)10-9-14(2)22(18,4)12-15-19(24)16(23)11-17(26-5)20(15)25/h7,11,14,18,24H,6,8-10,12H2,1-5H3/t14-,18+,21+,22+/m0/s1
SMILES:  COC1=CC(=O)C(=C(C1=O)C[C@]1(C)[C@@H](C)CC[C@]2([C@H]1CCC=C2C)C)O
Synthetic Gene Cluster:   n.a.
ChEMBL Identifier:   CHEMBL496254
PubChem CID:   11810478
Chemical Classification**:  
  • CHEMONTID:0000000 [Organic compounds]
    • [CHEMONTID:0000012] Lipids and lipid-like molecules
      • [CHEMONTID:0000259] Prenol lipids
        • [CHEMONTID:0001534] Quinone and hydroquinone lipids
          • [CHEMONTID:0002802] Prenylquinones

*Note: the InCHIKey will be temporarily assigned as the "Common Name" if no IUPAC name or alternative short name is available.
**Note: the Chemical Classification was calculated by NPClassifier Version 1.5. Reference: PMID:34662515.

  Species Source

☑ Note for Reference:
In addition to directly collecting NP source organism data from primary literature (where reference will provided as NCBI PMID or DOI links), NPASS also integrated them from below databases:
UNPD: Universal Natural Products Database [PMID: 23638153].
StreptomeDB: a database of streptomycetes natural products [PMID: 33051671].
TM-MC: a database of medicinal materials and chemical compounds in Northeast Asian traditional medicine [PMID: 26156871].
TCM@Taiwan: a Traditional Chinese Medicine database [PMID: 21253603].
TCMID: a Traditional Chinese Medicine database [PMID: 29106634].
TCMSP: The traditional Chinese medicine systems pharmacology database and analysis platform [PMID: 24735618].
HerDing: a herb recommendation system to treat diseases using genes and chemicals [PMID: 26980517].
MetaboLights: a metabolomics database [PMID: 27010336].
FooDB: a database of constituents, chemistry and biology of food species [www.foodb.ca].

  NP Quantity Composition/Concentration

☑ Note for Reference:
In addition to directly collecting NP quantitative data from primary literature (where reference will provided as NCBI PMID or DOI links), NPASS also integrated NP quantitative records for specific NP domains (e.g., NPS from foods or herbs) from domain-specific databases. These databases include:
DUKE: Dr. Duke's Phytochemical and Ethnobotanical Databases.
PHENOL EXPLORER: is the first comprehensive database on polyphenol content in foods [PMID: 24103452], its homepage can be accessed at here.
FooDB: a database of constituents, chemistry and biology of food species [www.foodb.ca].

  Biological Activity

☑ Note for Activity Records:
☉ The quantitative biological activities were primarily integrated from ChEMBL (Version-30) database and were also directly collected from PubMed literature. PubMed PMID was provided as the reference link for each activity record.

  Chemically structural similarity: I. Similar Active Natural Products in NPASS

Top-200 similar NPs were calculated against the active-NP-set (includes 4,3285 NPs with experimentally-derived bioactivity available in NPASS)

Similarity level is defined by Tanimoto coefficient (Tc) between two molecules. Tc lies between [0, 1] where '1' indicates the highest similarity. What is Tanimoto coefficient

●  The left chart: Distribution of similarity level between NPC8518 and all remaining natural products in the NPASS database.
●  The right table: Most similar natural products (Tc>=0.56 or Top200).

  Chemically structural similarity: II. Similar Clinical/Approved Drugs

Similarity level is defined by Tanimoto coefficient (Tc) between two molecules.

●  The left chart: Distribution of similarity level between NPC8518 and all drugs/candidates.
●  The right table: Most similar clinical/approved drugs (Tc>=0.56 or Top200).

  Bioactivity similarity: Similar Natural Products in NPASS

Bioactivity similarity was calculated based on bioactivity descriptors of compounds. The bioactivity descriptors were calculated by a recently developed AI algorithm Chemical Checker (CC) [Nature Biotechnology, 38:1087–1096, 2020; Nature Communications, 12:3932, 2021], which evaluated bioactivity similarities at five levels:
A: chemistry similarity;
B: biological targets similarity;
C: networks similarity;
D: cell-based bioactivity similarity;
E: similarity based on clinical data.

Those 5 categories of CC bioactivity descriptors were calculated and then subjected to manifold projection using UMAP algorithm, to project all NPs on a 2-Dimensional space. The current NP was highlighted with a small circle in the 2-D map. Below figures: left-to-right, A-to-E.

A: chemistry similarity
B: biological targets similarity
C: networks similarity
D: cell-based bioactivity similarity
E: similarity based on clinical data