Structure

Physi-Chem Properties

Molecular Weight:  305.16
Volume:  324.371
LogP:  1.415
LogD:  0.476
LogS:  -1.856
# Rotatable Bonds:  10
TPSA:  64.63
# H-Bond Aceptor:  5
# H-Bond Donor:  1
# Rings:  1
# Heavy Atoms:  5

MedChem Properties

QED Drug-Likeness Score:  0.711
Synthetic Accessibility Score:  2.243
Fsp3:  0.412
Lipinski Rule-of-5:  Accepted
Pfizer Rule:  Accepted
GSK Rule:  Accepted
BMS Rule:  0
Golden Triangle Rule:  Accepted
Chelating Alert:  0
PAINS Alert:  0

ADMET Properties (ADMETlab2.0)

ADMET: Absorption

Caco-2 Permeability:  -4.609
MDCK Permeability:  4.2494910303503275e-05
Pgp-inhibitor:  0.731
Pgp-substrate:  0.061
Human Intestinal Absorption (HIA):  0.022
20% Bioavailability (F20%):  0.003
30% Bioavailability (F30%):  0.009

ADMET: Distribution

Blood-Brain-Barrier Penetration (BBB):  0.975
Plasma Protein Binding (PPB):  54.29434585571289%
Volume Distribution (VD):  0.766
Pgp-substrate:  24.03243637084961%

ADMET: Metabolism

CYP1A2-inhibitor:  0.136
CYP1A2-substrate:  0.506
CYP2C19-inhibitor:  0.356
CYP2C19-substrate:  0.885
CYP2C9-inhibitor:  0.114
CYP2C9-substrate:  0.754
CYP2D6-inhibitor:  0.035
CYP2D6-substrate:  0.869
CYP3A4-inhibitor:  0.282
CYP3A4-substrate:  0.468

ADMET: Excretion

Clearance (CL):  7.837
Half-life (T1/2):  0.917

ADMET: Toxicity

hERG Blockers:  0.063
Human Hepatotoxicity (H-HT):  0.127
Drug-inuced Liver Injury (DILI):  0.119
AMES Toxicity:  0.042
Rat Oral Acute Toxicity:  0.161
Maximum Recommended Daily Dose:  0.525
Skin Sensitization:  0.669
Carcinogencity:  0.055
Eye Corrosion:  0.005
Eye Irritation:  0.062
Respiratory Toxicity:  0.228

Download Data

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

  Natural Product: NPC101908

Natural Product ID:  NPC101908
Common Name*:   XAJXBWHLLVMCFS-OBUCXMDBSA-N
IUPAC Name:   n.a.
Synonyms:  
Standard InCHIKey:  XAJXBWHLLVMCFS-OBUCXMDBSA-N
Standard InCHI:  InChI=1S/C17H23NO4/c1-4-18-17(20)8-6-5-7-14(19)11-13-9-10-15(21-2)16(12-13)22-3/h5-10,12,14,19H,4,11H2,1-3H3,(H,18,20)/b7-5+,8-6+/t14-/m0/s1
SMILES:  CCN=C(/C=C/C=C/[C@@H](Cc1ccc(c(c1)OC)OC)O)O
Synthetic Gene Cluster:   n.a.
ChEMBL Identifier:   n.a.
PubChem CID:   72196849
Chemical Classification**:  
  • CHEMONTID:0000000 [Organic compounds]
    • [CHEMONTID:0002448] Benzenoids
      • [CHEMONTID:0002279] Benzene and substituted derivatives
        • [CHEMONTID:0004113] Methoxybenzenes
          • [CHEMONTID:0004111] Dimethoxybenzenes

*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

Organism ID Organism Name Taxonomy Level Family SuperKingdom Isolation Part Collection Location Collection Time Reference
NPO6257 Ficus septica Species Moraceae Eukaryota Stems Tainan Hsien, Taiwan, Republic of China 2000-JAN PMID[16038551]
NPO6257 Ficus septica Species Moraceae Eukaryota leaves n.a. n.a. PMID[19938815]
NPO2618 Curcuma wenyujin Species Zingiberaceae Eukaryota n.a. rhizome n.a. PMID[23738470]
NPO2618 Curcuma wenyujin Species Zingiberaceae Eukaryota n.a. root n.a. PMID[23738470]
NPO2618 Curcuma wenyujin Species Zingiberaceae Eukaryota Rhizomes n.a. n.a. PMID[25275213]
NPO794 Lippia graveolens Species Verbenaceae Eukaryota Root n.a. n.a. Database[FooDB]
NPO794 Lippia graveolens Species Verbenaceae Eukaryota Shoot n.a. n.a. Database[FooDB]
NPO794 Lippia graveolens Species Verbenaceae Eukaryota n.a. n.a. Database[FooDB]
NPO13118 Paeonia officinalis Species Paeoniaceae Eukaryota n.a. n.a. n.a. Database[HerDing]
NPO6257 Ficus septica Species Moraceae Eukaryota n.a. n.a. n.a. Database[HerDing]
NPO13118 Paeonia officinalis Species Paeoniaceae Eukaryota n.a. n.a. n.a. Database[TCMID]
NPO6257 Ficus septica Species Moraceae Eukaryota n.a. n.a. n.a. Database[TCMID]
NPO2618 Curcuma wenyujin Species Zingiberaceae Eukaryota n.a. n.a. n.a. Database[TCM_Taiwan]
NPO2618 Curcuma wenyujin Species Zingiberaceae Eukaryota n.a. n.a. n.a. Database[TM-MC]
NPO12080 Metrodorea flavida Species Rutaceae Eukaryota n.a. n.a. n.a. Database[UNPD]
NPO10161 Macaranga pleiostemona Species Euphorbiaceae Eukaryota n.a. n.a. n.a. Database[UNPD]
NPO13118 Paeonia officinalis Species Paeoniaceae Eukaryota n.a. n.a. n.a. Database[UNPD]
NPO11817 Seiridium cardinale Species Sporocadaceae Eukaryota n.a. n.a. n.a. Database[UNPD]
NPO1187 Baccharis scandens Species Asteraceae Eukaryota n.a. n.a. n.a. Database[UNPD]
NPO794 Lippia graveolens Species Verbenaceae Eukaryota n.a. n.a. n.a. Database[UNPD]
NPO7346 Ligularia odontomanes Species Asteraceae Eukaryota n.a. n.a. n.a. Database[UNPD]
NPO1863 Alpinia pricei Species Zingiberaceae Eukaryota n.a. n.a. n.a. Database[UNPD]
NPO8078 Palaquium leiocarpum Species Sapotaceae Eukaryota n.a. n.a. n.a. Database[UNPD]
NPO6257 Ficus septica Species Moraceae Eukaryota n.a. n.a. n.a. Database[UNPD]
NPO2618 Curcuma wenyujin Species Zingiberaceae Eukaryota n.a. n.a. n.a. Database[UNPD]
NPO3390 Radermachia sinica n.a. n.a. n.a. n.a. n.a. n.a. Database[UNPD]
NPO474 Beilschmiedia kunstleri Species Lauraceae Eukaryota n.a. n.a. n.a. Database[UNPD]
NPO20070.1 Crinum stuhlmannii subsp. delagoense Subspecies Amaryllidaceae Eukaryota n.a. n.a. n.a. Database[UNPD]
NPO11706 Gordonia terrae Species Gordoniaceae Bacteria n.a. n.a. n.a. Database[UNPD]
NPO12193 Metadina trichotoma Species Rubiaceae Eukaryota n.a. n.a. n.a. Database[UNPD]
NPO15978 Nalanthamala diospyri Species Nectriaceae Eukaryota n.a. n.a. n.a. Database[UNPD]
NPO12316 Pterospermum lanceolatum Species Malvaceae Eukaryota n.a. n.a. n.a. Database[UNPD]
NPO11948 Ferulago sylvatica Species Apiaceae Eukaryota n.a. n.a. n.a. Database[UNPD]

☑ 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

Organism ID NP ID Organism Material Preparation Organism Part NP Quantity (Standard) NP Quantity (Minimum) NP Quantity (Maximum) Quantity Unit Reference

☑ 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

Target ID Target Type Target Name Target Organism Activity Type Activity Relation Value Unit Reference
NPT165 Cell Line HeLa Homo sapiens IC50 = 14580.0 nM PMID[478514]
NPT81 Cell Line A549 Homo sapiens IC50 = 13830.0 nM PMID[478514]
NPT83 Cell Line MCF7 Homo sapiens IC50 = 12870.0 nM PMID[478514]
NPT744 Cell Line IMR-32 Homo sapiens IC50 = 9430.0 nM PMID[478514]

☑ 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 NPC101908 and all remaining natural products in the NPASS database.
●  The right table: Most similar natural products (Tc>=0.56 or Top200).

Similarity Score Similarity Level Natural Product ID

  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 NPC101908 and all drugs/candidates.
●  The right table: Most similar clinical/approved drugs (Tc>=0.56 or Top200).

Similarity Score Similarity Level Drug ID Developmental Stage

  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