Structure

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Physi-Chem Properties

Molecular Weight:  548.34
Volume:  589.142
LogP:  4.226
LogD:  4.173
LogS:  -4.539
# Rotatable Bonds:  8
TPSA:  98.82
# H-Bond Aceptor:  8
# H-Bond Donor:  2
# Rings:  3
# Heavy Atoms:  8

MedChem Properties

QED Drug-Likeness Score:  0.529
Synthetic Accessibility Score:  4.447
Fsp3:  0.5
Lipinski Rule-of-5:  Accepted
Pfizer Rule:  Accepted
GSK Rule:  Rejected
BMS Rule:  0
Golden Triangle Rule:  Rejected
Chelating Alert:  0
PAINS Alert:  0

ADMET Properties (ADMETlab2.0)

ADMET: Absorption

Caco-2 Permeability:  -4.905
MDCK Permeability:  0.000128700296045281
Pgp-inhibitor:  0.999
Pgp-substrate:  0.011
Human Intestinal Absorption (HIA):  0.039
20% Bioavailability (F20%):  0.007
30% Bioavailability (F30%):  0.028

ADMET: Distribution

Blood-Brain-Barrier Penetration (BBB):  0.021
Plasma Protein Binding (PPB):  95.80585479736328%
Volume Distribution (VD):  0.597
Pgp-substrate:  1.4382575750350952%

ADMET: Metabolism

CYP1A2-inhibitor:  0.009
CYP1A2-substrate:  0.09
CYP2C19-inhibitor:  0.418
CYP2C19-substrate:  0.921
CYP2C9-inhibitor:  0.874
CYP2C9-substrate:  0.121
CYP2D6-inhibitor:  0.031
CYP2D6-substrate:  0.211
CYP3A4-inhibitor:  0.968
CYP3A4-substrate:  0.835

ADMET: Excretion

Clearance (CL):  5.929
Half-life (T1/2):  0.602

ADMET: Toxicity

hERG Blockers:  0.024
Human Hepatotoxicity (H-HT):  0.68
Drug-inuced Liver Injury (DILI):  0.856
AMES Toxicity:  0.033
Rat Oral Acute Toxicity:  0.808
Maximum Recommended Daily Dose:  0.059
Skin Sensitization:  0.016
Carcinogencity:  0.011
Eye Corrosion:  0.003
Eye Irritation:  0.006
Respiratory Toxicity:  0.012

Download Data

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

  Natural Product: NPC470546

Natural Product ID:  NPC470546
Common Name*:   (3S,6S,9S,12S)-6,12-Dibenzyl-3,9-Di-Sec-Butyl-1,7-Dimethyl-1,4,7,10-Tetraazacyclododecane-2,5,8,11-Tetraone
IUPAC Name:   (3S,6S,9S,12S)-6,12-dibenzyl-3,9-bis[(2S)-butan-2-yl]-1,7-dimethyl-1,4,7,10-tetrazacyclododecane-2,5,8,11-tetrone
Synonyms:  
Standard InCHIKey:  ZHRZJBVCMAGJJK-XGSSJENOSA-N
Standard InCHI:  InChI=1S/C32H44N4O4/c1-7-21(3)27-31(39)35(5)26(20-24-17-13-10-14-18-24)30(38)34-28(22(4)8-2)32(40)36(6)25(29(37)33-27)19-23-15-11-9-12-16-23/h9-18,21-22,25-28H,7-8,19-20H2,1-6H3,(H,33,37)(H,34,38)/t21-,22-,25-,26-,27-,28-/m0/s1
SMILES:  CC[C@@H]([C@@H]1N=C(O)[C@H](Cc2ccccc2)N(C)C(=O)[C@@H](N=C([C@@H](N(C1=O)C)Cc1ccccc1)O)[C@H](CC)C)C
Synthetic Gene Cluster:   n.a.
ChEMBL Identifier:   CHEMBL2063169
PubChem CID:   60155058
Chemical Classification**:  
  • CHEMONTID:0000000 [Organic compounds]
    • [CHEMONTID:0000264] Organic acids and derivatives
      • [CHEMONTID:0000265] Carboxylic acids and derivatives
        • [CHEMONTID:0000013] Amino acids, peptides, and analogues
          • [CHEMONTID:0000348] Peptides
            • [CHEMONTID:0004831] Oligopeptides

*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 NPC470546 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 NPC470546 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