Natural Product: NPC600998

The Chemical Classification was calculated by Classyfire, a software for chemical taxonomy calculation. Reference: DOI:10.1186/s13321-016-0174-y.

  Chemical Representations

  Calculated Properties

Physi-Chem Properties

MedChem Properties

ADMET Properties (ADMETlab3.0)

ADMET: Absorption

ADMET: Distribution

ADMET: Metabolism

ADMET: Excretion

ADMET: Toxicity

  Species Source

Organism ID Organism Name Taxonomy Level Family SuperKingdom Isolation Part Collection Location Collection Time Reference

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 Organism Name 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

Molecular-level activity

Target ID Target Type Target Name Target Organism Activity Type Activity Relation Value Unit Reference

In vitro activity

Target ID Target Type Target Name Target Organism Activity Type Activity Relation Value Unit Reference

In vivo activity

Target ID Target Type Target Name Target Organism Activity Type Activity Relation Value Unit Reference





 Experimental ADME

Experiment Model Experiment Tissue ADME Type ADME Relation ADME Value ADME Unit Reference





 Experimental Toxicity

Quantitative toxicity

Experiment Model Experiment Organism Toxicity Type Toxicity Relation Toxicity Value Toxicity Unit Reference

Common Abbreviations:
LC: Lethal Concentration; LD: Lethal Dose; LT:Lethal Time; NOAEL: No-observed-adverse-effect Level; BMDL: Benchmark Dose Lower Confidence Limit; BMD: Benchmark Dose; BMC:Benchmark Concentration; LOAEL: Lowest Observed Adverse Effect Level; RfD:Reference Dose; RfC:Reference Concentration; MRL: Minimal Risk Level; MEG: Maximum Exposure Guideline; PAC: Protective Action Criteria

Categorical toxicity labels

Hepatotoxicity Carcinogenicity Mutagenicity Cardiotoxicity Respiratory Toxicity Eye Irritation Endocrine Disruption
Hepatotoxicity Carcinogenicity Mutagenicity Cardiotoxicity Respiratory Toxicity Eye Irritation Endocrine Disruption

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 toxicity records from domain-specific databases. These databases include:
ToxValDB: a curated database that compiles quantitative toxicity values for chemicals from diverse public sources to support toxicological research and risk assessment.
TOXRIC: a comprehensive, free-to-access, online database providing toxicological/feature data. The toxicity labels are retrieved from this database. [PMID: 36400569]


  Chemically structural similarity

Similar Active Natural Products in NPASS

Top-200 similar NPs were calculated against the active-NP-set (includes approximately 50,000 NPs with experimentally-derived bioactivity available in NPASS)

Similarity is measured using the Tanimoto coefficient (Tc) , which compares the binary fingerprints of two molecules. Tc is calculated as the intersection divided by the union of '1' bits in the fingerprints, ranging from 0 to 1, with 1 indicating highest similarity.

●  The left chart: Distribution of similarity level between NPC600998 and all remaining natural products in the NPASS database.
●  The right table: Most similar natural products (Tc>=0.5 or Top200).

Similarity Score Similarity Level Natural Product ID
1.0 High Similarity NPC611186
0.9728 High Similarity NPC477630
0.9097 High Similarity NPC607485
0.8611 High Similarity NPC600735
0.8411 Intermediate Similarity NPC610952
0.8289 Intermediate Similarity NPC477639
0.8258 Intermediate Similarity NPC601264
0.8182 Intermediate Similarity NPC609988
0.8039 Intermediate Similarity NPC609634
0.7898 Intermediate Similarity NPC477638
0.7834 Intermediate Similarity NPC477631
0.7834 Intermediate Similarity NPC610535
0.7798 Intermediate Similarity NPC610704
0.7561 Intermediate Similarity NPC610817
0.7391 Intermediate Similarity NPC477636
0.7089 Intermediate Similarity NPC603072
0.7024 Intermediate Similarity NPC605177
0.6982 Remote Similarity NPC611013
0.6962 Remote Similarity NPC610870
0.6855 Remote Similarity NPC604370
0.6786 Remote Similarity NPC600008
0.6786 Remote Similarity NPC600634
0.6786 Remote Similarity NPC603103
0.6786 Remote Similarity NPC604252
0.6605 Remote Similarity NPC606505
0.6543 Remote Similarity NPC477637
0.64 Remote Similarity NPC610649
0.6205 Remote Similarity NPC608676
0.6045 Remote Similarity NPC607277
0.6034 Remote Similarity NPC608521
0.5988 Remote Similarity NPC601582
0.5988 Remote Similarity NPC603992
0.5988 Remote Similarity NPC610984
0.5787 Remote Similarity NPC607866
0.5741 Remote Similarity NPC603629
0.5741 Remote Similarity NPC606524
0.5731 Remote Similarity NPC610756
0.5642 Remote Similarity NPC607892
0.56 Remote Similarity NPC605666
0.5529 Remote Similarity NPC600192
0.5449 Remote Similarity NPC601205
0.5449 Remote Similarity NPC604059
0.5422 Remote Similarity NPC611091
0.5361 Remote Similarity NPC323927
0.5359 Remote Similarity NPC611717
0.534 Remote Similarity NPC611556
0.5337 Remote Similarity NPC600580
0.5337 Remote Similarity NPC605764
0.5337 Remote Similarity NPC605765
0.5326 Remote Similarity NPC600257
0.5326 Remote Similarity NPC603201
0.5325 Remote Similarity NPC603135
0.5307 Remote Similarity NPC610355
0.5291 Remote Similarity NPC601051
0.5291 Remote Similarity NPC609990
0.5254 Remote Similarity NPC478028
0.5227 Remote Similarity NPC478029
0.5185 Remote Similarity NPC607511
0.5167 Remote Similarity NPC601510
0.5148 Remote Similarity NPC604566
0.5029 Remote Similarity NPC604994

Similar Clinical/Approved Drugs

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

●  The left chart: Distribution of similarity level between NPC600998 and all drugs/candidates.
●  The right table: Most similar clinical/approved drugs (Tc>=0.5 or Top200).

Similarity Score Similarity Level Drug ID Developmental Stage
NPD

Bioactivity similarity

  Bioactivity similarity

Similar Natural Products in NPASS

Similarity level is defined by 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