List of Natural Products databases mentioned in scientific litterature since 2000The Table1_s3 is the most recent version.The table "stereoOverlapTable" references the percentage of agreement in terms of stereochemistry between databases when the latter share molecules.

A structure database of natural products in SDF format was created from the LOTUS database version 9 .

This database is intended to facilitate the dereplication of natural products.

The LOTUS database was described in this publication (free download).

File 220916_frozen_metadata.csv was downloaded from the LOTUS database version 9 and the SMILES chains of the compounds were collected.

The SMILES chains were translated to 2D chemical structures using python scripts relying on the RDKit library.

Each compound was associated to predicted 13C NMR chemical shifts by means of an already reported procedure (free download).

Each compound was also supplemented with metadata from file 220916_frozen_metadata.csv .

Archive file acd_lotusv9.sdf.zip contains acd_lotusv9.sdf with 218,478 compound descriptions inside.

Archive file acd_lotusv9.NMRUDB.zip is a compressed version of acd_lotusv9.NMRUDB, itself created by importation of file acd_lotusv9.sdf in an ACD/Labs database file (new with version 0.0.4).

The description of the first compound was copied in file firstmolv9.sdf and is provided for a quick inspection of the database content.

The title line in firstmolv9.sdf is Q43656_2, meaning that more data about this compound may be found by searching in Wikidata for Q43656 and that the initial data was given by line 2 in file 220916_frozen_metadata.csv .

Files acd_lotusv9.sdf acd_lotusv9.NMRUDB contain biological taxonomy data from file 220916_frozen_metadata.csv that were not exploited in acd_lotusv7. Sub-files dealing with a particular taxon can be easily produced now.

Chemical shift calculations for 13C nuclei using the HOSE code approach are available here for the compounds in acd_lotusv7.

A continuation of the "Phytochemistry of Australian Plants" database compiled by David Collins and Don McGilvery. Contains chemical structures, references, species names, with persistent identifiers to the literature and Atlas of Living Australia (ALA) for geographical distributions. The current curation effort here adds DOIs/ISBNs/ISSNs for ~80% of references, persistent IDs for all species or genus to the ALA or other datasets, and validated structures (smiles) for ~70% of structures. No new entries have been added since the last update to the original database in 2022. Change log is in the README file.

Data provided here was obtained by the listed authors on linked publications, and these authors may have no association with CSIRO. CSIRO acknowledges that the publications linked here may contain Indigenous Cultural and Intellectual Property (ICIP), including traditional knowledge. CSIRO recognizes that First Nations peoples have the right to control, own and maintain their ICIP in accordance with Article 31 of the United Nations Declaration on the Rights of Indigenous Peoples. Users of this dataset may need to obtain permission from First Nations peoples for use of the information in linked publications. Users intending to collect and use biological specimens containing the compounds described in the dataset may also require permission of First Nations peoples, and may require permits and access permission from landholders. Recognizing that any ICIP in the linked publications is already publicly available but that the publications are not readily accessible by First Nations peoples, CSIRO is committed to finding ways to make the ICIP in these publications more findable and accessible to the First Nations communities from which the knowledge was originally obtained. Users should be aware that because of the historical context of some of the linked publications, they may contain words, descriptions, images or terms which may be culturally sensitive and/or offensive and that reflect authors’ views, or those of the period in which the content was created but may not be considered appropriate today. If First Nations people identify content within this dataset that they consider breaches cultural protocols they are encouraged to contact CSIRO on csiroenquiries@csiro.au or +61 3 9545 2176 to request its removal from the dataset. Please note that while CSIRO is able to administer the data housed within this dataset, this control does not extend to the associated publications. Requests to remove publications should be directed to the associated publishing company. Lineage: Original data extracted in 2022 from https://fms05.filemakerstudio.com.au/fmi/webd?homeurl=http://www.monash.edu/#PhytoChem by kind permission of David Collins and Don McGilvery.

Natural products and semi-synthetic compounds continue to be a significant source of drug candidates for a broad range of diseases, including the current pandemic caused by COVID-19. Besides being attractive sources of bioactive compounds for further development or optimization, natural products are excellent candidates of unique substructures for fragment-based drug discovery inspired on natural products. To this end, fragment libraries are required that can be incorporated into automated drug design pipelines. However, it is still scarce to have public fragment libraries based on extensive collections of natural products. Herein we report the generation and analysis of a fragment library of natural products derived from a database with more than 400,000 compounds. We also report fragment libraries of food chemical databases and other compound data sets of interest in drug discovery, including compound libraries relevant for COVID-19 drug discovery. The fragment libraries were characterized in terms of contents and diversity.Sopporting information contains: COCONUT_COMPOUNDS.csv, FooDB_COMPOUNDS.csv, DCM_COMPOUNDS.csv, CAS_COMPOUNDS.csv, 3CLP_COMPOUNDS.csv. All datasets contain the curated structures and the following information: identicator number (ID), simplified molecular input line entry system (Smiles), Average Molecular Weight (AMW), number of carbons, oxygens, nitrogens, heavy atoms, aliphatic rings, aromatic rings, heterocycles, bridgehead atoms, fraction of sp3 carbon atoms and chiral carbons, and a list of fragments generated from each compound. FRAGMENTS_COCONUT.csv, FRAGMENTS_FooDB.csv, FRAGMENTS_DCM.csv, FRAGMENTS_CAS.csv, FRAGMENTS_3CLP.csv. All libraries contain structures generated (Fragments) from each compound library (Dataset) and the following information: number of compounds that contain that fragment in a dataset (Count) and fraction of them (Proportion), average Molecular Weight (AMW), number of carbons, oxygens, nitrogens, heavy atoms, aliphatic rings, aromatic rings, heterocycles, bridgehead atoms, fraction of sp3 carbon atoms and chiral carbons.

The discovery of novel and/or new bioactive natural products from biota sources is often confounded by the reisolation of known natural products. Dereplication strategies that involve the analysis of NMR and MS spectroscopic data to infer structural features present in purified natural products in combination with database searches of these substructures provide an efficient method to rapidly identify known natural products. Unfortunately this strategy has been hampered by the lack of publically available and comprehensive natural product databases and open source cheminformatics tools. A new platform, DEREP-NP, has been developed to help solve this problem. DEREP-NP uses the open source cheminformatics program DataWarrior to generate a database containing counts of 65 structural fragments present in 229 358 natural product structures derived from plants, animals, and microorganisms, published before 2013 and freely available in the nonproprietary Universal Natural Products Database (UNPD). By counting the number of times one or more of these structural features occurs in an unknown compound, as deduced from the analysis of its NMR (1H, HSQC, and/or HMBC) and/or MS data, matching structures carrying the same numeric combination of searched structural features can be retrieved from the database. Confirmation that the matching structure is the same compound can then be verified through literature comparison of spectroscopic data. This methodology can be applied to both purified natural products and fractions containing a small number of individual compounds that are often generated as screening libraries. The utility of DEREP-NP has been verified through the analysis of spectra derived from compounds (and fractions containing two or three compounds) isolated from plant, marine invertebrate, and fungal sources. DEREP-NP is freely available at https://github.com/clzani/DEREP-NP and will help to streamline the natural product discovery process.

An In Silico spectral DataBase (ISDB) of natural products calculated from structures aggregated in the frame of the LOTUS Initiative (). Fragmented using cfm-predict 4 () . In silico spectral database preparation and use for dereplication initially described in Integration of Molecular Networking and In-Silico MS/MS Fragmentation for Natural Products Dereplication See for associated building scripts. See for associated matching scripts. The pickle formated ISDBs are build for quicker loading via matchms.

Natural product in the COCONUT database with details of source organisms, geolocations and citations.

Computer-aided drug design (CADD) often involves virtual screening (VS) of large compound datasets and the availability of such is vital for drug discovery protocols. We assess the bioactivity and “drug-likeness” of a relatively small but structurally diverse dataset (containing >1,000 compounds) from African medicinal plants, which have been tested and proven a wide range of biological activities. The geographical regions of collection of the medicinal plants cover the entire continent of Africa, based on data from literature sources and information from traditional healers. For each isolated compound, the three dimensional (3D) structure has been used to calculate physico-chemical properties used in the prediction of oral bioavailability on the basis of Lipinski’s “Rule of Five”. A comparative analysis has been carried out with the “drug-like”, “lead-like”, and “fragment-like” subsets, as well as with the Dictionary of Natural Products. A diversity analysis has been carried out in comparison with the ChemBridge diverse database. Furthermore, descriptors related to absorption, distribution, metabolism, excretion and toxicity (ADMET) have been used to predict the pharmacokinetic profile of the compounds within the dataset. Our results prove that drug discovery, beginning with natural products from the African flora, could be highly promising. The 3D structures are available and could be useful for virtual screening and natural product lead generation programs.

Database for integrating species source of natural products & connecting natural products to biological targets via experimental-derived quantitative activity data.

Natural product in the COCONUT database with details of source organisms, geolocations and citations.

A compilation of different molecule databases ready to be used in HERMES. We have compiled different open-access DBs and adapted their format to the HERMES requisite columns. Since all databases share the "Name" and "MolecularFormula" columns, merges between databases can be easily generated.

More databases and merges will be added in the future. If you have any suggestions or want to contribute, feel free to contact us!

All rights reserved to the original authors of the databases.

Description of the files:

• ECMDB.csv: Entries from E. coli Metabolome Database. 3760 compounds.
• Merge_KEGG_ECMDB.csv: a merge between all metabolites from KEGG pathways associated to E.coli K12 with the ECMDB.csv from above. 6107 compounds.
• Merge_LipidMaps_LipidBlast.csv: a merge between lipid entities from LipidMaps LMSD and the metadata (just Name and Molecular Formula) of LipidBlast entries. 163453 compounds.
• norman.xls: Entries from NORMAN SusDat, containing common and emerging drugs, pollutants, etc. 52019 compounds.
• PubChemLite_31Oct2020.csv Adapted column names from https://zenodo.org/record/4183801. 371,663 compounds related to exposomics.
• MS1_2ID.csv. Merge of HMDB, ChEBI and NORMAN compounds. 183911 compounds related to Human Metabolism, drugs, etc..
• COCONUT_NP.csv: parsed collection of entries from the COlleCtion of Open Natural ProdUcTs (COCONUT).406752 compounds.
• DiTriPeptides.csv: a list of all theoretically possible dipeptides (400) and tripeptides (8000) and their associated molecular formulas. 8400 compounds.

In support of nutrition research, concentrations of compounds from different parts of the watermelon plant are provided. The parts of the plant for which data are tabulated include (red) flesh, heart tissue, juice, seed, rind, peel, yellow flesh, seedling, leaf, root, other parts of the plant, and detected but plant part undeclared. The collected data include the low value in the range, the high value in the range, deviation from those values, and units (assumed to be fresh or wet weight unless noted). This table also provides for all compounds the citations to the literature and database sources. The “AFC” identifier represents the Agricultural Research Service (ARS) Food Compound; PubChem refers to the identifier from this resource of chemical compounds. Resources in this dataset:Resource Title: Catalog of natural products occurring in watermelon. File Name: Watermelon_NP_catalog_20210623.tsvResource Description: This is a table of chemical compounds found in watermelonResource Title: Data dictionary. File Name: Data_dictionary_Watermelon_compounds_NAL_20210623.xlsxResource Description: This is the data dictionaryResource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/access

This is a COCONUT dump in the form of CSV format, converted from the SDF file available on the COCONUT website.

BackgroundComplex diseases seriously threaten human health. Drug discovery approaches based on “single genes, single drugs, and single targets” are limited in targeting complex diseases. The development of new multicomponent drugs for complex diseases is imperative, and the establishment of a suitable solution for drug group-target protein network analysis is a key scientific problem that must be addressed. Herbal medicines have formed the basis of sophisticated systems of traditional medicine and have given rise to some key drugs that remain in use today. The search for new molecules is currently taking a different route, whereby scientific principles of ethnobotany and ethnopharmacognosy are being used by chemists in the discovery of different sources and classes of compounds.ResultsIn this study, we developed TarNet, a manually curated database and platform of traditional medicinal plants with natural compounds that includes potential bio-target information. We gathered information on proteins that are related to or affected by medicinal plant ingredients and data on protein–protein interactions (PPIs). TarNet includes in-depth information on both plant–compound–protein relationships and PPIs. Additionally, TarNet can provide researchers with network construction analyses of biological pathways and protein–protein interactions (PPIs) associated with specific diseases. Researchers can upload a gene or protein list mapped to our PPI database that has been manually curated to generate relevant networks. Multiple functions are accessible for network topological calculations, subnetwork analyses, pathway analyses, and compound–protein relationships.ConclusionsTarNet will serve as a useful analytical tool that will provide information on medicinal plant compound-affected proteins (potential targets) and system-level analyses for systems biology and network pharmacology researchers. TarNet is freely available at http://www.herbbol.org:8001/tarnet, and detailed tutorials on the program are also available.

COCONUT is a COlleCtion of Open NatUral producTs. The database is now available at coconut.naturalproducts.net, where the latest updates will appear before being available here. To assemble COCONUT, data from 55 open access collections and databases of natural products was retrieved and curated. This archive contains two files: The MongoDB dump, the most complete version of the dataset, with extensive molecular annotations The COCONUT4MetFrag file, used for MetFrag. The last version of COCONUT4MetFrag is in the file "COCONUT4MetFrag_april.csv" The COCONUT.sdf file containing all unique NP molecules with selected metadata To restore the dataset in MongoDB: unzip COCONUT_2021_03.zip cd COCONUT_2021_03/COCONUT_2021_03/ mongorestore --db=COCONUT --noIndexRestore . It is generally useful to avoid restoring indexes, as they can interfere with the local installation. Here are the commands to rebuild indexes: mongo use COCONUT db.sourceNaturalProduct.createIndex( {source:1}) db.sourceNaturalProduct.createIndex( {simpleInchi:"hashed"}) db.sourceNaturalProduct.createIndex( {simpleInchiKey:1}) db.sourceNaturalProduct.createIndex( {originalInchiKey:1}) db.sourceNaturalProduct.createIndex( {originalSmiles:"hashed"}) db.sourceNaturalProduct.createIndex( {absoluteSmiles:"hashed"}) db.sourceNaturalProduct.createIndex( {idInSource:1}) db.uniqueNaturalProduct.createIndex( {inchi:"hashed"}) db.uniqueNaturalProduct.createIndex( {inchikey:1}) db.uniqueNaturalProduct.createIndex( {clean_smiles: "hashed"}) db.uniqueNaturalProduct.createIndex( {molecular_formula:1}) db.uniqueNaturalProduct.createIndex( {name:1}) db.uniqueNaturalProduct.createIndex( {coconut_id:1}) db.uniqueNaturalProduct.createIndex( {fragmentsWithSugar:"hashed"}) db.uniqueNaturalProduct.createIndex( {fragments:"hashed"}) db.fragment.createIndex({signature:1}) db.fragment.createIndex({signature:1, withsugar:-1}) db.sourceNaturalProduct.createIndex( {source:1}) db.sourceNaturalProduct.createIndex( {simpleInchi:"hashed"}) db.sourceNaturalProduct.createIndex( {simpleInchiKey:1}) db.sourceNaturalProduct.createIndex( {originalInchiKey:1}) db.sourceNaturalProduct.createIndex( {originalSmiles:"hashed"}) db.sourceNaturalProduct.createIndex( {absoluteSmiles:"hashed"}) db.sourceNaturalProduct.createIndex( {idInSource:1}) db.uniqueNaturalProduct.createIndex( {inchi:"hashed"}) db.uniqueNaturalProduct.createIndex( {inchikey:1}) db.uniqueNaturalProduct.createIndex( {clean_smiles: "hashed"}) db.uniqueNaturalProduct.createIndex( {molecular_formula:1}) db.uniqueNaturalProduct.createIndex( {name:1}) db.uniqueNaturalProduct.createIndex( {coconut_id:1}) db.uniqueNaturalProduct.createIndex( {fragmentsWithSugar:"hashed"}) db.uniqueNaturalProduct.createIndex( {fragments:"hashed"}) db.fragment.createIndex({signature:1}) db.fragment.createIndex({signature:1, withsugar:-1}) This version of COCONUT is beta and will be curated further, but can already be used as it is. {"references": ["https://jcheminf.biomedcentral.com/articles/10.1186/s13321-020-00424-9"]}

Natural products derived from bacteria play a pivotal role in drug discovery and utilization. In the past few years, there has been significant interest in pharmaceutical research on the anti-cancer activities of bacteria anti-cancer metabolites (BAMs). However, there are only a few open access databases dedicated to BAMs research. To meet the increasing need for mining and sharing for BAMs-related data resources, this study introduces Microbiota-derived anti-cancer natural products database (MANP), a comprehensive database designed for bacteria anti-cancer natural products based on manually curated data. Currently, MANP contains more than 1000 secondary metabolites with anti-cancer activities, systematic taxonomy and geographical distribution of source bacteria, detailed structure characterization, physico-chemical properties, ADMET information, experimental biological activity data, and accessible literature citations. MANP is an integrated platform for the investigation of bacteria secondary metabolites, discovery of lead compounds, and mining data for structure-activity relationships. Researchers can utilize MANP to explore potential anti-cancer agents derived from bacteria and facilitate the development of novel therapeutic interventions. By providing a comprehensive database of bacterial secondary metabolites, MANP database offers a valuable tool for drug discovery and development in the fight against cancer.

Natural product in the COCONUT database with details of source organisms, geolocations and citations.

Natural product in the COCONUT database with details of source organisms, geolocations and citations.

Additional file 1. Chemical compounds of the Natural Compound Databases (NCDBs).

Natural product in the COCONUT database with details of source organisms, geolocations and citations.