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.

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.

Curated database of natural products and their corresponding kingdom classification (e.g. GBIF Backbone Taxonomy, Catalogue of Life) from the LOTUS database (https://lotus.naturalproducts.net/). df_curated.csv data for Github Repository at https://github.com/SIBERanalytics/NPTaxonomy/

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.

A chunk of the opensource Coconut Natural Product Database I found at: https://coconut.naturalproducts.net/

Exploring natural product chemical space loosely based around drug-like properties (more lipophilic and larger end of spectrum). Had a bit of trouble finding csv files (not sdf) for chemical compound collections including basic physiochemical descriptors so thought I'd add one! I'm working on mapping the spread of this space based on common descriptors.

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.

NPASS Natural Compounds Bioactivity Dataset

This dataset is extracted from the NPASS database and provides a clean collection of natural compounds with their chemical representations and bioactivity values. It includes:

• np_id: Compound identifier
• SMILES: Chemical structure representation (for model training)
• InChI and InChIKey: Optional chemical identifiers for verification
• activity_value: Bioactivity measurement (can be used for QSAR or other predictive modeling)

This dataset is ready for training deep learning models, such as Transformers, to learn chemical compound representations and explore bioactivity relationships.

Source: NPASS database – Natural Product Activity & Species Source Database

Suitable for researchers in computational chemistry, drug discovery, or any project focusing on chemical compound modeling.

COCONUT (Collection of Open Natural Products) Online

COlleCtion of Open NatUral producTs (COCONUT) is an aggregated dataset comprising elucidated and predicted natural products (NPs) from open repositories. It offers a user-friendly web interface for browsing, searching, and efficiently downloading NPs. The latest database integrates more than 63 open NP resources, providing unrestricted access to data free of charge. Each entry in the database represents a "flat" NP structure, accompanied by information on its known stereochemical forms, relevant literature, producing organisms, natural geographical distribution, and precomputed molecular properties.

Natural products are small bioactive molecules produced by living organisms with potential applications in pharmacology and various industries. The significance of these compounds has driven global interest in NP research across diverse fields. However, despite the growing number of general and specialized NP databases, no comprehensive online resource has consolidated all known NPs in one place—until COCONUT. This became a resource facilitating NP research, enabling computational screening and other in-silico applications.

Summary of the COCONUT database's statistics:

COCONUT was meticulously assembled from a range of public databases and primary sources, including:

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.

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

Natural products (NPs) have long been the cornerstone of drug discovery. Halogenated organic NPs are limited, while around one-fourth of approved chemical drugs are organohalogens. This suggests that the introduction of halogens into NPs may enhance their potential for transformation into drugs. In this study, we utilized a matched molecular pair (MMP) approach alongside a database survey to investigate the impact of halogenation on this transformation. The study revealed that halogenation increased the bioactivity of 70.3% of NPs, with 50.3% exhibiting at least a 2-fold enhancement. Halogen bonds (XBs) are prevalent between organohalogens and their targets. To explore whether halogenated NPs could form XBs with their targets, computational studies were performed and demonstrated that halogenated NPs or NP-derived drugs formed strong XBs with their targets, resulting in improved binding affinities. This study highlights the considerable potential of introducing halogens into NPs as a strategic approach for enhancing bioactivity and facilitating the development of drugs.

Additional file 2. Physicochemical properties and cell-based anti-inflammatory activity of InflamNat Compounds.

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.

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.

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.