This section is structured as follows: first, we present an overview of the LOTUS initiative at its current stage of development. The central curation and dissemination elements of the LOTUS initiative are then explained in detail. The third section addresses the interaction modes between end-users and LOTUS, including data retrieval, addition, and editing. Some examples on how LOTUS data can be used to answer research questions or develop hypothesis are given. The final section is dedicated to the interpretation of LOTUS data and illustrates the dimensions and qualities of the current LOTUS dataset from chemical and biological perspectives.

Blueprint of the LOTUS initiative

Building on the standards established by the related WikiProjects on Wikidata (Chemistry, Taxonomy and Source Metadata), a NP chemistry-oriented subproject was created (Chemistry/Natural products). Its central data consists of three minimal sufficient objects:

• A chemical structure object, with associated Simplified Molecular Input Line Entry System (SMILES) (Weininger, 1988), International Chemical Identifier (InChI) (Heller et al., 2013) and InChIKey (a hashed version of the InChI).

• A biological organism object, with associated taxon name, the taxonomic DB where it was described and the taxon ID in the respective DB.

• A reference object describing the structure-organism pair, with the associated article title and a Digital Object Identifier (DOI), a PubMed (PMID), or PubMed Central (PMCID) ID.

As data formats are largely inhomogeneous among existing electronic NP resources, fields related to chemical structure, biological organism and references are variable and essentially not standardized. Therefore, LOTUS implements multiple stages of harmonization, processing, and validation (Figure 1, stages 1–3). LOTUS employs a Single Source of Truth (SSOT, Single_source_of_truth) to ensure data reliability and continuous availability of the latest curated version of LOTUS data in both Wikidata and LNPN (Figure 1, stage 4). The SSOT approach consists of a PostgreSQL DB that structures links and data schemes such that every data element has a single place. The LOTUS processing pipeline is tailored to efficiently include and diffuse novel or curated data directly from new sources or at the Wikidata level. This iterative workflow relies both on data addition and retrieval actions as described in the Data Interaction section. The overall process leading to referenced and curated structure-organisms pairs is illustrated in Figure 1 and detailed hereafter.

Figure 1

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By design, this iterative process fosters community participation, essential to efficiently document NP occurrences. All stages of the workflow are described on the git sites of the LOTUS initiative at https://github.com/lotusnprod and in the methods. At the time of writing, 750,000+ LOTUS entries contained a curated chemical structure, biological organism and reference and were available on both Wikidata and LNPN. As the LOTUS data volume is expected to increase over time, a frozen (as of 2021-12-20), tabular version of this dataset with its associated metadata is made available at https://doi.org/10.5281/zenodo.5794106 (Rutz et al., 2021a).

Data processing and validation

As shown in Figure 1, data curation consisted of three stages: harmonization, processing, and validation. Thereby, after the harmonization stage, each of the three central objects – chemical compounds, biological organisms, and reference – were processed, as described in related methods section. Given the data size (2.5M+ initial entries), manual validation was unfeasible. Curating the references was a particularly challenging part of the process. Whereas organisms are typically reported by at least their vernacular or scientific denomination and chemical structures via their SMILES, InChI, InChIKey or image (not covered in this work), references suffer from largely insufficient reporting standards. Despite poor standardization of the initial reference field, proper referencing remains an indispensable way to establish the validity of structure-organism pairs. Better reporting practices, supported by tools such as Scholia (Blomqvist et al., 2017; Rasberry et al., 2019) and relying on Wikidata, Fatcat, or Semantic Scholar should improve reference-related information retrieval in the future.

In addition to curating the entries during data processing, 420 referenced structure-organism pairs were selected for manual validation. An entry was considered as valid if: (i) the structure (in the form of any structural descriptor that could be linked to the final sanitized InChIKey) was described in the reference (ii) the containing organism (as any organism descriptor that could be linked to the accepted canonical name) was described in the reference and (iii) the reference was describing the occurrence of the chemical structure in the biological organism. More details are available in the related methods section. This process allowed us to establish rules for automatic filtering and validation of the entries. The parameters of the automatic filtering are available as a function (filter_dirty.R) and are further described in the related methods section. The automatic filtering was then applied to all entries. To confirm the efficacy of the filtering process, a new subset of 100 diverse, automatically curated and automatically validated entries was manually checked, yielding a rate of 97% of true positives. The detailed results of the two manual validation steps are reported in Appendix 2. The resulting data is also available in the dataset shared at https://doi.org/10.5281/zenodo.5794106 (Rutz et al., 2021a). Table 1 shows an example of a referenced structure-organism pair before and after curation. This process resolved the structure to an InChIKey, the organism to a valid taxonomic name and the reference to a DOI, thereby completing the establishment of the essential referenced structure-organism pair.

Table 1

	Structure	Organism	Reference
Before curation	Cyathocaline	Stem bark of Cyathocalyx zeylanica CHAMP. ex HOOK. f. & THOMS. (Annonaceae)	Wijeratne E. M. K., de Silva L. B., Kikuchi T., Tezuka Y., Gunatilaka A. A. L., Kingston D. G. I., J. Nat. Prod., 58, 459–462 (1995).
After curation	VFIIVOHWCNHINZ-UHFFFAOYSA-N	Cyathocalyx zeylanicus	10.1021 /NP50117A020

Challenging examples encountered during the development of the curation process were compiled in an edge cases table (tests/tests.tsv) to allow for automated unit testing. These tests allow a continuous revalidation of any change made to the code, ensuring that corrected errors will not reappear. The alluvial plot in Figure 2 illustrates the individual contribution of each source and original subcategory that led to the processed categories: structure, organism, and reference.

Figure 2

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The figure highlights, for example, the essential contribution of the reference DOI category to the final validated entries. A similar pattern can be seen concerning structures, where the validation rate of structural identifiers is higher than chemical names. The combination of the results of the automated curation pipeline and the manually curated entries led to the establishment of four categories (manually validated, manually rejected, automatically validated and automatically rejected) of the referenced structure-organism pairs that formed the processed part of the SSOT. Out of a total of 2.5M+ initial pairs, the manual and automatic validation retained 750,000+ pairs (approximately 30%), which were then selected for dissemination on Wikidata. Among validated entries, multiple ones were redundant among the source databases, thus also explaining the decrease of entries between the initial pairs and validated ones. Moreover, because data quality was favored over quantity, the number of rejected entries is high. Among them, multiple correct entries were certainly falsely rejected, but still not disseminated. All rejected entries were kept aside for later manual inspection and validation. These are publicly available at https://doi.org/10.5281/zenodo.5794597 (Rutz et al., 2021b). In the end, the disseminated data contained 290,000+ unique chemical structures, 40,000+ distinct organisms and 75,000+ references.

Wikidata

The easiest way to search for NP occurrence information in Wikidata is by typing the name of a chemical structure directly into the ‘Search Wikidata’ field, which (for left-to-right languages) can be found in the upper right corner of the Wikidata homepage or any other Wikidata page. For example, by typing ‘erysodine’, the user will land on the page of this compound (Q27265641). Scrolling down to the ‘found in taxon’ statement will allow the user to view the biological organisms reported to contain this NP (Figure 3). Clicking the reference link under each taxon name links to the publication(s) documenting the occurrence.

Figure 3

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The typical approach to more elaborated querying involves writing SPARQL queries using the Wikidata Query Service or another direct connection to a SPARQL endpoint. Table 2 contains some examples from simple to more elaborated queries, demonstrating what can be done using this approach. The full-text queries with explanations are included in Supplementary file 1.

Table 2

Question	Wikidata SPARQL query
What are the compounds present in Mouse-ear cress (Arabidopsis thaliana) or its child taxa?	https://w.wiki/4Vcv
Which organisms are known to contain β-sitosterol?	https://w.wiki/4VFn
Which organisms are known to contain stereoisomers of β-sitosterol?	https://w.wiki/4VFq
Which pigments are found in which taxa, according to which reference?	https://w.wiki/4VFx
What are examples of organisms where compounds were found in an organism sharing the same parent taxon, but not in the organism itself?	https://w.wiki/4Wt3
Which Zephyranthes species lack compounds known from at least two species in the genus?	https://w.wiki/4VG3
How many compounds are structurally similar to compounds labeled as antibiotics? (grouped by the parent taxon of the containing organism)	https://w.wiki/4VG4
Which organisms contain indolic scaffolds? Count occurrences, group and order the results by the parent taxon.	https://w.wiki/4VG9
Which compounds with known bioactivities were isolated from Actinobacteria, between 2014 and 2019, with related organisms and references?	https://w.wiki/4VGC
Which compounds labeled as terpenoids were found in Aspergillus species, between 2010 and 2020, with related references?	https://w.wiki/4VGD
Which are the available referenced structure-organism pairs on Wikidata? (example limited to 1000 results)	https://w.wiki/4VFh

The queries presented in Table 2 are only selected examples, and many other ways of interrogating LOTUS can be formulated. Generic queries can be used, for example, for hypothesis generation when starting a research project. For instance, a generic SPARQL query - listed in Table 2 as “Which are the available referenced structure-organism pairs on Wikidata?” - retrieves all structures, identified by their InChIKey (P235), which contain ‘found in taxon’ (P703) statements that are stated in (P248) a bibliographic reference: https://w.wiki/4VFh. Data can then be exported in various formats, such as classical tabular formats, json, or html tables (see Download tab on the lower right of the query frame). At the time of writing (2021-12-20), this query (without the LIMIT 1000) returned 951,800 entries; a frozen query result is available at https://doi.org/10.5281/zenodo.5668854 (Rutz et al., 2021d).

Targeted queries allowing to interrogate LOTUS data from the perspective of one of the three objects forming the referenced structure-organism pairs can also be built. Users can, for example, retrieve a list of all structures reported from a given organism, such as all structures reported from Arabidopsis thaliana (Q158695) or its child taxa (https://w.wiki/4Vcv). Alternatively, all organisms containing a given chemical can be queried via its structure, such as in the search for all organisms where β-sitosterol (Q121802) was found in (https://w.wiki/4VFn). For programmatic access, the lotus-wikidata-exporter repository also allows data retrieval in RDF format and as TSV tables.

To further showcase the possibilities, two additional queries were established (https://w.wiki/4VGC and https://w.wiki/4VGD). Both queries were inspired by recent literature review works (Jose et al., 2021; Zhao et al., 2022). The first work describes compounds found in Actinobacteria, with a biological focus on compounds with reported bioactivity. The second one describes compounds found in Aspergillus spp., with a chemical focus on terpenoids. In both cases, in seconds, the queries allow retrieving a table similar to the ones in the mentioned literature reviews. While these queries are not a direct substitute for manual literature review, they do allow researchers to quickly begin such a review process with a very strong body of relevant references.

For a convenient expansion or limitation of the results, certain types of queries such as structure or similarity searches usually exist in molecular electronic resources. As these queries are not natively integrated by SPARQL, they are not readily available for Wikidata exploration. To address such limitation, Galgonek et al. developed an in-house SPARQL engine that allows utilization of Sachem, a high-performance chemical DB cartridge for PostgreSQL for fingerprint-guided substructure and similarity search (Kratochvíl et al., 2018). The engine is used by the Integrated Database of Small Molecules (IDSM) that operates, among other things, several dedicated endpoints allowing structural search in selected small-molecule datasets via SPARQL (Kratochvíl et al., 2019). To allow substructure and similarity searches via SPARQL also on compounds from Wikidata, a dedicated IDSM/Sachem endpoint was created for the LOTUS project. The endpoint indexes isomeric (P2017) and canonical (P233) SMILES code available in Wikidata. To ensure that data is kept up-to-date, SMILES codes are automatically downloaded from Wikidata daily. The endpoint allows users to run federated queries and, thereby, proceed to structure-oriented searches on the LOTUS data hosted at Wikidata. For example, the SPARQL query https://w.wiki/4VG9 returns a list of all organisms that produce NP with an indolic scaffold. The output is aggregated at the parent taxa level of the containing organisms and ranked by the number of scaffold occurrences.

Regarding the versioning aspects, some challenges are implied by the dynamic nature of the Wikidata environment. However, tracking of the data evolution can be achieved in multiple ways and at different levels: at the full Wikidata level, dumps are regularly created (https://dumps.wikimedia.org/wikidatawiki/entities) while at the individual entry level the full history of modification can be consulted (see following link for the full edit history of erythromycin for example (https://www.wikidata.org/w/index.php?title=Q213511&action=history)).

We propose to the users a simple approach to document, version and share the output of queries on the LOTUS data at a defined time point. For this, in addition to sharing the short url of a the SPARQL query (which will return results evolving over time), a simple archiving of the returned table to Zenodo or similar platform can be done. In order to gather results of SPARQL queries, we established the LOTUS Initiative Community repository. The following link allows to directly contribute to the community repository https://zenodo.org/deposit/new?c=the-lotus-initiative. For example, the output of this Wikidata SPARQL query https://w.wiki/4N8G realized on the 2021-11-10T16:56 can be easily archived and shared in a publication via its DOI 10.5281/zenodo.5668380.

Distributions of organisms per structure and structures per organism

Readily achievable outcomes from LOTUS show that the depth of exploration of the world of NP is rather limited: as depicted in Figure 4, on average, three organisms are reported per chemical structure and eleven structures per organism. Notably, half of all structures have been reported from a single organism and half of all studied organisms are reported to contain five or fewer structures. Metabolomics studies suggest that these numbers are heavily underrated (Noteborn et al., 2000; Wang et al., 2020) and indicate that a better reporting of the metabolites detected in the course of NP chemistry investigations should greatly improve coverage.

This incomplete coverage may be partially explained by the habit in classical NP journals to accept only new and/or bioactive chemical structures for publication. Another possible explanation is the fact that specific chemical classes have been under heavier scrutiny by the natural products community than others. For example, alkaloids have three specific characteristics which favor their reporting in the literature. First, they are often endowed with potent biological activities making them a target in the frame of pharmacognosy research. Second, their chemical nature makes them readily accessible from complex biological matrices through acido-basic extraction. Third, they ionize greatly in positive MS mode, which makes their detection even at a very low concentration possible, where other compounds present in much higher concentrations are not detected. It is thus a complex task to answer the following question: “Is the currently observed repartition of alkaloids across the tree of life a reflection of their true biological occurrence or is this repartition biased by the aforementioned characteristics of this chemical class?” While the LOTUS initiative does not allow yet disentangling the bias from the true occurrence, it should offer sound and strong foundations for such challenging research problematic.

Another obvious explanation for the limited coverage (see Figure 4) is the fact that most of the chemical structures in LOTUS have been physically isolated and described. This is an extremely time-consuming effort that can obviously not be carried on all metabolites of all biological organisms. Here, the sensitivity of mass spectrometry and the ever-increasing efficiency of computational metabolite annotation solutions could offer a strong take. The documentation of metabolite annotation results obtained on large collections of biological matrices and the associated metadata within knowledge graphs offers exciting perspectives regarding the possibilities to expand both the chemical and biological coverage of the LOTUS data in a feasible manner.

Figure 4

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Contribution of individual electronic NP resources to LOTUS

The added value of the LOTUS initiative to assemble multiple electronic NP resources is illustrated in Figure 5: Panel A shows the contributions of the individual electronic NP resources to the ensemble of chemical structures found in one of the most studied vascular plants, Arabidopsis thaliana (“Mouse-ear cress”; Q147096). Panel B shows the ensemble of taxa reported to contain the planar structure of the widely occurring triterpenoid β-sitosterol (Q121802).

Figure 5

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Figure 5A also shows that according to NPClassifier, the ‘chemical pathway’ category distribution across electronic NP resources is not conserved. Note that NPClassifier and ClassyFire (Djoumbou Feunang et al., 2016) chemical classification results are both available as metadata in the frozen LOTUS export and LNPN. Both classification tools return a chemical taxonomy for individual structures, thus allowing their grouping at higher hierarchical levels, in the same way as it is done for biological taxonomies. The UpSet plot in Figure 5 indicates the poor overlap of preexisting electronic NP resources and the added value of an aggregated dataset. This is particularly well illustrated in Figure 5B., where the number of organisms for which the planar structure of β-sitosterol (KZJWDPNRJALLNS) has been reported is shown for each intersection. NAPRALERT has by far the highest number of entries (2330 in total), while other electronic NP resources complement this well: e.g. UNPD has 573 reported organisms with β-sitosterol that do not overlap with any other resource. Of note, β-sitosterol is documented in only 13 organisms in the DNP, highlighting the importance of a better systematic reporting of ubiquitous metabolites and the interest of multiple data sources agglomeration.

A biologically interpreted chemical tree

The chemical diversity captured in LOTUS is here displayed using TMAP (Figure 6), a visualization library allowing the structural organization of large chemical datasets as a minimum spanning tree (Probst and Reymond, 2020). Using Faerun, an interactive HTML file is generated to display metadata and molecule structures by embedding the SmilesDrawer library (Probst and Reymond, 2018a; Probst and Reymond, 2018b). Planar structures were used for all compounds to generate the TMAP (chemical space tree-map) using MAP4 encoding (Capecchi et al., 2020). As the tree organizes structures according to their molecular fingerprint, an anticipated coherence between the clustering of compounds and the mapped NPClassifier chemical class is observed (Figure 6A.). For clarity, some of the most represented chemical classes of LOTUS plus quassinoids and stigmastane steroids are mapped, with examples of a quassinoid (NXZXPYYKGQCDRO) (light green star) and a stigmastane steroid (KZJWDPNRJALLNS) (dark green diamond) and their corresponding location in the TMAP.

Figure 6

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To explore relationships between chemistry and biology, it is possible to map taxonomical information such as the most reported biological family per chemical compound (Figure 6B.) or the biological specificity of chemical classes (Figure 6C.) on the TMAP. The biological specificity score at a given taxonomic level for a given chemical class is calculated as a Jensen-Shannon divergence. A score of 1 suggests that compounds are highly specific, 0 that they are ubiquitous. For more details, see 3_analyzing/jensen_shannon_divergence.R. This visualization allows to highlight chemical classes specific to a given taxon, such as the quassinoids in the Simaroubaceae family. In this case, it is striking to see how well the compounds of a given chemical class (quassinoids) (Figure 6A.) and the most reported plant family per compound (Simaroubaceae) (Figure 6B.) overlap. This is also evidenced on Figure 6C. with a Jensen-Shannon divergence of 0.99 at the biological family level for quassinoids. In this plot, it is also possible to identify chemical classes that are widely spread among living organisms, such as the stigmastane steroids, which exhibit a Jensen-Shannon divergence of 0.73 at the biological family level. This means that repartition of stigmastane steroids among families is not specific. Figure 7—figure supplement 1 further supports this statement.

A chemically interpreted biological tree

An alternative view of the biological and chemical diversity covered by LOTUS is illustrated in Figure 7. Here, chemical compounds are not organized but biological organisms are placed in their taxonomy. To limit bias due to under-reporting in the literature and keep a reasonable display size, only families with at least 50 reported compounds were included. Organisms were classified according to the OTL taxonomy and structures according to NPClassifier. The tips were labeled according to the biological family and colored according to their biological kingdom. The bars represent structure specificity of the most characteristic chemical class of the given biological family (the higher the more specific). This specificity score was a Jaccard index between the chemical class and the biological family. For more details, see 4_visualizing/plot_magicTree.R.

Figure 7 with 1 supplement see all

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Figure 7 makes it possible to spot highly specific compound classes such as trinervitane terpenoids in the Termitidae, the rhizoxin macrolides in the Rhizopodaceae, or the quassinoids and limonoids typical, respectively, of Simaroubaceae and Meliaceae. Similarly, tendencies of more generic occurrence of NP can be observed. For example, within the fungal kingdom, Basidiomycotina appear to have a higher biosynthetic specificity toward terpenoids than other fungi, which mostly focus on polyketides production. As explained previously, Figure 7 is highly dependent of the data reported in literature. As also illustrated in Figure 4, some compounds can be over-studied among several organisms, and many organisms studied for specific compounds only. This is a direct consequence of the way the NP community reports its data actually. Having this in mind, when observed at a finer scale, down to the structure level, such chemotaxonomic representation can give valuable insights. For example, among all chemical structures, only two were found in all biological kingdoms, namely heptadecanoic acid (KEMQGTRYUADPNZ-UHFFFAOYSA-N) and β-carotene (OENHQHLEOONYIE-JLTXGRSLSA-N). Looking at the distribution of β-sitosterol (KZJWDPNRJALLNS-VJSFXXLFSA-N) within the overall biological tree, Figure 7—figure supplement 1 plots its presence/absence versus those of its superior chemical classifications, namely the stigmastane, steroid and terpenoid derivatives, over the same tree used in Figure 7. The comparison of these five chemically interpreted biological trees clearly highlights the increasing speciation of the β-sitosterol biosynthetic pathway in the Archaeplastida kingdom, while the superior classes are distributed across all kingdoms. Figure 7 is zoomable and vectorized for detailed inspection.

As illustrated, the possibility of data interrogation at multiple precision levels, from fully defined chemical structures to broader chemical classes, is of great interest, for example, for taxonomic and evolution studies. This makes LOTUS a unique resource for the advancement of chemotaxonomy, a discipline pioneered by Augustin Pyramus de Candolle and pursued by other notable researchers (Robert Hegnauer, Otto R. Gottlieb) (Gottlieb, 1982; Hegnauer, 1986a; Candolle, 1816). Six decades after Hegnauer’s publication of ‘Die Chemotaxonomie der Pflanzen’ (Hegnauer, 1986b) much remains to be done for the advancement of this field of study and the LOTUS initiative aims to provide a solid basis for researchers willing to pursue these exciting explorations at the interface of chemistry, biology and evolution.

As shown recently in the context of spectral annotation (Dührkop et al., 2021), lowering the precision level of the annotation allows a broader coverage along with greater confidence. Genetic studies investigating the pathways involved and the organisms carrying the responsible biosynthetic genes would be of interest to confirm the previous observations. These forms of data interpretation exemplify the importance of reporting not only new structures, but also novel occurrences of known structures in organisms as comprehensive chemotaxonomic studies are pivotal for a better understanding of the metabolomes of living organisms.

The integration of multiple knowledge sources, for example genetics for NP producing gene clusters (Kautsar et al., 2020) combined to taxonomies and occurrences DB, also opens new opportunities to understand if an organism is responsible for the biosynthesis of a NP or merely contains it. This understanding is of utmost importance for the chemotaxonomic field and will help to understand to which extent microorganisms (endosymbionts) play a role in host development and its NP expression potential (Saikkonen et al., 2004).

A snapshot of the obtained data at the time of re-submission (2021-12-20) is available at the following Zenodo community: https://zenodo.org/communities/the-lotus-initiative and related records: https://zenodo.org/record/5793224, https://zenodo.org/record/5794107, https://zenodo.org/record/5794597 and https://zenodo.org/record/5801816. The https://lotus.nprod.net website is intended to gather news and features related to the LOTUS initiative in the future.

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Author details

1. Adriano Rutz

   1. School of Pharmaceutical Sciences, University of Geneva, Geneva, Switzerland
   2. Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Geneva, Switzerland

   Contribution

   Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Software, Validation, Visualization, Wikidata, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0443-9902

2. Maria Sorokina

   Institute for Inorganic and Analytical Chemistry, Friedrich-Schiller-University Jena, Jena, Germany

   Contribution

   LNPN Website, Software, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-9359-7149

3. Jakub Galgonek

   Institute of Organic Chemistry and Biochemistry of the CAS, Prague, Czech Republic

   Contribution

   Sachem, IDSM, Software, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-7038-544X

4. Daniel Mietchen

   1. Ronin Institute, Montclair, United States
   2. Leibniz Institute of Freshwater Ecology and Inland Fisheries, Berlin, Germany
   3. School of Data Science, University of Virginia, Charlottesville, United States

   Contribution

   Wikidata, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-9488-1870

5. Egon Willighagen

   Department of Bioinformatics-BiGCaT, Maastricht University, Maastricht, Netherlands

   Contribution

   Wikidata, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7542-0286

6. Arnaud Gaudry

   1. School of Pharmaceutical Sciences, University of Geneva, Geneva, Switzerland
   2. Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Geneva, Switzerland

   Contribution

   Visualization, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3648-7362

7. James G Graham

   1. Center for Natural Product Technologies and WHO Collaborating Centre for Traditional Medicine (WHO CC/TRM), Pharmacognosy Institute; College of Pharmacy, University of Illinois at Chicago, Chicago, United States
   2. Department of Pharmaceutical Sciences, College of Pharmacy, University of Illinois at Chicago, Chicago, United States

   Contribution

   NAPRALERT, Writing – review and editing

   Competing interests

   No competing interests declared

8. Ralf Stephan

   Ontario Institute for Cancer Research (OICR), University Ave Suite, Toronto, Canada

   Contribution

   Wikidata, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-4650-631X

9. Roderic Page

   University of Glasgow, Glasgow, United Kingdom

   Contribution

   Wikidata

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-7101-9767

10. Jiří Vondrášek

    Institute of Organic Chemistry and Biochemistry of the CAS, Prague, Czech Republic

    Contribution

    Funding acquisition, Resources, Sachem, IDSM

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-6066-973X

11. Christoph Steinbeck

    Institute for Inorganic and Analytical Chemistry, Friedrich-Schiller-University Jena, Jena, Germany

    Contribution

    Funding acquisition, LNPN Website, Resources, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-6966-0814

12. Guido F Pauli

    1. Center for Natural Product Technologies and WHO Collaborating Centre for Traditional Medicine (WHO CC/TRM), Pharmacognosy Institute; College of Pharmacy, University of Illinois at Chicago, Chicago, United States
    2. Department of Pharmaceutical Sciences, College of Pharmacy, University of Illinois at Chicago, Chicago, United States

    Contribution

    Funding acquisition, NAPRALERT, Resources, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-1022-4326

13. Jean-Luc Wolfender

    1. School of Pharmaceutical Sciences, University of Geneva, Geneva, Switzerland
    2. Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Geneva, Switzerland

    Contribution

    Funding acquisition, Resources, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-0125-952X

14. Jonathan Bisson

    1. Center for Natural Product Technologies and WHO Collaborating Centre for Traditional Medicine (WHO CC/TRM), Pharmacognosy Institute; College of Pharmacy, University of Illinois at Chicago, Chicago, United States
    2. Department of Pharmaceutical Sciences, College of Pharmacy, University of Illinois at Chicago, Chicago, United States

    Contribution

    Conceptualization, Data curation, Formal analysis, Investigation, Methodology, NAPRALERT, Project administration, Resources, Software, Supervision, Validation, Writing – review and editing

    For correspondence

    bjo@uic.edu

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-1640-9989

15. Pierre-Marie Allard

    1. School of Pharmaceutical Sciences, University of Geneva, Geneva, Switzerland
    2. Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Geneva, Switzerland
    3. Department of Biology, University of Fribourg, Fribourg, Switzerland

    Contribution

    Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Wikidata, Writing – original draft, Writing – review and editing

    For correspondence

    pierre-marie.allard@unifr.ch

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-3389-2191

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