A Sample-Centric and Knowledge-Driven Computational Framework for Natural Products Drug Discovery Article Swipe
Arnaud Gaudry
,
Marco Pagni
,
Florence Mehl
,
Sébastien Moretti
,
Luis-Manuel Quirós-Guerrero
,
Adriano Rutz
,
Marcel Kaiser
,
Laurence Marcourt
,
Emerson Ferreira Queiroz
,
Jean‐Robert Ioset
,
Antonio Grondin
,
Bruno David
,
Jean‐Luc Wolfender
,
Pierre‐Marie Allard
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.26434/chemrxiv-2023-sljbt
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.26434/chemrxiv-2023-sljbt
Modern natural products (NPs) research relies on untargeted liquid chromatography coupled with mass spectrometry metabolomics. Together with cutting-edge processing and computational annotation strategies, such approaches can yield extensive spectral and structural information. However, current processing workflows require feature-alignment steps based on retention time which hinders the comparison of samples originating from different batches or analyzed using different instrumental setups. In addition, there is currently no analytical framework available to efficiently match processed metabolomics data and associated metadata with external resources. To address these limitations, we present a new sample-centric and knowledge-driven framework allowing multi-modal data alignment - e.g. through chemical structures, biological activities, or spectral features - and demonstrate its value in exploring large and chemodiverse natural extract datasets. Here, the experimental data is processed at the sample level, matched with external identifiers where possible, semantically enriched, and integrated into a unified knowledge graph. The use of semantic web technology enables comparison of processed and standardized data, information, and knowledge at the repository scale. We demonstrate the utility of the developed framework, the Experimental Natural Products Knowledge Graph (ENPKG), to leverage the results obtained from screening 1,600 plant extracts against trypanosomatids and streamline the identification of new antiparasitic compounds. Thanks to its versatility, the proposed approach allows for a radically novel exploitation of metabolomics data. Semantic web technologies are a fundamental asset and we anticipate that their adoption will complement the current computational metabolomics pipelines and enable the community to advance in the description of global chemodiversity and drug discovery projects.
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- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.26434/chemrxiv-2023-sljbt
- https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/649ea085ba3e99daef499a00/original/a-sample-centric-and-knowledge-driven-computational-framework-for-natural-products-drug-discovery.pdf
- OA Status
- gold
- Cited By
- 7
- References
- 75
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4382981645
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4382981645Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.26434/chemrxiv-2023-sljbtDigital Object Identifier
- Title
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A Sample-Centric and Knowledge-Driven Computational Framework for Natural Products Drug DiscoveryWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-07-03Full publication date if available
- Authors
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Arnaud Gaudry, Marco Pagni, Florence Mehl, Sébastien Moretti, Luis-Manuel Quirós-Guerrero, Adriano Rutz, Marcel Kaiser, Laurence Marcourt, Emerson Ferreira Queiroz, Jean‐Robert Ioset, Antonio Grondin, Bruno David, Jean‐Luc Wolfender, Pierre‐Marie AllardList of authors in order
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https://doi.org/10.26434/chemrxiv-2023-sljbtPublisher landing page
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https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/649ea085ba3e99daef499a00/original/a-sample-centric-and-knowledge-driven-computational-framework-for-natural-products-drug-discovery.pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/649ea085ba3e99daef499a00/original/a-sample-centric-and-knowledge-driven-computational-framework-for-natural-products-drug-discovery.pdfDirect OA link when available
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Computer science, Leverage (statistics), Knowledge extraction, Data science, Workflow, Drug discovery, Data mining, Machine learning, Bioinformatics, Database, BiologyTop concepts (fields/topics) attached by OpenAlex
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7Total citation count in OpenAlex
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2024: 5, 2023: 2Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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