A Sample-Centric and Knowledge-Driven Computational Framework for Natural Products Drug Discovery

被引：5

作者：

Gaudry, Arnaud ^{[1
,2
]}

Pagni, Marco ^{[3
]}

Mehl, Florence ^{[3
]}

Moretti, Sebastien ^{[3
]}

Quiros-Guerrero, Luis-Manuel ^{[1
,2
]}

Cappelletti, Luca ^{[4
]}

Rutz, Adriano ^{[1
,2
]}

Kaiser, Marcel ^{[5
,6
]}

Marcourt, Laurence ^{[1
,2
]}

Queiroz, Emerson Ferreira ^{[1
,2
]}

Ioset, Jean-Robert ^{[7
]}

Grondin, Antonio ^{[8
]}

David, Bruno ^{[8
]}

Wolfender, Jean-Luc ^{[1
,2
]}

Allard, Pierre-Marie ^{[1
,2
,4
]}

机构：

[1] Univ Geneva, Inst Pharmaceut Sci Western Switzerland, CH-1211 Geneva 4, Switzerland

[2] Univ Geneva, Sch Pharmaceut Sci, CH-1211 Geneva 4, Switzerland

[3] SIB Swiss Inst Bioinformat, Vital IT, CH-1015 Lausanne, Switzerland

[4] Univ Fribourg, Dept Biol, CH-1700 Fribourg, Switzerland

[5] Swiss Trop & Publ Hlth Inst, Dept Med Parasitol & Infect Biol, CH-4123 Allschwil, Switzerland

[6] Univ Basel, Fac Sci, CH-4002 Basel, Switzerland

[7] Drugs Neglected Dis Initiat DNDi, CH-1202 Geneva, Switzerland

[8] Inst Rech Pierre Fabre, Green Mission Pierre Fabre, F-31562 Toulouse, France

来源：

ACS CENTRAL SCIENCE | 2024年 / 10卷 / 03期

基金：

瑞士国家科学基金会;

关键词：

MASS-SPECTROMETRY DATA; TRYPANOSOMA-BRUCEI; MOLECULAR NETWORKING; ASSAY; PRIORITIZATION; DISTRIBUTIONS; RHODESIENSE;

D O I：

10.1021/acscentsci.3c00800

中图分类号：

O6 [化学];

学科分类号：

0703 ;

摘要：

The Experimental Natural Products Knowledge Graph (ENPKG) framework combines a sample-centric approach with semantic enrichment to organize large heterogeneous metabolomics data sets as a knowledge graph. Harmonization of experimental data with publicly available data sets and federated queries mechanisms enable efficient information extraction and the contextualization of metabolomics studies, thereby offering exciting opportunities for drug discovery and global chemodiversity characterization.

引用

页码：494 / 510

页数：17

共 4 条

[1] Something old, something new: revisiting natural products in antibiotic drug discovery
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[2] Discovery of Acylated Glycine and Alanine by Integrating Chemical Derivatization-Based LC-MS/MS and Knowledge-Driven Prediction
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[3] Innovative omics-based approaches for prioritisation and targeted isolation of natural products - new strategies for drug discovery
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[4] An ultrafast and flexible liquid chromatography/tandem mass spectrometry system paves the way for machine learning driven in vivo sample processing in early drug discovery
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Spaeth, Christian
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RAPID COMMUNICATIONS IN MASS SPECTROMETRY, 2021, 35 (12)

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