Jean-Luc Wolfender^1,2 A. Kirchhoffer^1,2, Arnaud Gaudry^1,2, Luis Quiros-Guerrero^1,2, Olivier Marco Pagni³, Florence Mehl³, Frederic Burdet³, Laurence Marcourt^1,2, Bruno David⁴, Antonio Grondin⁴, Adriano Rutz^1,2,  Emerson Ferreira Queiroz^1,2, Pierre-Marie Allard^1,2,5 Louis-Félix Nothias^1,2,6

Jean-luc.wolfender@unige.ch

¹Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, CMU, 1211 Geneva, Switzerland, ²School of Pharmaceutical Sciences, University of Geneva, CMU, 1211 Geneva, Switzerland,³ Vital-IT, SIB Swiss Institute of Bioinformatics,1015 Lausanne, Switzerland,  ⁴ Green Mission Pierre Fabre, Branche Phytochimie et Biodiversité, Institut de Recherche Pierre Fabre, Toulouse, France, ⁵ Department of Biology, University of Fribourg, 1700 Fribourg, Switzerland, ⁶Université Côte d’Azur, CNRS, ICN, Nice, France.

The integration of omics, digital technologies and artificial intelligence (AI) is gradually leading to a transformative change in pharmacognosy, enabling unprecedented insights into the plant and microorganisms chemodiversity through detailed metabolome profiling [1]. In this context, modern LC-MS platforms deliver extensive qualitative insights into natural extracts, yet key challenges remain unresolved [2]. These include ensuring accurate metabolite annotations, linking spectral data to bioactive compound quantities, and deriving 3D molecular structures critical for predicting bioactivity. Our research leverages metabolomic datasets from thousands of biodiverse plant and fungal extracts [3]. We are trying to understand how the massive amount of information collected and the taxonomic links can be used to answer questions about confidence/redundance in MS annotation [4]. Managing this flood of data requires innovative solutions, such as semantic web-based knowledge graphs (KGs), which organize complex relationships and enable advanced querying for pattern discovery [5]. This work addresses practical hurdles in automating chemical composition assessment and highlights the synergistic potential of KGs and AI in natural products metabolomics. By combining digitised data with the traditional roots of pharmacognosy, we aim to streamline NP research, accelerate new natural ingredient development and open up new paths for drug discovery.

Acknowledgements: J-LW, L-FN and P-MA are thankful to the Swiss National Science Foundation for the funding of the project (SNF N° CRSII5_189921/1).

Keywords: Metabolomics, knowledge graph, artificial intelligence.

References:

1. Mullowney MW, Duncan KR, Elsayed SS et al. Artificial intelligence for natural product drug discovery. Nat Rev Drug Discovery 2023; 22: 895-916. DOI: 10.1038/s41573-023-00774-7
2. Wolfender JL, Nuzillard JM, van der Hooft JJJ et al. Accelerating Metabolite Identification in Natural Product Research: Toward an Ideal Combination of Liquid Chromatography-High-Resolution Tandem Mass Spectrometry and NMR Profiling, in Silico Databases, and Chemometrics. Anal Chem 2019; 91: 704-742. DOI: 10.1021/acs.analchem.8b05112
3. Allard PM, Gaudry A, Quiros-Guerrero LM et al. Open and reusable annotated mass spectrometry dataset of a chemodiverse collection of 1,600 plant extracts. Gigascience 2022; 12. DOI: 10.1093/gigascience/giac124
4. Rutz A, Wolfender JL. Automated Composition Assessment of Natural Extracts: Untargeted Mass Spectrometry-Based Metabolite Profiling Integrating Semiquantitative Detection. J Agric Food Chem 2023; 71: 18010-18023. DOI: 10.1021/acs.jafc.3c03099
5. Gaudry A, Pagni M, Mehl F et al. A Sample-Centric and Knowledge-Driven Computational Framework for Natural Products Drug Discovery. Acs Central Science 2024; 10: 494-510. DOI: 10.1021/acscentsci.3c00800

Return to previous page