@article{MullowneyDuncanElsayedetal.2023, author = {Mullowney, Michael W. and Duncan, Katherine R. and Elsayed, Somayah S. and Garg, Neha and van der Hooft, Justin J. J. and Martin, Nathaniel I. and Meijer, David and Terlouw, Barbara R. and Biermann, Friederike and Blin, Kai and Durairaj, Janani and Gorostiola Gonz{\´a}lez, Marina and Helfrich, Eric J. N. and Huber, Florian and Leopold-Messer, Stefan and Rajan, Kohulan and de Rond, Tristan and van Santen, Jeffrey A. and Sorokina, Maria and Balunas, Marcy J. and Beniddir, Mehdi A. and van Bergeijk, Doris A. and Carroll, Laura M. and Clark, Chase M. and Clevert, Djork-Arn{\´e} and Dejong, Chris A. and Du, Chao and Ferrinho, Scarlet and Grisoni, Francesca and Hofstetter, Albert and Jespers, Willem and Kalinina, Olga V. and Kautsar, Satria A. and Kim, Hyunwoo and Leao, Tiago F. and Masschelein, Joleen and Rees, Evan R. and Reher, Raphael and Reker, Daniel and Schwaller, Philippe and Segler, Marwin and Skinnider, Michael A. and Walker, Allison S. and Willighagen, Egon L. and Zdrazil, Barbara and Ziemert, Nadine and Goss, Rebecca J. M. and Guyomard, Pierre and Volkamer, Andrea and Gerwick, William H. and Kim, Hyun Uk and M{\"u}ller, Rolf and van Wezel, Gilles P. and van Westen, Gerard J. P. and Hirsch, Anna K. H. and Linington, Roger G. and Robinson, Serina L. and Medema, Marnix H.}, title = {Artificial intelligence for natural product drug discovery}, series = {Nature Reviews Drug Discovery}, volume = {22}, journal = {Nature Reviews Drug Discovery}, number = {11}, publisher = {Springer Nature}, issn = {1474-1776}, doi = {10.1038/s41573-023-00774-7}, pages = {895 -- 916}, year = {2023}, subject = {Maschinelles Lernen}, language = {en} } @article{BeniddirKangGentaJouveetal.2021, author = {Beniddir, Mehdi A. and Kang, Kyo Bin and Genta-Jouve, Gr{\´e}gory and Huber, Florian and Rogers, Simon and van der Hooft, Justin J. J.}, title = {Advances in decomposing complex metabolite mixtures using substructure- and network-based computational metabolomics approaches}, series = {Natural Product Reports}, volume = {38}, journal = {Natural Product Reports}, number = {11}, publisher = {The Royal Society of Chemistry}, issn = {1460-4752}, doi = {10.1039/D1NP00023C}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:due62-opus-34772}, pages = {1967 -- 1993}, year = {2021}, language = {en} } @article{SzwarcRutzLeeetal.2025, author = {Szwarc, Sarah and Rutz, Adriano and Lee, Kyungha and Mejri, Yassine and Bonnet, Olivier and Hazni, Hazrina and Jagora, Adrien and Mbeng Obame, Rany B. and Noh, Jin Kyoung and Otogo N'Nang, Elvis and Alaribe, Stephenie C. and Awang, Khalijah and Bernadat, Guillaume and Choi, Young Hae and Courdavault, Vincent and Frederich, Michel and Gaslonde, Thomas and Huber, Florian and Kam, Toh-Seok and Low, Yun Yee and Poupon, Erwan and van der Hooft, Justin J. J. and Kang, Kyo Bin and Le Pogam, Pierre and Beniddir, Mehdi A.}, title = {Translating community-wide spectral library into actionable chemical knowledge: a proof of concept with monoterpene indole alkaloids}, series = {Journal of Cheminformatics}, volume = {17}, journal = {Journal of Cheminformatics}, number = {1}, publisher = {Springer Nature}, issn = {1758-2946}, doi = {10.1186/s13321-025-01009-0}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:due62-opus-54607}, pages = {15}, year = {2025}, abstract = {With over 3000 representatives, the monoterpene indole alkaloids (MIAs) class is among the most diverse families of plant natural products. The MS/MS spectral space exploration of these complex compounds using chemoinformatic and computational mass spectrometry tools offers a valuable opportunity to extract and share chemical insights from this emblematic family of natural products (NPs). In this work, we first present a substantially updated version of the MIADB, a database now containing 422 MS/MS spectra of MIAs that has been uploaded to the GNPS library versus 172 initial entries. We then introduce an innovative workflow that leverages hundreds of fragmentation spectra to support the FAIRification, extraction and dissemination of chemical knowledge. This workflow aims at the extraction of spectral patterns matching finely defined MIA skeletons. These extracted signatures can then be queried against complex biological extract datasets using MassQL. By applying this strategy to an LC-MS/MS dataset of 75 plant extracts, our results demonstrated the efficiency of this approach in identifying the diversity of MIA skeletons present in the analyzed samples. Additionally, our work enabled the digitization of structural data for diverse MIA skeletons by converting them into machine-readable formats and thereby enhancing their dissemination for the scientific community. Scientific contribution A comprehensive investigation of the monoterpene indole alkaloid chemical space, aiming to highlight skeleton-dependent fragmentation similarity trends and to generate valuable spectrometric signatures that could be used as queries.}, subject = {Computational chemistry}, language = {en} }