TY - JOUR A1 - Mullowney, Michael W. A1 - Duncan, Katherine R. A1 - Elsayed, Somayah S. A1 - Garg, Neha A1 - van der Hooft, Justin J. J. A1 - Martin, Nathaniel I. A1 - Meijer, David A1 - Terlouw, Barbara R. A1 - Biermann, Friederike A1 - Blin, Kai A1 - Durairaj, Janani A1 - Gorostiola González, Marina A1 - Helfrich, Eric J. N. A1 - Huber, Florian A1 - Leopold-Messer, Stefan A1 - Rajan, Kohulan A1 - de Rond, Tristan A1 - van Santen, Jeffrey A. A1 - Sorokina, Maria A1 - Balunas, Marcy J. A1 - Beniddir, Mehdi A. A1 - van Bergeijk, Doris A. A1 - Carroll, Laura M. A1 - Clark, Chase M. A1 - Clevert, Djork-Arné A1 - Dejong, Chris A. A1 - Du, Chao A1 - Ferrinho, Scarlet A1 - Grisoni, Francesca A1 - Hofstetter, Albert A1 - Jespers, Willem A1 - Kalinina, Olga V. A1 - Kautsar, Satria A. A1 - Kim, Hyunwoo A1 - Leao, Tiago F. A1 - Masschelein, Joleen A1 - Rees, Evan R. A1 - Reher, Raphael A1 - Reker, Daniel A1 - Schwaller, Philippe A1 - Segler, Marwin A1 - Skinnider, Michael A. A1 - Walker, Allison S. A1 - Willighagen, Egon L. A1 - Zdrazil, Barbara A1 - Ziemert, Nadine A1 - Goss, Rebecca J. M. A1 - Guyomard, Pierre A1 - Volkamer, Andrea A1 - Gerwick, William H. A1 - Kim, Hyun Uk A1 - Müller, Rolf A1 - van Wezel, Gilles P. A1 - van Westen, Gerard J. P. A1 - Hirsch, Anna K. H. A1 - Linington, Roger G. A1 - Robinson, Serina L. A1 - Medema, Marnix H. T1 - Artificial intelligence for natural product drug discovery JF - Nature Reviews Drug Discovery KW - Maschinelles Lernen KW - Arzneimittelforschung KW - Deep learning KW - Omics-Technologie Y1 - 2023 U6 - https://doi.org/10.1038/s41573-023-00774-7 SN - 1474-1776 VL - 22 IS - 11 SP - 895 EP - 916 PB - Springer Nature ER - TY - JOUR A1 - Beniddir, Mehdi A. A1 - Kang, Kyo Bin A1 - Genta-Jouve, Grégory A1 - Huber, Florian A1 - Rogers, Simon A1 - van der Hooft, Justin J. J. T1 - Advances in decomposing complex metabolite mixtures using substructure- and network-based computational metabolomics approaches JF - Natural Product Reports Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-34772 SN - 1460-4752 VL - 38 IS - 11 SP - 1967 EP - 1993 PB - The Royal Society of Chemistry ER - TY - JOUR A1 - Szwarc, Sarah A1 - Rutz, Adriano A1 - Lee, Kyungha A1 - Mejri, Yassine A1 - Bonnet, Olivier A1 - Hazni, Hazrina A1 - Jagora, Adrien A1 - Mbeng Obame, Rany B. A1 - Noh, Jin Kyoung A1 - Otogo N’Nang, Elvis A1 - Alaribe, Stephenie C. A1 - Awang, Khalijah A1 - Bernadat, Guillaume A1 - Choi, Young Hae A1 - Courdavault, Vincent A1 - Frederich, Michel A1 - Gaslonde, Thomas A1 - Huber, Florian A1 - Kam, Toh-Seok A1 - Low, Yun Yee A1 - Poupon, Erwan A1 - van der Hooft, Justin J. J. A1 - Kang, Kyo Bin A1 - Le Pogam, Pierre A1 - Beniddir, Mehdi A. T1 - Translating community-wide spectral library into actionable chemical knowledge: a proof of concept with monoterpene indole alkaloids JF - Journal of Cheminformatics N2 - 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. KW - Computational chemistry KW - Massenspektrometrie KW - Naturstoffchemie Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-54607 SN - 1758-2946 VL - 17 IS - 1 PB - Springer Nature ER -