NPClassifier: A Deep Neural Network-Based Structural Classification Tool for Natural Products
Open accessPublished version of a preprint
Abstract
Computational approaches such as genome and metabolome mining are becoming essential to natural products (NPs) research. Consequently, a need exists for an automated structure-type classification system to handle the massive amounts of data appearing for NP structures. An ideal semantic ontology for the classification of NPs should go beyond the simple presence/absence of chemical substructures, but also include the taxonomy of the producing organism, the nature of the biosynthetic pathway, and/or their biological properties. Thus, a holistic and automatic NP classification framework could have considerable value to comprehensively navigate the relatedness of NPs, and especially so when analyzing large numbers of NPs. Here, we introduce NPClassifier, a deep-learning tool for the automated structural classification of NPs from their counted Morgan fingerprints. NPClassifier is expected to accelerate and enhance NP discovery by linking NP structures to their underlying properties.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
CANOPUS utilizes predicted fingerprints to predict the natural product class of the unknown natural product.
“CANOPUS166 utilizes these predicted fingerprints, which are esoteric to chemists, to predict the natural product class167 of the unknown natural product.”
From Connecting metabolome and phenotype: recent advances in functional metabolomics tools for the identification of bioactive natural products (2024) · cites this paper as [167]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works
Extended-Connectivity Fingerprints
Rogers, D. O. W., & Hahn, M. (2010) · Journal of Chemical Information and Modeling · doi:10.1021/ci100050t · Cited by 7,597 · see citing works
PubChem Substance and Compound databases
Kim, S., Thiessen, P. A., Bolton, E. E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B. A., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv951 · Cited by 5,436 · see citing works
A Deep Learning Approach to Antibiotic Discovery
Stokes, J. M., Yang, K., Swanson, K., Jin, W., Cubillos-Ruiz, A., Donghia, N. M., MacNair, C. R., French, S., Carfrae, L. A., Bloom-Ackermann, Z., et al. (2020) · Cell · doi:10.1016/j.cell.2020.01.021 · Cited by 2,463 · see citing works
Update of the LIPID MAPS comprehensive classification system for lipids
Fahy, E., Subramaniam, S., Murphy, R. C., Nishijima, M., Raetz, C. R. H., Shimizu, T., Spener, F., Gerrit van, M. E. E. R., Wakelam, M. J. O., & Dennis, E. A. (2008) · Journal of Lipid Research · doi:10.1194/jlr.r800095-jlr200 · Cited by 1,766 · see citing works
ClassyFire: automated chemical classification with a comprehensive, computable taxonomy
Djoumbou-Feunang, Y., Eisner, R., Knox, C., Chepelev, L. L., Hastings, J., Owen, G., Fahy, E., Steinbeck, C., Subramanian, S., Bolton, E., et al. (2016) · Journal of Cheminformatics · doi:10.1186/s13321-016-0174-y · Cited by 1,543 · see citing works
Dewick, P. M. (2009) · Wiley eBooks · doi:10.1002/9780470742761 · Cited by 1,279 · see citing works
ChemSpider: An Online Chemical Information Resource
Pence, H. E., & Williams, A. J. (2010) · Journal of Chemical Education · doi:10.1021/ed100697w · Cited by 1,277 · see citing works
ChEBI in 2016: Improved services and an expanding collection of metabolites
Hastings, J., Owen, G., Dekker, A., Ennis, M., Kale, N., Muthukrishnan, V., Turner, S., Swainston, N., Mendes, P., & Steinbeck, C. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv1031 · Cited by 1,083 · see citing works
Systematic classification of unknown metabolites using high-resolution fragmentation mass spectra
Dührkop, K., Nothias, L.-F., Fleischauer, M., Reher, R., Ludwig, M., Hoffmann, M. A., Petras, D., Gerwick, W. H., Rousu, J., Dorrestein, P. C., et al. (2021) · Nature Biotechnology · doi:10.1038/s41587-020-0740-8 · Cited by 860 · see citing works
Self-citationCyanobacterial peptides — Nature's own combinatorial biosynthesis
Welker, M., & von Döhren, H. (2006) · FEMS Microbiology Reviews · doi:10.1111/j.1574-6976.2006.00022.x · Cited by 779 · see citing works
COCONUT online: Collection of Open Natural Products database
Sorokina, M., Merseburger, P., Rajan, K., Yirik, M. A., & Steinbeck, C. (2021) · Journal of Cheminformatics · doi:10.1186/s13321-020-00478-9 · Cited by 681 · see citing works
Medicinal natural products. A biosynthetic approach
Heinrich, M. (2002) · Journal of Ethnopharmacology · doi:10.1016/s0378-8741(02)00014-4 · Cited by 669 · see citing works
Limonoids: Overview of Significant Bioactive Triterpenes Distributed in Plants Kingdom
Roy, A., & Saraf, S. (2006) · Biological and Pharmaceutical Bulletin · doi:10.1248/bpb.29.191 · Cited by 568 · see citing works
ROGERS, F. B. (1965) · Nature · doi:10.1038/205236a0 · Cited by 565 · see citing works
MIBiG 2.0: a repository for biosynthetic gene clusters of known function
Kautsar, S. A., Blin, K., Shaw, S., Navarro-Muñoz, J. C., Terlouw, B. R., van der Hooft, J. J. J., van Santen, J. A., Tracanna, V., Duran, H. G. S., Andreu, V. P., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz882 · Cited by 562 · see citing works
Koch, М. A., Schuffenhauer, A., Scheck, M., Wetzel, S., Casaulta, M., Odermatt, A., Ertl, P., & Waldmann, H. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0503647102 · Cited by 544 · see citing works
Retrospective analysis of natural products provides insights for future discovery trends
Pye, C. R., Bertin, M. J., Lokey, R. S., Gerwick, W. H., & Linington, R. G. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1614680114 · Cited by 537 · see citing works
Self-citationAnti-SARS-CoV-2 activities in vitro of Shuanghuanglian preparations and bioactive ingredients
Su, H.-X., Yao, S., Zhao, W.-F., Li, M.-J., Liu, J., Shang, W.-J., Xie, H., Ke, C.-Q., Hu, H.-C., Gao, M.-N., et al. (2020) · Acta Pharmacologica Sinica · doi:10.1038/s41401-020-0483-6 · Cited by 456 · see citing works
The LOTUS initiative for open knowledge management in natural products research
Rutz, A., Sorokina, M., Galgonek, J., Mietchen, D., Willighagen, E., Gaudry, A., Graham, J. G., Stephan, R., Page, R., Vondrášek, J., et al. (2022) · eLife · doi:10.7554/elife.70780 · Cited by 449 · see citing works
The Natural Products Atlas: An Open Access Knowledge Base for Microbial Natural Products Discovery
van Santen, J. A., Jacob, G., Singh, A. L., Aniebok, V., Balunas, M. J., Bunsko, D., Neto, F. C., Castaño-Espriu, L., Chang, C., Clark, T. N., et al. (2019) · ACS Central Science · doi:10.1021/acscentsci.9b00806 · Cited by 421 · see citing works
Self-citationUse of Natural Products as Chemical Library for Drug Discovery and Network Pharmacology
Gu, J., Gui, Y., Chen, L., Yuan, G., Lu, H., & Xu, X. (2013) · PLoS ONE · doi:10.1371/journal.pone.0062839 · Cited by 406 · see citing works
Charting, Navigating, and Populating Natural Product Chemical Space for Drug Discovery
Lachance, H., Wetzel, S., Kumar, K., & Waldmann, H. (2012) · Journal of Medicinal Chemistry · doi:10.1021/jm300288g · Cited by 403 · see citing works
High-confidence structural annotation of metabolites absent from spectral libraries
Hoffmann, M. A., Nothias, L.-F., Ludwig, M., Fleischauer, M., Gentry, E. C., Witting, M., Dorrestein, P. C., Dührkop, K., & Böcker, S. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01045-9 · Cited by 388 · see citing works
Self-citationKorotcov, A. V., Tkachenko, V., Russo, D. P., & Ekins, S. (2017) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.7b00578 · Cited by 349 · see citing works
Exploring Chemical Space for Drug Discovery Using the Chemical Universe Database
Reymond, J., & Awale, M. (2012) · ACS Chemical Neuroscience · doi:10.1021/cn3000422 · Cited by 321 · see citing works
Super Natural II—a database of natural products
Banerjee, P., Erehman, J., Gohlke, B.-O., Wilhelm, T. E., Preißner, R., & Dunkel, M. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku886 · Cited by 320 · see citing works
Chemical Entities of Biological Interest: an update
de Matos, P., Alcántara, R., Dekker, A., Ennis, M., Hastings, J., Haug, K., Spiteri, I., Turner, S., & Steinbeck, C. (2009) · Nucleic Acids Research · doi:10.1093/nar/gkp886 · Cited by 319 · see citing works
A Deep Learning Approach to Antibiotic Discovery
Stokes, J. M., Yang, K., Swanson, K., Jin, W., Cubillos-Ruiz, A., Donghia, N. M., MacNair, C. R., French, S., Carfrae, L. A., Bloom‐Ackermann, Z., et al. (2020) · Cell · doi:10.1016/j.cell.2020.04.001 · Cited by 298 · see citing works
Kittakoop, P., Mahidol, C., & Ruchirawat, S. (2013) · Current Topics in Medicinal Chemistry · doi:10.2174/1568026613666131216105049 · Cited by 256 · see citing works
Averesch, N. J. H., & Krömer, J. O. (2018) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2018.00032 · Cited by 232 · see citing works
Wenzel, J., Matter, H., & Schmidt, F. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00785 · Cited by 227 · see citing works
Natural products as starting points for the synthesis of complex and diverse compounds
Morrison, K. C., & Hergenrother, P. J. (2013) · Natural Product Reports · doi:10.1039/c3np70063a · Cited by 219 · see citing works
Reher, R., Kim, H. W., Zhang, C., Mao, H. H., Wang, M., Nothias-Scaglia, L.-F., Caraballo‐Rodríguez, A. M., Glukhov, E., Teke, B., Leão, T., et al. (2020) · Journal of the American Chemical Society · doi:10.1021/jacs.9b13786 · Cited by 216 · see citing works
Self-citationLinking genomics and metabolomics to chart specialized metabolic diversity
van der Hooft, J. J. J., Mohimani, H., Bauermeister, A., Dorrestein, P. C., Duncan, K., & Medema, M. H. (2020) · Chemical Society Reviews · doi:10.1039/d0cs00162g · Cited by 215 · see citing works
Self-citationA maximum common substructure-based algorithm for searching and predicting drug-like compounds
Cao, Y., Jiang, T., & Girke, T. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btn186 · Cited by 211 · see citing works
An interpreted atlas of biosynthetic gene clusters from 1,000 fungal genomes
Robey, M. T., Caesar, L. K., Drott, M. T., Keller, N. P., & Kelleher, N. L. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2020230118 · Cited by 188 · see citing works
Beniddir, M. A., Kang, K. B., Genta‐Jouve, G., Huber, F., Rogers, S., & van der Hooft, J. J. J. (2021) · Natural Product Reports · doi:10.1039/d1np00023c · Cited by 166 · see citing works
Self-citationPlant Glycosides and Glycosidases: A Treasure-Trove for Therapeutics
Kytidou, K., Artola, M., Overkleeft, H. S., & Aerts, J. M. F. G. (2020) · Frontiers in Plant Science · doi:10.3389/fpls.2020.00357 · Cited by 163 · see citing works
Merwin, N. J., Mousa, W. K., Dejong, C. A., Skinnider, M. A., Cannon, M. J., Li, H., Dial, K., Gunabalasingam, M., Johnston, C. W., & Magarvey, N. A. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1901493116 · Cited by 157 · see citing works
Handbook of Marine Natural Products
Fattorusso, E., Taglialatela‐Scafati, O., & Gerwick, W. H. (2012) · Springer eBooks · doi:10.1007/978-90-481-3834-0 · Cited by 145 · see citing works
Self-citationSmall Molecule Accurate Recognition Technology (SMART) to Enhance Natural Products Research
Zhang, C., Idelbayev, Y., Roberts, N. P., Tao, Y., Nannapaneni, Y., Duggan, B. M., Min, J., Lin, E. C., Gerwick, E. C., Cottrell, G. W., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-13923-x · Cited by 116 · see citing works
Self-citationRecent progress in the chemistry and biology of limonoids
Zhang, Y., & Xu, H. (2017) · RSC Advances · doi:10.1039/c7ra04715k · Cited by 93 · see citing works
Waitz, J. A., Horan, A. C., Kalyanpur, M., Lee, B. K., Loebenberg, D., MARQUEZ, J. A., Miller, G. H., & Patel, M. (1981) · The Journal of Antibiotics · doi:10.7164/antibiotics.34.1101 · Cited by 83 · see citing works
Exploring the chemical space and the bioactivity profile of lactams: a chemoinformatic study
Saldívar‐González, F. I., Lenci, E., Trabocchi, A., & Medina‐Franco, J. L. (2019) · RSC Advances · doi:10.1039/c9ra04841c · Cited by 81 · see citing works
QBMG: quasi-biogenic molecule generator with deep recurrent neural network
Zheng, S., Yan, X., Gu, Q., Yang, Y., Du, Y., Lu, Y., & Xu, J. (2019) · Journal of Cheminformatics · doi:10.1186/s13321-019-0328-9 · Cited by 79 · see citing works
Scaffold hopping from natural products to synthetic mimetics by holistic molecular similarity
Grisoni, F., Merk, D., Consonni, V., Hiss, J. A., Tagliabue, S. G., Todeschini, R., & Schneider, G. (2018) · Communications Chemistry · doi:10.1038/s42004-018-0043-x · Cited by 77 · see citing works
Natural products as mediators of disease
Garg, N., Luzzatto‐Knaan, T., Melnik, A. V., Caraballo‐Rodríguez, A. M., Floros, D. J., Petras, D., Gregor, R., Dorrestein, P. C., & Phelan, V. V. (2016) · Natural Product Reports · doi:10.1039/c6np00063k · Cited by 68 · see citing works
Self-citationSeo, M., Shin, H. K., Myung, Y., Hwang, S., & No, K. T. (2020) · Journal of Cheminformatics · doi:10.1186/s13321-020-0410-3 · Cited by 63 · see citing works
Progress in the Chemistry of Organic Natural Products 106
Kobayashi, J., Gibbons, S., Falk, H., & Kinghorn, A. D. (2017) · Fortschritte der Chemie Organischer Naturstoffe/Fortschritte der Chemie organischer Naturstoffe/Progress in the chemistry of organic natural products · doi:10.1007/978-3-319-59542-9 · Cited by 61 · see citing works
Bioactive Steroids and Saponins of the Genus Trillium
Rahman, S. U., Ismail, M., Khurram, M., Ullah, I., Rabbi, F., & Iriti, M. (2017) · Molecules · doi:10.3390/molecules22122156 · Cited by 59 · see citing works
Progress in the Chemistry of Organic Natural Products 104
Kinghorn, A. D., Falk, H., Gibbons, S., & Kobayashi, J. (2017) · Fortschritte der Chemie Organischer Naturstoffe/Fortschritte der Chemie organischer Naturstoffe/Progress in the chemistry of organic natural products · doi:10.1007/978-3-319-45618-8 · Cited by 58 · see citing works
Review on Abyssomicins: Inhibitors of the Chorismate Pathway and Folate Biosynthesis
Sadaka, C., Ellsworth, E. L., Hansen, P. B., Ewin, R. A., Damborg, P., & Watts, J. L. (2018) · Molecules · doi:10.3390/molecules23061371 · Cited by 48 · see citing works
Prediction of Natural Product Classes Using Machine Learning and 13C NMR Spectroscopic Data
Martínez-Treviño, S. H., Uc-Cetina, V., Fernández‐Herrera, M. A., & Merino, G. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00293 · Cited by 48 · see citing works
Machine learning in medical imaging
Kumar, A., Bi, L., Kim, J., & Feng, D. D. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-816034-3.00005-5 · Cited by 47 · see citing works
Recent progress in the synthesis of limonoids and limonoid-like natural products
Fu, S., & Liu, B. (2020) · Organic Chemistry Frontiers · doi:10.1039/d0qo00203h · Cited by 46 · see citing works
Anti-inflammatory Potential of Terpenes Present in Cannabis sativa L.
Downer, E. J. (2020) · ACS Chemical Neuroscience · doi:10.1021/acschemneuro.0c00075 · Cited by 45 · see citing works
MAIP: a web service for predicting blood‐stage malaria inhibitors
Bosc, N., Félix, E., Arcila, R., Méndez, D., Saunders, M. R., Green, D. V. S., Ochoada, J., Shelat, A. A., Martin, É., Iyer, P., et al. (2021) · Journal of Cheminformatics · doi:10.1186/s13321-021-00487-2 · Cited by 42 · see citing works
Monaghan, P., Fardis, M., Revill, W. P., & Bell, A. (2005) · The Journal of Infectious Diseases · doi:10.1086/428454 · Cited by 39 · see citing works
Bioengineering natural product biosynthetic pathways for therapeutic applications
Wu, M., Law, B. J. C., Wilkinson, B. M., & Micklefield, J. (2012) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2012.03.008 · Cited by 35 · see citing works
ChEBI: a chemistry ontology and database
de Matos, P., Dekker, A., Ennis, M., Hastings, J., Haug, K., Turner, S., & Steinbeck, C. (2010) · Journal of Cheminformatics · doi:10.1186/1758-2946-2-s1-p6 · Cited by 34 · see citing works
Ixabepilone: A novel microtubule-stabilizing agent for the treatment of metastatic breast cancer
Goodin, S. (2008) · American Journal of Health-System Pharmacy · doi:10.2146/ajhp070628 · Cited by 33 · see citing works
Hastings, J., Glauer, M., Memariani, A., Neuhaus, F., & Mossakowski, T. (2021) · Journal of Cheminformatics · doi:10.1186/s13321-021-00500-8 · Cited by 32 · see citing works
Virtual Screening: Is Bigger Always Better? Or Can Small Be Beautiful?
Clark, D. E. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00101 · Cited by 30 · see citing works
Repositioning Chromones for Early Anti-inflammatory Treatment of COVID-19
Sestili, P., & Stocchi, V. (2020) · Frontiers in Pharmacology · doi:10.3389/fphar.2020.00854 · Cited by 28 · see citing works
Assigning the Origin of Microbial Natural Products by Chemical Space Map and Machine Learning
Capecchi, A., & Reymond, J. (2020) · Biomolecules · doi:10.3390/biom10101385 · Cited by 23 · see citing works
Lianza, M., Leroy, R., Rodrigues, C. M., Borie, N., Sayagh, C., Rémy, S., Kühn, S., Renault, J., & Nuzillard, J. (2021) · Molecules · doi:10.3390/molecules26030637 · Cited by 23 · see citing works
New Structurally Diverse Limonoids from the Seeds of Khaya senegalensis
Tian, X., Li, H., An, F., Li, R., Zhou, M., Yang, M., Kong, L., & Luo, J. (2016) · Planta Medica · doi:10.1055/s-0042-117114 · Cited by 16 · see citing works
Bhat, K. L., & Joullié, M. M. (1987) · Journal of Chemical Education · doi:10.1021/ed064p21 · Cited by 10 · see citing works
Flagged first, then by citations
1 of the 70 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
- Kumar A.; Bi L.; Kim J.; Feng D. D. In Biomedical Information Technology, 2nd ed; Feng D. D., Ed.; Academic Press, 2020; Chapter 5, pp 167– 196.
- Shaffer J. P.; Nothias L.-F.; Thompson L. R.; Sanders J. G.; Salido R. A.; Couvillion S. P.; Brejnrod A. D.; Huang S.; Lejzerowicz F.; Lutz H. L.; Zhu Q.; Martino C.; Morton J. T.; Karthikeyan S.; Nothias-Esposito M.; Dührkop K.; Böcker S.; Kim H.; Aksenov A. A.; Bittremieux W.; Minich J. J.; Marotz C.; Bryant M. M.; Sanders K.; Schwartz T.; Humphrey G.; Vásquez-Baeza Y.; Tripathi A.; Parida L.; Carrieri A. P.; Haiminen N.; Beck K. L.; Das P.; González A.; McDonald D.; Karst S. M.; Albertsen M.; Ackermann G.; DeReus J.; Thomas T.; Petras D.; Shade A.; Stegen J.; Song S. J.; Metz T. O.; Swafford A. D.; Dorrestein P. C.; Jansson J. K.; Gilbert J. A.; Knight R. bioRxiv 2021.
Cited by
Sorted by citations
COCONUT online: Collection of Open Natural Products database
Sorokina, M., Merseburger, P., Rajan, K., Yirik, M. A., & Steinbeck, C. (2021) · Journal of Cheminformatics · doi:10.1186/s13321-020-00478-9 · Cited by 681 · see citing works
Artificial intelligence for natural product drug discovery
Mullowney, M. W., Duncan, K., Elsayed, S. S., Garg, N., van der Hooft, J. J. J., Martin, N. I., Meijer, D., Terlouw, B. R., Biermann, F., Blin, K., et al. (2023) · Nature Reviews Drug Discovery · doi:10.1038/s41573-023-00774-7 · Cited by 507 · see citing works
Self-citationThe LOTUS initiative for open knowledge management in natural products research
Rutz, A., Sorokina, M., Galgonek, J., Mietchen, D., Willighagen, E., Gaudry, A., Graham, J. G., Stephan, R., Page, R., Vondrášek, J., et al. (2022) · eLife · doi:10.7554/elife.70780 · Cited by 449 · see citing works
IMPPAT 2.0: An Enhanced and Expanded Phytochemical Atlas of Indian Medicinal Plants
Vivek-Ananth, R. P., Mohanraj, K., Sahoo, A. K., & Samal, A. (2023) · ACS Omega · doi:10.1021/acsomega.3c00156 · Cited by 410 · see citing works
High-confidence structural annotation of metabolites absent from spectral libraries
Hoffmann, M. A., Nothias, L.-F., Ludwig, M., Fleischauer, M., Gentry, E. C., Witting, M., Dorrestein, P. C., Dührkop, K., & Böcker, S. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01045-9 · Cited by 388 · see citing works
Self-citationThe Natural Products Atlas 2.0: a database of microbially-derived natural products
van Santen, J. A., Poynton, E. F., Iskakova, D., McMann, E., Alsup, T. A., Clark, T. N., Fergusson, C. H., Fewer, D. P., Hughes, A. H., McCadden, C. A., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab941 · Cited by 317 · see citing works
New software tools, databases, and resources in metabolomics: updates from 2020
Misra, B. B. (2021) · Metabolomics · doi:10.1007/s11306-021-01796-1 · Cited by 227 · see citing works
Standardized multi-omics of Earth’s microbiomes reveals microbial and metabolite diversity
Shaffer, J. P., Nothias-Scaglia, L.-F., Thompson, L., Sanders, J. G., Salido, R. A., Couvillion, S., Brejnrod, A., Lejzerowicz, F., Haiminen, N., Huang, S. W., et al. (2022) · Nature Microbiology · doi:10.1038/s41564-022-01266-x · Cited by 218 · see citing works
Self-citationMS2DeepScore: a novel deep learning similarity measure to compare tandem mass spectra
Huber, F., van der Burg, S., van der Hooft, J. J. J., & Ridder, L. (2021) · Journal of Cheminformatics · doi:10.1186/s13321-021-00558-4 · Cited by 209 · see citing works
Gaudêncio, S. P., Bayram, E., Bilela, L. L., Cueto, M., Díaz‐Marrero, A. R., HAZNEDAROĞLU, B. Z., Jiménez, C., Mandalakis, M., Pereira, F., Reyes, F., et al. (2023) · Marine Drugs · doi:10.3390/md21050308 · Cited by 204 · see citing works
Carroll, A. R., Copp, B. R., Grkovic, T., Keyzers, R. A., & Prinsep, M. R. (2024) · Natural Product Reports · doi:10.1039/d3np00061c · Cited by 183 · see citing works
Beniddir, M. A., Kang, K. B., Genta‐Jouve, G., Huber, F., Rogers, S., & van der Hooft, J. J. J. (2021) · Natural Product Reports · doi:10.1039/d1np00023c · Cited by 166 · see citing works
Self-citationGangwal, A., & Lavecchia, A. (2025) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.4c01257 · Cited by 158 · see citing works
RetractedRevisiting natural deep eutectic solvents (NADES) as extraction media and ready-to-use purposes
Bragagnolo, F. S., Strieder, M. M., Pizani, R. S., Mesquita, L. M. D. S., González‐Miquel, M., & Rostagno, M. A. (2024) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2024.117726 · Cited by 117 · see citing works
Domingo‐Fernándéz, D., Gadiya, Y., Preto, A. J., Krettler, C. A., Mubeen, S., Allen, A., Healey, D., & Colluru, V. (2024) · Journal of Natural Products · doi:10.1021/acs.jnatprod.4c00581 · Cited by 110 · see citing works
Integrative omics approaches for biosynthetic pathway discovery in plants
Singh, K. S., van der Hooft, J. J. J., Wees, S. C. M. V., & Medema, M. H. (2022) · Natural Product Reports · doi:10.1039/d2np00032f · Cited by 105 · see citing works
Shah, A. K. P. M., Walter, A., Ottosson, F. A., Russo, F., Navarro-Díaz, M., Boldt, J., Kalinski, J. J., Kontou, E. E., Elofson, J., Polyzois, A., et al. (2024) · Nature Protocols · doi:10.1038/s41596-024-01046-3 · Cited by 101 · see citing works
Self-citationAnnotating metabolite mass spectra with domain-inspired chemical formula transformers
Goldman, S., Wohlwend, J., Stražar, M., Haroush, G., Xavier, R. J., & Coley, C. W. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00708-3 · Cited by 87 · see citing works
Artificial intelligence in microbial natural product drug discovery: current and emerging role
Sahayasheela, V. J., Lankadasari, M. B., Dan, V. M., Dastager, S. G., Pandian, G. N., & Sugiyama, H. (2022) · Natural Product Reports · doi:10.1039/d2np00035k · Cited by 83 · see citing works
Petrén, H., Köllner, T. G., & Junker, R. R. (2022) · New Phytologist · doi:10.1111/nph.18685 · Cited by 79 · see citing works
Coverage bias in small molecule machine learning
Kretschmer, F., Seipp, J., Ludwig, M., Klau, G. W., & Böcker, S. (2025) · Nature Communications · doi:10.1038/s41467-024-55462-w · Cited by 75 · see citing works
Recent advances in mass spectrometry-based computational metabolomics
Ebbels, T. M. D., van der Hooft, J. J. J., Chatelaine, H., Broeckling, C. D., Zamboni, N., Hassoun, S., & Mathé, E. A. (2023) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2023.102288 · Cited by 69 · see citing works
Ancajas, C. M. F., Oyedele, A. S., Butt, C. M., & Walker, A. S. (2024) · Natural Product Reports · doi:10.1039/d4np00009a · Cited by 69 · see citing works
Vitale, G. A., Geibel, C., Minda, V., Wang, M., Aron, A. T., & Petras, D. (2024) · Natural Product Reports · doi:10.1039/d3np00050h · Cited by 66 · see citing works
Self-citationThe year 2020 in natural product bioinformatics: an overview of the latest tools and databases
Medema, M. H. (2021) · Natural Product Reports · doi:10.1039/d0np00090f · Cited by 63 · see citing works
Drug discovery inspired by bioactive small molecules from nature
Kim, S., Lim, S.-W., & Choi, J. (2022) · Animal Cells and Systems · doi:10.1080/19768354.2022.2157480 · Cited by 54 · see citing works
Machine learning-assisted structure annotation of natural products based on MS and NMR data
Hu, G., & Qiu, M. (2023) · Natural Product Reports · doi:10.1039/d3np00025g · Cited by 54 · see citing works
Molecular similarity: Theory, applications, and perspectives
López-Pérez, K., Avellaneda-Tamayo, J. F., Chen, L., López‐López, E., Juárez‐Mercado, K. E., Medina‐Franco, J. L., & Miranda‐Quintana, R. A. (2024) · Artificial Intelligence Chemistry · doi:10.1016/j.aichem.2024.100077 · Cited by 53 · see citing works
Chemotaxonomic investigation of plant terpenoids with an established database (TeroMOL)
Zeng, T., Chen, Y., Jian, Y., Zhang, F., & Wu, R. (2022) · New Phytologist · doi:10.1111/nph.18133 · Cited by 52 · see citing works
Synthetic biology advances towards a bio-based society in the era of artificial intelligence
Iram, A., Dong, Y., & Ignea, C. (2024) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2024.103143 · Cited by 53 · see citing works
67 million natural product-like compound database generated via molecular language processing
Tay, D. W. P., Yeo, N. Z. X., Adaikkappan, K., Lim, Y. H., & Ang, S. J. (2023) · Scientific Data · doi:10.1038/s41597-023-02207-x · Cited by 49 · see citing works
Natural product databases for drug discovery: Features and applications
Zeng, T., Li, J., & Wu, R. (2024) · Pharmaceutical Science Advances · doi:10.1016/j.pscia.2024.100050 · Cited by 47 · see citing works
Novák, V., Andeer, P. F., Bowen, B. P., Ding, Y., Zhalnina, K. V., Hofmockel, K., Tomaka, C., Harwood, T. V., van Winden, M. C. M., Golini, A. N., et al. (2024) · Science Advances · doi:10.1126/sciadv.adg7888 · Cited by 44 · see citing works
Plant and microbial sciences as key drivers in the development of metabolomics research
Aharoni, A., Goodacre, R., & Fernie, A. R. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2217383120 · Cited by 43 · see citing works
Cheminformatics and artificial intelligence for accelerating agrochemical discovery
Djoumbou-Feunang, Y., Wilmot, J. T., Kinney, J., Chanda, P., Yu, P., Sader, C. A., Sharifi, M., Smith, S., Ou, J., Hu, J. K., et al. (2023) · Frontiers in Chemistry · doi:10.3389/fchem.2023.1292027 · Cited by 41 · see citing works
The LOTUS Initiative for Open Natural Products Research: Knowledge Management through WikidataPreprint
Rutz, A., Sorokina, M., Galgonek, J., Mietchen, D., Willighagen, E., Gaudry, A., Graham, J. G., Stephan, R., Page, R., Vondrášek, J., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.02.28.433265 · Cited by 40 · see citing works
Taşdemir, D., Scarpato, S., Utermann-Thüsing, C., Jensen, T., Blümel, M., Wenzel‐Storjohann, A., Welsch, C., & Echelmeyer, V. A. (2023) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2023.168422 · Cited by 39 · see citing works
Capecchi, A., & Reymond, J. (2021) · Journal of Cheminformatics · doi:10.1186/s13321-021-00559-3 · Cited by 39 · see citing works
A Catalog of Natural Products Occurring in Watermelon—Citrullus lanatus
Sorokina, M., McCaffrey, K. S., Deaton, E. E., Ma, G., Ordovás, J. M., Perkins-Veazie, P. M., Steinbeck, C., Levi, A., & Parnell, L. D. (2021) · Frontiers in Nutrition · doi:10.3389/fnut.2021.729822 · Cited by 38 · see citing works
PeruNPDB: the Peruvian Natural Products Database for in silico drug screening
Barazorda-Ccahuana, H. L., Ranilla, L. G., Candia-Puma, M. A., Rodriguez, E. G. C., Centeno-Lopez, A. E., Dávila-Del-Carpio, G., Medina‐Franco, J. L., & Chávez‐Fumagalli, M. Á. (2023) · Scientific Reports · doi:10.1038/s41598-023-34729-0 · Cited by 38 · see citing works
DFT_ANPD: A dual-feature two-sided attention network for anticancer natural products detection
Norouzi, R., Norouzi, R., Abbasi, K., Norouzi, R., & Razzaghi, P. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110442 · Cited by 37 · see citing works
Silva, S. G., Paula, P., Silva, J. P. D., Mil‐Homens, D., Teixeira, M. C., Fialho, A. M., Costa, R., & Keller‐Costa, T. (2022) · Marine Drugs · doi:10.3390/md20070423 · Cited by 37 · see citing works
Metabolomics-Guided Elucidation of Plant Abiotic Stress Responses in the 4IR Era: An Overview
Tinte, M. M., Chele, K. H., van der Hooft, J. J. J., & Tugizimana, F. (2021) · Metabolites · doi:10.3390/metabo11070445 · Cited by 36 · see citing works
A simple, cost-effective and optimized protocol for collecting root exudates from soil grown plants
Döll, S., Koller, H., & van Dam, N. M. (2024) · Rhizosphere · doi:10.1016/j.rhisph.2024.100899 · Cited by 36 · see citing works
Vu, A. H., Kang, M., Wurlitzer, J., Heinicke, S., Li, C., Wood, J. C., Grabe, V., Buell, C. R., Caputi, L., & O'Connor, S. E. (2024) · Journal of the American Chemical Society · doi:10.1021/jacs.4c06336 · Cited by 36 · see citing works
Ghosh, S., Basu, S., Kayal, T., Ashok, G., Ramaiah, S., & Anbarasu, A. (2025) · Discover Applied Sciences · doi:10.1007/s42452-025-06772-1 · Cited by 36 · see citing works
A Sample-Centric and Knowledge-Driven Computational Framework for Natural Products Drug Discovery
Gaudry, A., Pagni, M., Mehl, F., Moretti, S., Quirós-Guerrero, L.-M., Cappelletti, L., Rutz, A., Kaiser, M., Marcourt, L., Queiroz, E. F., et al. (2024) · ACS Central Science · doi:10.1021/acscentsci.3c00800 · Cited by 35 · see citing works
Carroll, A. R., Copp, B. R., Grkovic, T., Keyzers, R. A., & Prinsep, M. R. (2026) · Natural Product Reports · doi:10.1039/d5np00080g · Cited by 34 · see citing works
IMN4NPD: An Integrated Molecular Networking Workflow for Natural Product Dereplication
Sheng, Y., Wang, J., Liu, S., & Jiang, Y. (2024) · Analytical Chemistry · doi:10.1021/acs.analchem.3c04746 · Cited by 33 · see citing works
Leão, T., Wang, M., Silva, R., Gurevich, A. A., Bauermeister, A., Gomes, P. W. P., Brejnrod, A., Glukhov, E., Aron, A. T., Louwen, J. J. R., et al. (2022) · PNAS Nexus · doi:10.1093/pnasnexus/pgac257 · Cited by 33 · see citing works
Self-citationLouwen, J. J. R., Medema, M. H., & van der Hooft, J. J. J. (2023) · Microbiome · doi:10.1186/s40168-022-01444-3 · Cited by 33 · see citing works
Meroterpenoids? A historical and critical review of this biogenetic determinant
Goyer, E., Lavaud, C., & Massiot, G. (2023) · Natural Product Reports · doi:10.1039/d3np00004d · Cited by 33 · see citing works
Abderrezag, N., Bragagnolo, F. S., Louaer, O., Méniai, A.-H., Cifuentes, A., Ibáñez, E., & Mendiola, J. A. (2024) · The Journal of Supercritical Fluids · doi:10.1016/j.supflu.2024.106211 · Cited by 32 · see citing works
Salido, R. A., Zhao, H. N., McDonald, D. T., Mannochio-Russo, H., Zuffa, S., Oles, R. E., Aron, A. T., Abiead, Y. E., Farmer, S., González, A., et al. (2025) · Cell · doi:10.1016/j.cell.2025.01.039 · Cited by 32 · see citing works
Self-citationZeng, J., & Jia, X. (2025) · Journal of Pharmaceutical Analysis · doi:10.1016/j.jpha.2025.101342 · Cited by 32 · see citing works
Hastings, J., Glauer, M., Memariani, A., Neuhaus, F., & Mossakowski, T. (2021) · Journal of Cheminformatics · doi:10.1186/s13321-021-00500-8 · Cited by 32 · see citing works
Allard, P., Gaudry, A., Quirós-Guerrero, L.-M., Rutz, A., Kubo, M., Walker, T. W. N., Défossez, E., Long, C., Grondin, A., David, B., et al. (2022) · GigaScience · doi:10.1093/gigascience/giac124 · Cited by 31 · see citing works
Graph Neural Networks as a Potential Tool in Improving Virtual Screening Programs
Alves, L. A., Ferreira, N. C. D. S., Maricato, V., Alberto, A. V. P., Dias, E. A., & Coelho, N. J. A. (2022) · Frontiers in Chemistry · doi:10.3389/fchem.2021.787194 · Cited by 30 · see citing works
Phytochemical diversity impacts herbivory in a tropical rainforest tree community
Wang, X., He, Y., Sedio, B. E., Jin, L., Ge, X., Glomglieng, S., Cao, M., Yang, J., Swenson, N. G., & Yang, J. (2023) · Ecology Letters · doi:10.1111/ele.14308 · Cited by 30 · see citing works
Domingo‐Fernándéz, D., Gadiya, Y., Mubeen, S., Healey, D., Norman, B. H., & Colluru, V. (2023) · Journal of Cheminformatics · doi:10.1186/s13321-023-00778-w · Cited by 30 · see citing works
FragHub: A Mass Spectral Library Data Integration Workflow
Dablanc, A., Hennechart, S., Perez, A., Cabanac, G., Guitton, Y., Paulhe, N., Lyan, B., Jamin, E. L., Giacomoni, F., & Marti, G. (2024) · Analytical Chemistry · doi:10.1021/acs.analchem.4c02219 · Cited by 29 · see citing works
Nesane, T., Madala, N. E., Kabanda, M. M., Murulana, L. C., & Bahadur, I. (2023) · Colloids and Surfaces A Physicochemical and Engineering Aspects · doi:10.1016/j.colsurfa.2023.131405 · Cited by 29 · see citing works
OSADHI – An online structural and analytics based database for herbs of India
Kiewhuo, K., Gogoi, D., Mahanta, H. J., Rawal, R. K., Das, D., Vaikundamani, S., Jamir, E., & Sastry, G. N. (2022) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2022.107799 · Cited by 28 · see citing works
Effective data visualization strategies in untargeted metabolomics
Mildau, K., Ehlers, H., Meisenburg, M., Pup, E. D., Koetsier, R. A., Ortega, L. R. T., Jonge, N. D., Singh, K. S., Ferreira, D., Othibeng, K., et al. (2024) · Natural Product Reports · doi:10.1039/d4np00039k · Cited by 28 · see citing works
Wang, D., Wang, C., Hu, J.-Q., Wang, J., Liu, W.-C., Zhang, W.-J., Du, X., Wang, H., Zhu, L.-L., Sui, H., et al. (2023) · Analytical Chemistry · doi:10.1021/acs.analchem.2c05145 · Cited by 26 · see citing works
Goßner, M. M., Perret‐Gentil, A., Britt, E., Queloz, V., Glauser, G., Ladd, T. R., Roe, A. D., Cleary, M., Liziniewicz, M., Nielsen, L. R., et al. (2023) · New Phytologist · doi:10.1111/nph.19068 · Cited by 26 · see citing works
Pilon, A. C., Grande, M. D., Silvério, M. R. S., Silva, R., Albernaz, L. C., Vieira, P. C., Lopes, J. L. C., Espíndola, L. S., & Lopes, N. P. (2022) · Molecules · doi:10.3390/molecules27051588 · Cited by 26 · see citing works
Louwen, J. J. R., & van der Hooft, J. J. J. (2021) · mSystems · doi:10.1128/msystems.00726-21 · Cited by 25 · see citing works
Prediction of plant secondary metabolic pathways using deep transfer learning
Bao, H., Zhao, J., Zhao, X., Zhao, C., Lu, X., & Xu, G. (2023) · BMC Bioinformatics · doi:10.1186/s12859-023-05485-9 · Cited by 24 · see citing works
Kim, J.-A., Choi, S.-S., Lim, J. K., & Kim, E. (2025) · Natural Product Reports · doi:10.1039/d5np00013k · Cited by 24 · see citing works
Beltrán-Noboa, A., Jordan-Álvarez, A., Guevara-Terán, M., Gallo, B., Berrueta, L. Á., Giampieri, F., Battino, M. A., Álvarez-Suárez, J. M., & Tejera, E. (2023) · ACS Omega · doi:10.1021/acsomega.3c00043 · Cited by 24 · see citing works
Elser, D., Pflieger, D., Villette, C., Moegle, B., Miesch, L., & Gaquerel, E. (2023) · Science Advances · doi:10.1126/sciadv.ade8984 · Cited by 24 · see citing works
Spatially and temporally resolved metabolome of the human oral cavity
Ciurli, A., Mohammed, Y., Ammon, C., Derks, R. J. E., Olivier‐Jimenez, D., Ducarmon, Q. R., Slingerland, M., Neefjes, J., & Giera, M. A. (2024) · iScience · doi:10.1016/j.isci.2024.108884 · Cited by 23 · see citing works
Multidrug efflux pumps of Pseudomonas aeruginosa show selectivity for their natural substrates
Mazza, L., Bory, A. J., Lüscher, A., Kloehn, J., Wolfender, J., van Delden, C., & Köhler, T. (2025) · Frontiers in Microbiology · doi:10.3389/fmicb.2024.1512472 · Cited by 23 · see citing works
Lianza, M., Leroy, R., Rodrigues, C. M., Borie, N., Sayagh, C., Rémy, S., Kühn, S., Renault, J., & Nuzillard, J. (2021) · Molecules · doi:10.3390/molecules26030637 · Cited by 23 · see citing works
Danquah, C. A., Minkah, P. A. B., Agana, T. A., Moyo, P., Ofori, M., Doe, P., Rali, S., Duah, I. O., Amankwah, K. B., Somuah, S. O., et al. (2022) · Molecules · doi:10.3390/molecules27144475 · Cited by 23 · see citing works
Kang, K. B., Jeong, E., Son, S., Lee, E., Lee, S., Choi, S. Y., Kim, H. W., Yang, H., & Shim, S. H. (2022) · Scientific Data · doi:10.1038/s41597-022-01662-2 · Cited by 23 · see citing works
Self-citationMachine Learning-Based Bioactivity Classification of Natural Products Using LC-MS/MS Metabolomics
Brittin, N. J., Anderson, J. M., Braun, D. R., Rajski, S. R., Currie, C. R., & Bugni, T. S. (2025) · Journal of Natural Products · doi:10.1021/acs.jnatprod.4c01123 · Cited by 22 · see citing works
Inventa: A computational tool to discover structural novelty in natural extracts libraries
Quirós-Guerrero, L.-M., Nothias-Scaglia, L.-F., Gaudry, A., Marcourt, L., Allard, P., Rutz, A., David, B., Queiroz, E. F., & Wolfender, J. (2022) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2022.1028334 · Cited by 22 · see citing works
Gómez‐García, A., Jiménez, D. A. A., Zamora, W. J., Barazorda-Ccahuana, H. L., Chávez‐Fumagalli, M. Á., Valli, M., Andricopulo, A. D., Bolzani, V. D. S., Olmedo, D. A., Solís, P. N., et al. (2023) · Pharmaceuticals · doi:10.3390/ph16101388 · Cited by 22 · see citing works
Cordell, G. A. (2024) · Natural Products and Bioprospecting · doi:10.1007/s13659-024-00431-5 · Cited by 22 · see citing works
NIMO: A Natural Product-Inspired Molecular Generative Model Based on Conditional Transformer
Shen, X., Zeng, T., Chen, N., Li, J., & Wu, R. (2024) · Molecules · doi:10.3390/molecules29081867 · Cited by 21 · see citing works
Biosynthetic Origin of the Methoxy Group in Quinine and Related Alkaloids
Lombe, B. K., Zhou, T., Caputi, L., Ploss, K., & O'Connor, S. E. (2024) · Angewandte Chemie International Edition · doi:10.1002/anie.202418306 · Cited by 21 · see citing works
Molecular networking: An efficient tool for discovering and identifying natural products
Wang, Y., Wang, Y., Zhang, Z., Xu, K., Fang, Q., Wu, X., & Ma, S. (2025) · Journal of Pharmaceutical and Biomedical Analysis · doi:10.1016/j.jpba.2025.116741 · Cited by 21 · see citing works
Data considerations for predictive modeling applied to the discovery of bioactive natural products
Xue, H. T., Stanley-Baker, M., Kong, A. W., Li, H., & Goh, W. W. B. (2022) · Drug Discovery Today · doi:10.1016/j.drudis.2022.05.009 · Cited by 21 · see citing works
Problems, principles and progress in computational annotation of NMR metabolomics data
Judge, M. T., & Ebbels, T. M. D. (2022) · Metabolomics · doi:10.1007/s11306-022-01962-z · Cited by 21 · see citing works
Latin American Natural Product Database (LANaPDB): An Update
Gómez‐García, A., Jiménez, D. A. A., Zamora, W. J., Barazorda-Ccahuana, H. L., Chávez‐Fumagalli, M. Á., Valli, M., Andricopulo, A. D., Bolzani, V. D. S., Olmedo, D. A., Solís, P. N., et al. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c01560 · Cited by 20 · see citing works
Houriet, J., Manwill, P. K., Magaña, A. A., Anderson, V. M., Beniddir, M. A., Bertrand, S., Choi, J., Clark, T. N., Foster, L. J., Halabalaki, M., et al. (2025) · Analytical Chemistry · doi:10.1021/acs.analchem.4c05577 · Cited by 20 · see citing works
Hell, T., Rutz, A., Dürr, L., Dobrzyński, M., Reinhardt, J. K., Lehner, T., Keller, M., John, A., Gupta, M. P., Pertz, O. C., et al. (2022) · Journal of Natural Products · doi:10.1021/acs.jnatprod.2c00146 · Cited by 20 · see citing works
Metabotyping of Andean pseudocereals and characterization of emerging mycotoxins
Vásquez-Ocmín, P. G., Marti, G., Gadéa, A., Cabanac, G., Vásquez-Briones, J. A., Casavilca-Zambrano, S. A., Ponts, N., Jargeat, P., Haddad, M., & Bertani, S. (2022) · Food Chemistry · doi:10.1016/j.foodchem.2022.135134 · Cited by 20 · see citing works
Compendium of Metabolomic and Genomic Datasets for Cyanobacteria: Mined the Gap
Ferrinho, S., Connaris, H., Mouncey, N. J., & Goss, R. J. M. (2024) · Water Research · doi:10.1016/j.watres.2024.121492 · Cited by 19 · see citing works
Chen, W., Yu, Z., Leng, L., Sun, D., Liu, H., Gong, R., Cong, Z., Xiao, W., Zhang, G., Liu, Y., et al. (2025) · The Innovation · doi:10.1016/j.xinn.2025.101011 · Cited by 19 · see citing works
Turf algae redefine the chemical landscape of temperate reefs, limiting kelp forest recovery
Farrell, S., Petras, D., Stincone, P., Yiu, D. S., Burns, J. A., Shah, A. K. P. M., Hartmann, A. C., Brady, D. C., & Rasher, D. B. (2025) · Science · doi:10.1126/science.adt6788 · Cited by 18 · see citing works
Kumar, D. M., & Talwar, P. (2025) · Frontiers in Pharmacology · doi:10.3389/fphar.2025.1616144 · Cited by 18 · see citing works
Bazzano, C. F., de Felício, R., Alves, L. F. G., Costa, J. H., Ortega, R., Vieira, B. D., Morais‐Urano, R. P., Furtado, L. C., Ferreira, É. L. F., Gubiani, J. R., et al. (2024) · Analytical Chemistry · doi:10.1021/acs.analchem.3c05829 · Cited by 18 · see citing works
Guzmán, V. A. A., Montoya-Alfaro, M. E., Negrón, L. P., & Calero, C. S. (2024) · Pharmaceuticals · doi:10.3390/ph17060750 · Cited by 18 · see citing works
Analysis of plant metabolomics data using identification‐free approaches
Yuan, X., Smith, N. S. S., & Moghe, G. D. (2025) · Applications in Plant Sciences · doi:10.1002/aps3.70001 · Cited by 17 · see citing works
NMRMind: A Transformer-Based Model Enabling the Elucidation from Multidimensional NMR to Structures
Xue, X., Sun, H., Sun, J., Patiny, L., Liu, X., Chen, K., Yan, J., Liangning, L., Liu, X., Xu, S., et al. (2025) · Analytical Chemistry · doi:10.1021/acs.analchem.5c03783 · Cited by 18 · see citing works
Shin, S. H., Hur, G., Kim, N. R., Park, J. H. Y., Lee, K. W., & Yang, H. Y. (2023) · Food & Function · doi:10.1039/d2fo03466b · Cited by 17 · see citing works
A powerful two-dimensional chromatography method for the non-target analysis of depolymerised lignin
Tammekivi, E., Batteau, M., Laurenti, D., Lilti, H., & Faure, K. (2023) · Analytica Chimica Acta · doi:10.1016/j.aca.2023.342157 · Cited by 17 · see citing works
- Authors
- Kim, H. W., Wang, M., Leber, C. A., Nothias, L.-F., Reher, R., Kang, K. B., van der Hooft, J. J. J., Dorrestein, P. C., Gerwick, W. H., & Cottrell, G. W.
- Affiliations
- Scripps Institution of Oceanography (US)
- University of California San Diego (US)
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, California 92093, United States
- Sookmyung Women's University (KR)
- Bioinformatics Group, Wageningen University, Wageningen 6700, The Netherlands
- Corresponding author
- Gerwick, William H.
- Cottrell, Garrison W.
- ORCID
- Venue
- Journal of Natural Products
- Publisher
- American Chemical Society (ACS)
- Published
- 2021-10-18
- Volume
- 84
- Issue
- 11
- Pages
- 2795-2807
- ISSN
- 0163-3864 (ISSN-L)
- 1520-6025
- PMC
- PMC8631337
- OpenAlex
- W3206742358
- Cited by
- 551
- References
- 70
- Citations by year
- 2026: 140 · 2025: 165 · 2024: 115 · 2023: 83 · 2022: 30 · 2021: 18
- FWCI
- 32.17
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Natural Products (publishedVersion, cc-by-nc-nd)
- ChemRxiv (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Socio-Environmental Systems Modeling (acceptedVersion, cc-by-nc-nd)
- eScholarship (California Digital Library) (submittedVersion, cc-by-nc-nd)
- Figshare (submittedVersion)
- Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, cc-by)
- Data Archiving and Networked Services (DANS) (submittedVersion, cc-by-nc-nd)
- Funders
- Gordon and Betty Moore Foundation (GBMF7622)
- National Research Foundation of Korea (NRF-2020R1C1C1004046)
- Netherlands eScience Center (ASDI.2017.030)
- National Institute of General Medical Sciences (R01 GM107550)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Health Sciences › Medicine › Pharmacology › Microbial Natural Products and Biosynthesis
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Keywords
- natural products, structural classification, Morgan fingerprints, deep neural network, natural product discovery, biosynthetic pathway, chemical substructures
- MeSH terms
- Biological Products, Neural Networks, Computer, Biosynthetic Pathways
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
