The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
A Convolutional Neural Network-Based Approach for the Rapid Annotation of Molecularly Diverse Natural Products
Open accessAbstract
This report describes the first application of the novel NMR-based machine learning tool “Small Molecule Accurate Recognition Technology” (SMART 2.0) for mixture analysis and subsequent accelerated discovery and characterization of new natural products. The concept was applied to the extract of a filamentous marine cyanobacterium known to be a prolific producer of cytotoxic natural products. This environmental Symploca extract was roughly fractionated, and then prioritized and guided by cancer cell cytotoxicity, NMR-based SMART 2.0, and MS 2 -based molecular networking. This led to the isolation and rapid identification of a new chimeric swinholide-like macrolide, symplocolide A, as well as the annotation of swinholide A, samholides A–I, and several new derivatives. The planar structure of symplocolide A was confirmed to be a structural hybrid between swinholide A and luminaolide B by 1D/2D NMR and LC-MS 2 analysis. A second example applies SMART 2.0 to the characterization of structurally novel cyclic peptides, and compares this approach to the recently appearing “atomic sort” method. This study exemplifies the revolutionary potential of combined traditional and deep learning-assisted analytical approaches to overcome longstanding challenges in natural products drug discovery.
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Cites
Sumner, L. W., Amberg, A., Barrett, D., Beale, M. H., Beger, R., Daykin, C. A., Fan, T. W.-M., Fiehn, O., Goodacre, R., Griffin, J. L., et al. (2007) · Metabolomics · doi:10.1007/s11306-007-0082-2 · Cited by 5,368
Identifying Small Molecules via High Resolution Mass Spectrometry: Communicating Confidence
Schymanski, E. L., Jeon, J., Gulde, R., Fenner, K., Ruff, M., Singer, H. P., & Hollender, J. (2014) · Environmental Science & Technology · doi:10.1021/es5002105 · Cited by 4,519
Marine Natural Products: A Source of Novel Anticancer Drugs
Khalifa, S. A. M., Elias, N., Farag, M. A., Chen, L., Saeed, A., Hegazy, M.-E. F., Moustafa, M. S., Abd El-Wahed, A., Al-Mousawi, S. M., Musharraf, S. G., et al. (2019) · Marine Drugs · doi:10.3390/md17090491 · Cited by 555
Luesch, H., Moore, R. E., Paul, V. J., Mooberry, S. L., & Corbett, T. H. (2001) · Journal of Natural Products · doi:10.1021/np010049y · Cited by 378
Mass spectrometry searches using MASST
Wang, M., Jarmusch, A. K., Vargas, F., Aksenov, A. A., Gauglitz, J. M., Weldon, K., Petras, D., da Silva, R., Quinn, R., Melnik, A. V., et al. (2020) · Nature Biotechnology · doi:10.1038/s41587-019-0375-9 · Cited by 281
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Cited by
Marine Natural Products: A Source of Novel Anticancer Drugs
Khalifa, S. A. M., Elias, N., Farag, M. A., Chen, L., Saeed, A., Hegazy, M.-E. F., Moustafa, M. S., Abd El-Wahed, A., Al-Mousawi, S. M., Musharraf, S. G., et al. (2019) · Marine Drugs · doi:10.3390/md17090491 · Cited by 555
NPClassifier: A Deep Neural Network-Based Structural Classification Tool for Natural Products
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. (2021) · Journal of Natural Products · doi:10.1021/acs.jnatprod.1c00399 · Cited by 551
Self-citationMachine Learning Methods in Drug Discovery
Patel, L., Shukla, T., Huang, X., Ussery, D. W., & Wang, S. (2020) · Molecules · doi:10.3390/molecules25225277 · Cited by 460
The 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. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkab941 · Cited by 319
Carroll, A. R., Copp, B. R., Davis, R. A., Keyzers, R. A., & Prinsep, M. R. (2022) · Natural Product Reports · doi:10.1039/d1np00076d · Cited by 269
Metadata
- Authors
- Reher, R., Kim, H. W., Zhang, C., Mao, H. H., Wang, M., Nothias, L.-F., Caraballo-Rodriguez, A. M., Glukhov, E., Teke, B., Leao, T., Alexander, K. L., Duggan, B. M., Van Everbroeck, E. L., Dorrestein, P. C., Cottrell, G. W., & Gerwick, W. H.
- Venue
- Journal of the American Chemical Society
- Publisher
- American Chemical Society (ACS)
- Published
- 2020-02-11
- Volume
- 142
- Issue
- 9
- Pages
- 4114-4120
- ISSN
- 0002-7863 (ISSN-L)
- 1520-5126
- OpenAlex
- W3006044405
- Cited by
- 215
- References
- 5
- FWCI
- 13.39
- Type
- journal article
- Language
- en
- Open access
- green
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- Reher, R., Kim, H. W., Zhang, C., Mao, H. H., Wang, M., Nothias, L.-F., Caraballo-Rodriguez, A. M., Glukhov, E., Teke, B., Leao, T., Alexander, K. L., Duggan, B. M., Van Everbroeck, E. L., Dorrestein, P. C., Cottrell, G. W., & Gerwick, W. H. (2020). A Convolutional Neural Network-Based Approach for the Rapid Annotation of Molecularly Diverse Natural Products. Journal of the American Chemical Society. https://doi.org/10.1021/jacs.9b13786
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