The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
Artificial intelligence for natural product drug discovery
Open accessAbstract
Developments in computational omics technologies have provided new means to access the hidden diversity of natural products, unearthing new potential for drug discovery. In parallel, artificial intelligence approaches such as machine learning have led to exciting developments in the computational drug design field, facilitating biological activity prediction and de novo drug design for molecular targets of interest. Here, we describe current and future synergies between these developments to effectively identify drug candidates from the plethora of molecules produced by nature. We also discuss how to address key challenges in realizing the potential of these synergies, such as the need for high-quality datasets to train deep learning algorithms and appropriate strategies for algorithm validation. Advances in computational omics technologies are enabling access to the hidden diversity of natural products, and artificial intelligence approaches are facilitating key steps in harnessing the therapeutic potential of such compounds, including biological activity prediction. This article discusses synergies between these fields to effectively identify drug candidates from the plethora of molecules produced by nature, and how to address the challenges in realizing the potential of these synergies.
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Cites
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Cited by
Effectiveness of molecular fingerprints for exploring the chemical space of natural products
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Metadata
- Authors
- Mullowney, M. W., Duncan, K. R., Elsayed, S. S., Garg, N., van der Hooft, J. J. J., Martin, N. I., Meijer, D., Terlouw, B. R., Biermann, F., Blin, K., Durairaj, J., Gorostiola González, M., Helfrich, E. J. N., Huber, F., Leopold-Messer, S., Rajan, K., de Rond, T., van Santen, J. A., Sorokina, M., Balunas, M. J., Beniddir, M. A., van Bergeijk, D. A., Carroll, L. M., Clark, C. M., Clevert, D.-A., Dejong, C. A., Du, C., Ferrinho, S., Grisoni, F., Hofstetter, A., Jespers, W., Kalinina, O. V., Kautsar, S. A., Kim, H., Leao, T. F., Masschelein, J., Rees, E. R., Reher, R., Reker, D., Schwaller, P., Segler, M., Skinnider, M. A., Walker, A. S., Willighagen, E. L., Zdrazil, B., Ziemert, N., Goss, R. J. M., Guyomard, P., Volkamer, A., Gerwick, W. H., Kim, H. U., Müller, R., van Wezel, G. P., van Westen, G. J. P., Hirsch, A. K. H., Linington, R. G., Robinson, S. L., & Medema, M. H.
- Venue
- Nature Reviews Drug Discovery
- Published
- 2023-09-11
- Volume
- 22
- Issue
- 11
- Pages
- 895-916
- ISSN
- 1474-1776 (ISSN-L)
- 1474-1784
- OpenAlex
- W4386607677
- Cited by
- 460
- References
- 27
- FWCI
- 9.23
- Type
- journal article
- Language
- en
- Open access
- green
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- The paper
- Mullowney, M. W., Duncan, K. R., Elsayed, S. S., Garg, N., van der Hooft, J. J. J., Martin, N. I., Meijer, D., Terlouw, B. R., Biermann, F., Blin, K., Durairaj, J., Gorostiola González, M., Helfrich, E. J. N., Huber, F., Leopold-Messer, S., Rajan, K., de Rond, T., van Santen, J. A., Sorokina, M., . . . Medema, M. H. (2023). Artificial intelligence for natural product drug discovery. Nature Reviews Drug Discovery. https://doi.org/10.1038/s41573-023-00774-7
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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