Artificial intelligence for natural product drug discovery

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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 González, 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-Arné 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 Müller, 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. (2023) Artificial intelligence for natural product drug discovery. Nature Reviews Drug Discovery, 22 (11). pp. 895-916. ISSN 1474-1784 (https://doi.org/10.1038/s41573-023-00774-7)

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Abstract

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.

ORCID iDs

Mullowney, Michael W., Duncan, Katherine R. ORCID logoORCID: https://orcid.org/0000-0002-3670-4849, Elsayed, Somayah S., Garg, Neha, van der Hooft, Justin J. J., Martin, Nathaniel I., Meijer, David, Terlouw, Barbara R., Biermann, Friederike, Blin, Kai, Durairaj, Janani, Gorostiola González, Marina, Helfrich, Eric J. N., Huber, Florian, Leopold-Messer, Stefan, Rajan, Kohulan, de Rond, Tristan, van Santen, Jeffrey A., Sorokina, Maria, Balunas, Marcy J., Beniddir, Mehdi A., van Bergeijk, Doris A., Carroll, Laura M., Clark, Chase M., Clevert, Djork-Arné, Dejong, Chris A., Du, Chao, Ferrinho, Scarlet, Grisoni, Francesca, Hofstetter, Albert, Jespers, Willem, Kalinina, Olga V., Kautsar, Satria A., Kim, Hyunwoo, Leao, Tiago F., Masschelein, Joleen, Rees, Evan R., Reher, Raphael, Reker, Daniel, Schwaller, Philippe, Segler, Marwin, Skinnider, Michael A., Walker, Allison S., Willighagen, Egon L., Zdrazil, Barbara, Ziemert, Nadine, Goss, Rebecca J. M., Guyomard, Pierre, Volkamer, Andrea, Gerwick, William H., Kim, Hyun Uk, Müller, Rolf, van Wezel, Gilles P., van Westen, Gerard J. P., Hirsch, Anna K. H., Linington, Roger G., Robinson, Serina L. and Medema, Marnix H.;
CORE (COnnecting REpositories)