Artificial intelligence for natural product drug discovery
2023
Mullowney, Michael W. | Duncan, Katherine R. | 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. | Medema, Marnix H.
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.
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