Artificial intelligence for natural product drug discovery

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

Research output: Contribution to journalReview articlepeer-review

23 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)895-916
Number of pages22
JournalNature Reviews Drug Discovery
Volume22
Issue number11
DOIs
StatePublished - Nov 2023
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Drug Discovery
  • Pharmacology

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