Mullowney, Michael W.Duncan, Katherine R.Elsayed, Somayah S.Garg, Nehavan der Hooft, Justin J. J.Martin, Nathaniel I.Meijer, DavidTerlouw, Barbara R.Biermann, FriederikeBlin, KaiDurairaj, JananiGonzalez, Marina GorostiolaHelfrich, Eric J. N.Huber, FlorianLeopold-Messer, StefanRajan, Kohulande Rond, Tristanvan Santen, Jeffrey A.Sorokina, MariaBalunas, Marcy J.Beniddir, Mehdi A.van Bergeijk, Doris A.Carroll, Laura M.Clark, Chase M.Clevert, Djork-ArneDejong, Chris A.Du, ChaoFerrinho, ScarletGrisoni, FrancescaHofstetter, AlbertJespers, WillemKalinina, Olga V.Kautsar, Satria A.Kim, HyunwooLeao, Tiago F.Masschelein, JoleenRees, Evan R.Reher, RaphaelReker, DanielSchwaller, PhilippeSegler, MarwinSkinnider, Michael A.Walker, Allison S.Willighagen, Egon L.Zdrazil, BarbaraZiemert, NadineGoss, Rebecca J. M.Guyomard, PierreVolkamer, AndreaGerwick, William H.Kim, Hyun UkMueller, Rolfvan Wezel, Gilles P.van Westen, Gerard J. P.Hirsch, Anna K. H.Linington, Roger G.Robinson, Serina L.Medema, Marnix H.2024-02-192024-02-192024-02-192023-09-1110.1038/s41573-023-00774-7https://infoscience.epfl.ch/handle/20.500.14299/204105WOS:001093023100001Developments 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.Life Sciences & BiomedicineMass-Spectrometry DataMacromolecular TargetsStructure ElucidationNonribosomal PeptideLigand-BindingPredictionDatabaseSpectraDesignModelstext::journal::journal article::review article