Actualité
Artificial intelligence for natural product drug discovery
- Santé-Sciences-Technologie,
Date(s)
du 27 septembre 2023 au 31 mars 2026
Lieu(x)
BioCIS - Université Paris Saclay
In this review article published in Nature Reviews drug discovery, Dr Mehdi Beniddir from the ANR project, MIACYC and collaborators describe current and future synergies between these developments to effectively identify drug candidates from the plethora of molecules produced by nature.
Artificial intelligence for natural product drug discovery
Mullowney MW, Duncan KR, Elsayed SS, Garg N, van der Hooft JJJ, Martin NI, Meijer D, Terlouw BR, Biermann F, Blin K, Durairaj J, Gorostiola González M, Helfrich EJN, Huber F, Leopold-Messer S, Rajan K, de Rond T, van Santen JA, Sorokina M, Balunas MJ, Beniddir MA, van Bergeijk DA, Carroll LM, Clark CM, Clevert DA, Dejong CA, Du C, Ferrinho S, Grisoni F, Hofstetter A, Jespers W, Kalinina OV, Kautsar SA, Kim H, Leao TF, Masschelein J, Rees ER, Reher R, Reker D, Schwaller P, Segler M, Skinnider MA, Walker AS, Willighagen EL, Zdrazil B, Ziemert N, Goss RJM, Guyomard P, Volkamer A, Gerwick WH, Kim HU, Müller R, van Wezel GP, van Westen GJP, Hirsch AKH, Linington RG, Robinson SL, Medema MH. Artificial intelligence for natural product drug discovery. Nat Rev Drug Discov. 2023 Nov;22(11):895-916. doi: 10.1038/s41573-023-00774-7.
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.
