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. Balunas; Mehdi 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

doi:10.1038/s41573-023-00774-7

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^*

^*Corresponding author for this work

Research output: Contribution to journal › Review › peer-review

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 language	English
Journal	Nature Reviews Drug Discovery
Volume	22
Pages (from-to)	895-916
ISSN	1474-1776
DOIs	https://doi.org/10.1038/s41573-023-00774-7
Publication status	Published - 2023

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Mullowney, M. W., Duncan, K. R., Elsayed, S. S., Garg, N., van der Hooft, J. J. J., Martin, N. I., Meijer, D., Terlouw, B. R., Biermann, F., Blin, K., Durairaj, J., Gorostiola González, M., Helfrich, E. J. N., Huber, F., Leopold-Messer, S., Rajan, K., de Rond, T., van Santen, J. A., Sorokina, M., ... Medema, M. H. (2023). Artificial intelligence for natural product drug discovery. Nature Reviews Drug Discovery, 22, 895-916. https://doi.org/10.1038/s41573-023-00774-7

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title = "Artificial intelligence for natural product drug discovery",

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.",

author = "Mullowney, {Michael W.} and Duncan, {Katherine R.} and Elsayed, {Somayah S.} and Neha Garg and {van der Hooft}, {Justin J.J.} and Martin, {Nathaniel I.} and David Meijer and Terlouw, {Barbara R.} and Friederike Biermann and Kai Blin and Janani Durairaj and {Gorostiola Gonz{\'a}lez}, Marina and Helfrich, {Eric J.N.} and Florian Huber and Stefan Leopold-Messer and Kohulan Rajan and {de Rond}, Tristan and {van Santen}, {Jeffrey A.} and Maria Sorokina and Balunas, {Marcy J.} and Beniddir, {Mehdi A.} and {van Bergeijk}, {Doris A.} and Carroll, {Laura M.} and Clark, {Chase M.} and Clevert, {Djork Arn{\'e}} and Dejong, {Chris A.} and Chao Du and Scarlet Ferrinho and Francesca Grisoni and Albert Hofstetter and Willem Jespers and Kalinina, {Olga V.} and Kautsar, {Satria A.} and Hyunwoo Kim and Leao, {Tiago F.} and Joleen Masschelein and Rees, {Evan R.} and Raphael Reher and Daniel Reker and Philippe Schwaller and Marwin Segler and Skinnider, {Michael A.} and Walker, {Allison S.} and Willighagen, {Egon L.} and Barbara Zdrazil and Nadine Ziemert and Goss, {Rebecca J.M.} and Pierre Guyomard and Andrea Volkamer and Gerwick, {William H.} and Kim, {Hyun Uk} and Rolf M{\"u}ller 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.}",

note = "Publisher Copyright: {\textcopyright} 2023, Springer Nature Limited.",

year = "2023",

doi = "10.1038/s41573-023-00774-7",

language = "English",

volume = "22",

pages = "895--916",

journal = "Nature Reviews Drug Discovery",

issn = "1474-1776",

publisher = "Nature Publishing Group",

}

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 2023, 'Artificial intelligence for natural product drug discovery', Nature Reviews Drug Discovery, vol. 22, pp. 895-916. https://doi.org/10.1038/s41573-023-00774-7

TY - JOUR

T1 - Artificial intelligence for natural product drug discovery

AU - Mullowney, Michael W.

AU - Duncan, Katherine R.

AU - Elsayed, Somayah S.

AU - Garg, Neha

AU - van der Hooft, Justin J.J.

AU - Martin, Nathaniel I.

AU - Meijer, David

AU - Terlouw, Barbara R.

AU - Biermann, Friederike

AU - Blin, Kai

AU - Durairaj, Janani

AU - Gorostiola González, Marina

AU - Helfrich, Eric J.N.

AU - Huber, Florian

AU - Leopold-Messer, Stefan

AU - Rajan, Kohulan

AU - de Rond, Tristan

AU - van Santen, Jeffrey A.

AU - Sorokina, Maria

AU - Balunas, Marcy J.

AU - Beniddir, Mehdi A.

AU - van Bergeijk, Doris A.

AU - Carroll, Laura M.

AU - Clark, Chase M.

AU - Clevert, Djork Arné

AU - Dejong, Chris A.

AU - Du, Chao

AU - Ferrinho, Scarlet

AU - Grisoni, Francesca

AU - Hofstetter, Albert

AU - Jespers, Willem

AU - Kalinina, Olga V.

AU - Kautsar, Satria A.

AU - Kim, Hyunwoo

AU - Leao, Tiago F.

AU - Masschelein, Joleen

AU - Rees, Evan R.

AU - Reher, Raphael

AU - Reker, Daniel

AU - Schwaller, Philippe

AU - Segler, Marwin

AU - Skinnider, Michael A.

AU - Walker, Allison S.

AU - Willighagen, Egon L.

AU - Zdrazil, Barbara

AU - Ziemert, Nadine

AU - Goss, Rebecca J.M.

AU - Guyomard, Pierre

AU - Volkamer, Andrea

AU - Gerwick, William H.

AU - Kim, Hyun Uk

AU - Müller, Rolf

AU - van Wezel, Gilles P.

AU - van Westen, Gerard J.P.

AU - Hirsch, Anna K.H.

AU - Linington, Roger G.

AU - Robinson, Serina L.

AU - Medema, Marnix H.

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

U2 - 10.1038/s41573-023-00774-7

DO - 10.1038/s41573-023-00774-7

M3 - Review

C2 - 37697042

AN - SCOPUS:85170520728

SN - 1474-1776

VL - 22

SP - 895

EP - 916

JO - Nature Reviews Drug Discovery

JF - Nature Reviews Drug Discovery

ER -

Artificial intelligence for natural product drug discovery

Abstract

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