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; Et al.; Gerard J.P. van Westen

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, Et al., Gerard J.P. van Westen^*

^*Corresponding author for this work

Research output: Contribution to journal › (Systematic) Review article › peer-review

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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
Pages (from-to)	895–916
Number of pages	22
Journal	Nature Reviews Drug Discovery
Volume	22
Issue number	11
DOIs	https://doi.org/10.1038/s41573-023-00774-7
Publication status	Published - Nov 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., ... van Westen, G. J. P. (2023). Artificial intelligence for natural product drug discovery. Nature Reviews Drug Discovery, 22(11), 895–916. https://doi.org/10.1038/s41573-023-00774-7

@article{04043be5bae147869123b2cec66d5d95,

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 {Et al.} and {van Westen}, {Gerard J.P.}",

note = "Funding Information: All authors thank the Lorentz Center and Leiden University for funding the Lorentz Workshop {\textquoteleft}Artificial Intelligence for Natural Product Drug Discovery{\textquoteright} that laid the foundation for this Review. M.W.M. was supported by funds from the Duchossois Family Institute at the University of Chicago. K.R.D. was supported by the UK Research and Innovation Biotechnology and Biological Sciences Research Council (BB/R022054/1). N.G. was supported by an NSF CAREER award (award number 2047235). J.J.J.v.d.H. was supported by an ASDI eScience grant from the Netherlands eScience Center (award number ASDI.2017.030). N.I.M. is supported by funding from the European Research Council (ERC consolidator grant agreement no. 725523). K.B. was supported by a Novo Nordisk Foundation grant NNF20CC0035580. M.G.G. was supported by ONCODE funding. E.J.N.H. was supported by the LOEWE Center for Translational Biodiversity Genomics and the Funds of the Chemical Industry Germany. M.S. was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), Project-ID 239748522, SFB 1127 ChemBioSys. M.A.B. was supported by the National French Agency (ANR grants 15-CE29-0001 and 20-CE43-0010). C.M.C. was supported by a National Library of Medicine training grant to the Computation and Informatics in Biology and Medicine Training Program (NLM 5T15LM007359). S.F. was supported by MASTS/IbioIC/Xanthella. O.V.K. was funded by the Klaus Faber Foundation. H.K. was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (grants NRF 2018R1A5A2023127 and NRF 2022R1F1A107462311). J.M. was supported by a grant from the Research Foundation – Flanders (G061821N). E.R.R. was supported by the US National Science Foundation (DBI-1845890). D.R. was supported, in part, by a Flash Grant from NC Biotech (2021-FLG-3819), a UNC CGIBD Pilot Award (NIH NIDDK DK034987), a Duke Cancer Institute and Duke Microbiome Center Pilot Award (NIH NCI CA014236), the Engineering Research Center for Precision Microbiome Engineering (NSF EEC-2133504), and the Duke Science and Technology Initiative. P.S. acknowledges support from the NCCR Catalysis (grant number 180544), a National Centre of Competence in Research funded by the Swiss National Science Foundation. N.Z. was supported by Germany{\textquoteright}s Excellence Strategy – EXC 2124-390838134. H.U.K. was supported by the KAIST Key Research Institute (Interdisciplinary Research Group) Project. R.G.L. was supported by the US NIH (U41-AT008718 and U24-AT010811). S.L.R. was supported by Eawag discretionary funding. M.H.M. was supported by the Leiden University {\textquoteleft}van der Klaauw{\textquoteright} chair for theoretical biology and an ERC Starting Grant (DECIPHER-948770). We thank A. R. Leach for discussion of the content of this manuscript. We thank all participants of the Lorentz Workshop {\textquoteleft}Artificial Intelligence for Natural Product Drug Discovery{\textquoteright} who did not participate in the review writing for providing inspiration through their talks and/or discussions. Publisher Copyright: {\textcopyright} 2023, Springer Nature Limited.",

year = "2023",

month = nov,

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

language = "English",

volume = "22",

pages = "895–916",

journal = "Nature Reviews Drug Discovery",

issn = "1474-1776",

publisher = "Nature Research",

number = "11",

}

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, Et al. & van Westen, GJP 2023, 'Artificial intelligence for natural product drug discovery', Nature Reviews Drug Discovery, vol. 22, no. 11, 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 - Et al.

AU - van Westen, Gerard J.P.

N1 - Funding Information: All authors thank the Lorentz Center and Leiden University for funding the Lorentz Workshop ‘Artificial Intelligence for Natural Product Drug Discovery’ that laid the foundation for this Review. M.W.M. was supported by funds from the Duchossois Family Institute at the University of Chicago. K.R.D. was supported by the UK Research and Innovation Biotechnology and Biological Sciences Research Council (BB/R022054/1). N.G. was supported by an NSF CAREER award (award number 2047235). J.J.J.v.d.H. was supported by an ASDI eScience grant from the Netherlands eScience Center (award number ASDI.2017.030). N.I.M. is supported by funding from the European Research Council (ERC consolidator grant agreement no. 725523). K.B. was supported by a Novo Nordisk Foundation grant NNF20CC0035580. M.G.G. was supported by ONCODE funding. E.J.N.H. was supported by the LOEWE Center for Translational Biodiversity Genomics and the Funds of the Chemical Industry Germany. M.S. was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), Project-ID 239748522, SFB 1127 ChemBioSys. M.A.B. was supported by the National French Agency (ANR grants 15-CE29-0001 and 20-CE43-0010). C.M.C. was supported by a National Library of Medicine training grant to the Computation and Informatics in Biology and Medicine Training Program (NLM 5T15LM007359). S.F. was supported by MASTS/IbioIC/Xanthella. O.V.K. was funded by the Klaus Faber Foundation. H.K. was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (grants NRF 2018R1A5A2023127 and NRF 2022R1F1A107462311). J.M. was supported by a grant from the Research Foundation – Flanders (G061821N). E.R.R. was supported by the US National Science Foundation (DBI-1845890). D.R. was supported, in part, by a Flash Grant from NC Biotech (2021-FLG-3819), a UNC CGIBD Pilot Award (NIH NIDDK DK034987), a Duke Cancer Institute and Duke Microbiome Center Pilot Award (NIH NCI CA014236), the Engineering Research Center for Precision Microbiome Engineering (NSF EEC-2133504), and the Duke Science and Technology Initiative. P.S. acknowledges support from the NCCR Catalysis (grant number 180544), a National Centre of Competence in Research funded by the Swiss National Science Foundation. N.Z. was supported by Germany’s Excellence Strategy – EXC 2124-390838134. H.U.K. was supported by the KAIST Key Research Institute (Interdisciplinary Research Group) Project. R.G.L. was supported by the US NIH (U41-AT008718 and U24-AT010811). S.L.R. was supported by Eawag discretionary funding. M.H.M. was supported by the Leiden University ‘van der Klaauw’ chair for theoretical biology and an ERC Starting Grant (DECIPHER-948770). We thank A. R. Leach for discussion of the content of this manuscript. We thank all participants of the Lorentz Workshop ‘Artificial Intelligence for Natural Product Drug Discovery’ who did not participate in the review writing for providing inspiration through their talks and/or discussions. Publisher Copyright: © 2023, Springer Nature Limited.

PY - 2023/11

Y1 - 2023/11

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 - (Systematic) Review article

SN - 1474-1776

VL - 22

SP - 895

EP - 916

JO - Nature Reviews Drug Discovery

JF - Nature Reviews Drug Discovery

IS - 11

ER -

Artificial intelligence for natural product drug discovery

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