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Curated Papers > Recent high-impact research on "AI Drug Discovery" (IF≥30, last 5 years)

Artificial intelligence for natural product drug discovery. ​

Literature Information ​

DOI10.1038/s41573-023-00774-7
PMID37697042
JournalNature reviews. Drug discovery
Impact Factor101.8
JCR QuartileQ1
Publication Year2023
Times Cited94
KeywordsArtificial Intelligence, Natural Products, Drug Discovery, Machine Learning, Computational Drug Design
Literature TypeJournal Article, Review, Research Support, N.I.H., Extramural, Research Support, U.S. Gov't, Non-P.H.S., Research Support, Non-U.S. Gov't
ISSN1474-1776
Pages895-916
Issue22(11)
AuthorsMichael 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

TL;DR ​

This research highlights the synergy between advancements in computational omics technologies and artificial intelligence in drug discovery, emphasizing their potential to uncover new natural product diversity and improve drug design through enhanced biological activity predictions. It also addresses critical challenges, such as the necessity for high-quality datasets and effective validation strategies for machine learning algorithms to fully harness these innovations.

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Artificial Intelligence · Natural Products · Drug Discovery · Machine Learning · Computational Drug Design

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.

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Primary Questions Addressed ​

  1. How can machine learning algorithms be optimized to improve the accuracy of biological activity predictions in natural product drug discovery?
  2. What specific challenges do researchers face in obtaining high-quality datasets for training AI models in the context of natural products?
  3. In what ways can the integration of computational omics technologies enhance the efficiency of de novo drug design processes?
  4. How do current AI approaches compare with traditional methods in identifying potential drug candidates from natural products?
  5. What strategies can be implemented to validate the algorithms used in drug discovery to ensure their reliability and effectiveness?

Key Findings ​

Key Insights ​

  1. Research Background and Objectives: The study focuses on the intersection of computational omics technologies and artificial intelligence (AI) in the context of natural product drug discovery. Natural products, which have historically been a rich source of therapeutic agents, exhibit vast chemical diversity. However, traditional methods of drug discovery often struggle to tap into this potential efficiently. The objective of the research is to explore how advancements in AI, particularly machine learning, can enhance the identification and development of novel drug candidates from the extensive library of natural compounds.

  2. Main Methods and Findings: The authors discuss the integration of computational omics technologies with AI methodologies. They highlight how machine learning techniques can predict biological activities and facilitate de novo drug design, allowing researchers to target specific molecular interactions. The synergy between these cutting-edge technologies enables the identification of promising drug candidates from the vast pool of natural products. However, the study also points out the challenges faced in this integration, notably the necessity for high-quality datasets to train deep learning algorithms effectively and to implement robust validation strategies for these algorithms.

  3. Core Conclusions: The research concludes that the combination of computational omics and AI has the potential to revolutionize natural product drug discovery by significantly accelerating the identification of viable drug candidates. However, realizing this potential is contingent upon overcoming the challenges related to data quality and algorithm validation. The findings suggest that enhancing the datasets used for training and developing standardized validation processes will be crucial for the successful application of AI in this field.

  4. Research Significance and Impact: This study is significant as it illuminates a pathway for harnessing the immense biodiversity of natural products for drug discovery through advanced computational techniques. The insights provided could lead to more efficient and effective drug development processes, potentially resulting in novel therapies for various diseases. By addressing the highlighted challenges, the research could pave the way for a new paradigm in drug discovery, where AI plays a central role in translating complex biological data into actionable drug candidates. The implications of this work extend beyond academic research, promising to impact pharmaceutical industries and public health by expediting the development of new, effective treatments derived from nature.

References ​

  1. 'Metabolite-likeness' as a criterion in the design and selection of pharmaceutical drug libraries. - Paul D Dobson;Yogendra Patel;Douglas B Kell - Drug discovery today (2009)
  2. Natural Products as Sources of New Drugs over the Nearly Four Decades from 01/1981 to 09/2019. - David J Newman;Gordon M Cragg - Journal of natural products (2020)
  3. The evolving role of natural products in drug discovery. - Frank E Koehn;Guy T Carter - Nature reviews. Drug discovery (2005)
  4. MIBiG 3.0: a community-driven effort to annotate experimentally validated biosynthetic gene clusters. - Barbara R Terlouw;Kai Blin;Jorge C Navarro-Muñoz;Nicole E Avalon;Marc G Chevrette;Susan Egbert;Sanghoon Lee;David Meijer;Michael J J Recchia;Zachary L Reitz;Jeffrey A van Santen;Nelly Selem-Mojica;Thomas Tørring;Liana Zaroubi;Mohammad Alanjary;Gajender Aleti;César Aguilar;Suhad A A Al-Salihi;Hannah E Augustijn;J Abraham Avelar-Rivas;Luis A Avitia-Domínguez;Francisco Barona-Gómez;Jordan Bernaldo-Agüero;Vincent A Bielinski;Friederike Biermann;Thomas J Booth;Victor J Carrion Bravo;Raquel Castelo-Branco;Fernanda O Chagas;Pablo Cruz-Morales;Chao Du;Katherine R Duncan;Athina Gavriilidou;Damien Gayrard;Karina Gutiérrez-García;Kristina Haslinger;Eric J N Helfrich;Justin J J van der Hooft;Afif P Jati;Edward Kalkreuter;Nikolaos Kalyvas;Kyo Bin Kang;Satria Kautsar;Wonyong Kim;Aditya M Kunjapur;Yong-Xin Li;Geng-Min Lin;Catarina Loureiro;Joris J R Louwen;Nico L L Louwen;George Lund;Jonathan Parra;Benjamin Philmus;Bita Pourmohsenin;Lotte J U Pronk;Adriana Rego;Devasahayam Arokia Balaya Rex;Serina Robinson;L Rodrigo Rosas-Becerra;Eve T Roxborough;Michelle A Schorn;Darren J Scobie;Kumar Saurabh Singh;Nika Sokolova;Xiaoyu Tang;Daniel Udwary;Aruna Vigneshwari;Kristiina Vind;Sophie P J M Vromans;Valentin Waschulin;Sam E Williams;Jaclyn M Winter;Thomas E Witte;Huali Xie;Dong Yang;Jingwei Yu;Mitja Zdouc;Zheng Zhong;Jérôme Collemare;Roger G Linington;Tilmann Weber;Marnix H Medema - Nucleic acids research (2023)
  5. Compendium of specialized metabolite biosynthetic diversity encoded in bacterial genomes. - Athina Gavriilidou;Satria A Kautsar;Nestor Zaburannyi;Daniel Krug;Rolf Müller;Marnix H Medema;Nadine Ziemert - Nature microbiology (2022)
  6. Linking genomics and metabolomics to chart specialized metabolic diversity. - Justin J J van der Hooft;Hosein Mohimani;Anelize Bauermeister;Pieter C Dorrestein;Katherine R Duncan;Marnix H Medema - Chemical Society reviews (2020)
  7. TorchMD: A Deep Learning Framework for Molecular Simulations. - Stefan Doerr;Maciej Majewski;Adrià Pérez;Andreas Krämer;Cecilia Clementi;Frank Noe;Toni Giorgino;Gianni De Fabritiis - Journal of chemical theory and computation (2021)
  8. GPCRmd uncovers the dynamics of the 3D-GPCRome. - Ismael Rodríguez-Espigares;Mariona Torrens-Fontanals;Johanna K S Tiemann;David Aranda-García;Juan Manuel Ramírez-Anguita;Tomasz Maciej Stepniewski;Nathalie Worp;Alejandro Varela-Rial;Adrián Morales-Pastor;Brian Medel-Lacruz;Gáspár Pándy-Szekeres;Eduardo Mayol;Toni Giorgino;Jens Carlsson;Xavier Deupi;Slawomir Filipek;Marta Filizola;José Carlos Gómez-Tamayo;Angel Gonzalez;Hugo Gutiérrez-de-Terán;Mireia Jiménez-Rosés;Willem Jespers;Jon Kapla;George Khelashvili;Peter Kolb;Dorota Latek;Maria Marti-Solano;Pierre Matricon;Minos-Timotheos Matsoukas;Przemyslaw Miszta;Mireia Olivella;Laura Perez-Benito;Davide Provasi;Santiago Ríos;Iván R Torrecillas;Jessica Sallander;Agnieszka Sztyler;Silvana Vasile;Harel Weinstein;Ulrich Zachariae;Peter W Hildebrand;Gianni De Fabritiis;Ferran Sanz;David E Gloriam;Arnau Cordomi;Ramon Guixà-González;Jana Selent - Nature methods (2020)
  9. Computational Approaches for De Novo Drug Design: Past, Present, and Future. - Xuhan Liu;Adriaan P IJzerman;Gerard J P van Westen - Methods in molecular biology (Clifton, N.J.) (2021)
  10. Structure-based drug repurposing: Traditional and advanced AI/ML-aided methods. - Chinmayee Choudhury;N Arul Murugan;U Deva Priyakumar - Drug discovery today (2022)

Literatures Citing This Work ​

  1. Predicting success in Cu-catalyzed C-N coupling reactions using data science. - Mohammad H Samha;Lucas J Karas;David B Vogt;Emmanuel C Odogwu;Jennifer Elward;Jennifer M Crawford;Janelle E Steves;Matthew S Sigman - Science advances (2024)
  2. Antimicrobial resistance crisis: could artificial intelligence be the solution? - Guang-Yu Liu;Dan Yu;Mei-Mei Fan;Xu Zhang;Ze-Yu Jin;Christoph Tang;Xiao-Fen Liu - Military Medical Research (2024)
  3. Research progress of plant-derived natural products in thyroid carcinoma. - Qiujing Du;Weidong Shen - Frontiers in chemistry (2023)
  4. Natural products in osteoarthritis treatment: bridging basic research to clinical applications. - Shunzheng Fang;Bin Zhang;Wei Xiang;Liujie Zheng;Xiaodong Wang;Song Li;Tongyi Zhang;Daibo Feng;Yunquan Gong;Jinhui Wu;Jing Yuan;Yaran Wu;Yizhen Zhu;Enli Liu;Zhenhong Ni - Chinese medicine (2024)
  5. The antibiotic crisis: On the search for novel antibiotics and resistance mechanisms. - Marc W Van Goethem;Ramona Marasco;Pei-Ying Hong;Daniele Daffonchio - Microbial biotechnology (2024)
  6. Effectiveness of molecular fingerprints for exploring the chemical space of natural products. - Davide Boldini;Davide Ballabio;Viviana Consonni;Roberto Todeschini;Francesca Grisoni;Stephan A Sieber - Journal of cheminformatics (2024)
  7. Promiscuous, persistent and problematic: insights into current enterococcal genomics to guide therapeutic strategy. - David Hourigan;Ewelina Stefanovic;Colin Hill;R Paul Ross - BMC microbiology (2024)
  8. Harnessing regulatory networks in Actinobacteria for natural product discovery. - Hannah E Augustijn;Anna M Roseboom;Marnix H Medema;Gilles P van Wezel - Journal of industrial microbiology & biotechnology (2024)
  9. Advancing diabetes treatment: the role of mesenchymal stem cells in islet transplantation. - Lisha Mou;Tony Bowei Wang;Xinyu Wang;Zuhui Pu - Frontiers in immunology (2024)
  10. Combinatorial biosynthesis for the engineering of novel fungal natural products. - Elizabeth Skellam;Sanjeevan Rajendran;Lei Li - Communications chemistry (2024)

... (84 more literatures)

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