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Manen-Freixa L, Antolin AA. Polypharmacology prediction: the long road toward comprehensively anticipating small-molecule selectivity to de-risk drug discovery. Expert Opin Drug Discov 2024:1-27. [PMID: 39004919 DOI: 10.1080/17460441.2024.2376643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Accepted: 07/02/2024] [Indexed: 07/16/2024]
Abstract
INTRODUCTION Small molecules often bind to multiple targets, a behavior termed polypharmacology. Anticipating polypharmacology is essential for drug discovery since unknown off-targets can modulate safety and efficacy - profoundly affecting drug discovery success. Unfortunately, experimental methods to assess selectivity present significant limitations and drugs still fail in the clinic due to unanticipated off-targets. Computational methods are a cost-effective, complementary approach to predict polypharmacology. AREAS COVERED This review aims to provide a comprehensive overview of the state of polypharmacology prediction and discuss its strengths and limitations, covering both classical cheminformatics methods and bioinformatic approaches. The authors review available data sources, paying close attention to their different coverage. The authors then discuss major algorithms grouped by the types of data that they exploit using selected examples. EXPERT OPINION Polypharmacology prediction has made impressive progress over the last decades and contributed to identify many off-targets. However, data incompleteness currently limits most approaches to comprehensively predict selectivity. Moreover, our limited agreement on model assessment challenges the identification of the best algorithms - which at present show modest performance in prospective real-world applications. Despite these limitations, the exponential increase of multidisciplinary Big Data and AI hold much potential to better polypharmacology prediction and de-risk drug discovery.
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Affiliation(s)
- Leticia Manen-Freixa
- Oncobell Division, Bellvitge Biomedical Research Institute (IDIBELL) and ProCURE Department, Catalan Institute of Oncology (ICO), Barcelona, Spain
| | - Albert A Antolin
- Oncobell Division, Bellvitge Biomedical Research Institute (IDIBELL) and ProCURE Department, Catalan Institute of Oncology (ICO), Barcelona, Spain
- Center for Cancer Drug Discovery, The Division of Cancer Therapeutics, The Institute of Cancer Research, London, UK
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2
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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; 22:895-916. [PMID: 37697042 DOI: 10.1038/s41573-023-00774-7] [Citation(s) in RCA: 33] [Impact Index Per Article: 33.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/20/2023] [Indexed: 09/13/2023]
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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Affiliation(s)
| | - Katherine R Duncan
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow, UK
| | - Somayah S Elsayed
- Department of Molecular Biotechnology, Institute of Biology, Leiden University, Leiden, The Netherlands
| | - Neha Garg
- School of Chemistry and Biochemistry, Center for Microbial Dynamics and Infection, Georgia Institute of Technology, Atlanta, GA, USA
| | - Justin J J van der Hooft
- Bioinformatics Group, Wageningen University, Wageningen, The Netherlands
- Department of Biochemistry, University of Johannesburg, Johannesburg, South Africa
| | - Nathaniel I Martin
- Biological Chemistry Group, Institute of Biology, Leiden University, Leiden, The Netherlands
| | - David Meijer
- Bioinformatics Group, Wageningen University, Wageningen, The Netherlands
| | - Barbara R Terlouw
- Bioinformatics Group, Wageningen University, Wageningen, The Netherlands
| | - Friederike Biermann
- Bioinformatics Group, Wageningen University, Wageningen, The Netherlands
- Institute of Molecular Bio Science, Goethe-University Frankfurt, Frankfurt am Main, Germany
- LOEWE Center for Translational Biodiversity Genomics (TBG), Frankfurt am Main, Germany
| | - Kai Blin
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark
| | | | - Marina Gorostiola González
- Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden, The Netherlands
- ONCODE institute, Leiden, The Netherlands
| | - Eric J N Helfrich
- Institute of Molecular Bio Science, Goethe-University Frankfurt, Frankfurt am Main, Germany
- LOEWE Center for Translational Biodiversity Genomics (TBG), Frankfurt am Main, Germany
| | - Florian Huber
- Center for Digitalization and Digitality, Hochschule Düsseldorf, Düsseldorf, Germany
| | - Stefan Leopold-Messer
- Institut für Mikrobiologie, Eidgenössische Technische Hochschule (ETH) Zürich, Zürich, Switzerland
| | - Kohulan Rajan
- Institute for Inorganic and Analytical Chemistry, Friedrich-Schiller-University Jena, Jena, Germany
| | - Tristan de Rond
- School of Chemical Sciences, University of Auckland, Auckland, New Zealand
| | - Jeffrey A van Santen
- Department of Chemistry, Simon Fraser University, Burnaby, British Columbia, Canada
| | - Maria Sorokina
- Institute for Inorganic and Analytical Chemistry, Friedrich-Schiller University, Jena, Germany
- Pharmaceuticals R&D, Bayer AG, Berlin, Germany
| | - Marcy J Balunas
- Department of Microbiology and Immunology, University of Michigan, Ann Arbor, MI, USA
- Department of Medicinal Chemistry, University of Michigan, Ann Arbor, MI, USA
| | - Mehdi A Beniddir
- Équipe "Chimie des Substances Naturelles", Université Paris-Saclay, CNRS, BioCIS, Orsay, France
| | - Doris A van Bergeijk
- Department of Molecular Biotechnology, Institute of Biology, Leiden University, Leiden, The Netherlands
| | - Laura M Carroll
- Structural and Computational Biology Unit, EMBL, Heidelberg, Germany
| | - Chase M Clark
- Division of Pharmaceutical Sciences, School of Pharmacy, University of Wisconsin-Madison, Madison, WI, USA
| | | | | | - Chao Du
- Department of Molecular Biotechnology, Institute of Biology, Leiden University, Leiden, The Netherlands
| | | | - Francesca Grisoni
- Institute for Complex Molecular Systems, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Centre for Living Technologies, Alliance TU/e, WUR, UU, UMC Utrecht, Utrecht, The Netherlands
| | | | - Willem Jespers
- Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden, The Netherlands
| | - Olga V Kalinina
- Helmholtz Institute for Pharmaceutical Research Saarland (HIPS), Helmholtz Centre for Infection Research (HZI), Saarbrücken, Germany
- Drug Bioinformatics, Medical Faculty, Saarland University, Homburg, Germany
- Center for Bioinformatics, Saarland University, Saarbrücken, Germany
| | | | - Hyunwoo Kim
- College of Pharmacy and Integrated Research Institute for Drug Development, Dongguk University Seoul, Goyang-si, Republic of Korea
| | - Tiago F Leao
- Center for Nuclear Energy in Agriculture, University of São Paulo, Piracicaba, Brazil
| | - Joleen Masschelein
- Center for Microbiology, VIB-KU Leuven, Heverlee, Belgium
- Department of Biology, KU Leuven, Heverlee, Belgium
| | - Evan R Rees
- Division of Pharmaceutical Sciences, School of Pharmacy, University of Wisconsin-Madison, Madison, WI, USA
| | - Raphael Reher
- Institute of Pharmaceutical Biology and Biotechnology, University of Marburg, Marburg, Germany
- Institute of Pharmacy, Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany
| | - Daniel Reker
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
- Duke Microbiome Center, Duke University, Durham, NC, USA
| | - Philippe Schwaller
- Laboratory of Artificial Chemical Intelligence, Institut des Sciences et Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | | | - Michael A Skinnider
- Adapsyn Bioscience, Hamilton, Ontario, Canada
- Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia, Canada
| | - Allison S Walker
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
- Department of Biological Sciences, Vanderbilt University, Nashville, TN, USA
| | - Egon L Willighagen
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, Maastricht, The Netherlands
| | - Barbara Zdrazil
- European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridgeshire, UK
| | - Nadine Ziemert
- Interfaculty Institute for Microbiology and Infection Medicine Tuebingen (IMIT), Institute for Bioinformatics and Medical Informatics (IBMI), University of Tuebingen, Tuebingen, Germany
| | | | - Pierre Guyomard
- Bonsai team, CRIStAL - Centre de Recherche en Informatique Signal et Automatique de Lille, Université de Lille, Villeneuve d'Ascq Cedex, France
| | - Andrea Volkamer
- Center for Bioinformatics, Saarland University, Saarbrücken, Germany
- In silico Toxicology and Structural Bioinformatics, Institute of Physiology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - William H Gerwick
- Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA
| | - Hyun Uk Kim
- Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea
| | - Rolf Müller
- Helmholtz Institute for Pharmaceutical Research Saarland (HIPS), Helmholtz Centre for Infection Research (HZI), Saarbrücken, Germany
- Department of Pharmacy, Saarland University, Saarbrücken, Germany
- German Center for infection research (DZIF), Braunschweig, Germany
- Helmholtz International Lab for Anti-Infectives, Saarbrücken, Germany
| | - Gilles P van Wezel
- Department of Molecular Biotechnology, Institute of Biology, Leiden University, Leiden, The Netherlands
- Netherlands Institute of Ecology, NIOO-KNAW, Wageningen, The Netherlands
| | - Gerard J P van Westen
- Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden, The Netherlands.
| | - Anna K H Hirsch
- Helmholtz Institute for Pharmaceutical Research Saarland (HIPS), Helmholtz Centre for Infection Research (HZI), Saarbrücken, Germany.
- Department of Pharmacy, Saarland University, Saarbrücken, Germany.
- German Center for infection research (DZIF), Braunschweig, Germany.
- Helmholtz International Lab for Anti-Infectives, Saarbrücken, Germany.
| | - Roger G Linington
- Department of Chemistry, Simon Fraser University, Burnaby, British Columbia, Canada.
| | - Serina L Robinson
- Department of Environmental Microbiology, Eawag: Swiss Federal Institute for Aquatic Science and Technology, Dübendorf, Switzerland.
| | - Marnix H Medema
- Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.
- Institute of Biology, Leiden University, Leiden, The Netherlands.
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3
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Bustillo L, Laino T, Rodrigues T. The rise of automated curiosity-driven discoveries in chemistry. Chem Sci 2023; 14:10378-10384. [PMID: 37799997 PMCID: PMC10548516 DOI: 10.1039/d3sc03367h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Accepted: 09/07/2023] [Indexed: 10/07/2023] Open
Abstract
The quest for generating novel chemistry knowledge is critical in scientific advancement, and machine learning (ML) has emerged as an asset in this pursuit. Through interpolation among learned patterns, ML can tackle tasks that were previously deemed demanding to machines. This distinctive capacity of ML provides invaluable aid to bench chemists in their daily work. However, current ML tools are typically designed to prioritize experiments with the highest likelihood of success, i.e., higher predictive confidence. In this perspective, we build on current trends that suggest a future in which ML could be just as beneficial in exploring uncharted search spaces through simulated curiosity. We discuss how low and 'negative' data can catalyse one-/few-shot learning, and how the broader use of curious ML and novelty detection algorithms can propel the next wave of chemical discoveries. We anticipate that ML for curiosity-driven research will help the community overcome potentially biased assumptions and uncover unexpected findings in the chemical sciences at an accelerated pace.
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Affiliation(s)
- Latimah Bustillo
- Research Institute for Medicines (iMed), Faculdade de Farmácia, Universidade de Lisboa Lisbon Portugal
| | - Teodoro Laino
- IBM Research Europe Säumerstrasse 4 8803 Rüschlikon Switzerland
- National Center for Competence in Research-Catalysis (NCCR-Catalysis) Zurich Switzerland
| | - Tiago Rodrigues
- Research Institute for Medicines (iMed), Faculdade de Farmácia, Universidade de Lisboa Lisbon Portugal
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4
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TCMSID: a simplified integrated database for drug discovery from traditional chinese medicine. J Cheminform 2022; 14:89. [PMID: 36587232 PMCID: PMC9805110 DOI: 10.1186/s13321-022-00670-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Accepted: 12/14/2022] [Indexed: 01/01/2023] Open
Abstract
Traditional Chinese Medicine (TCM) has been widely used in the treatment of various diseases for millennia. In the modernization process of TCM, TCM ingredient databases are playing more and more important roles. However, most of the existing TCM ingredient databases do not provide simplification function for extracting key ingredients in each herb or formula, which hinders the research on the mechanism of actions of the ingredients in TCM databases. The lack of quality control and standardization of the data in most of these existing databases is also a prominent disadvantage. Therefore, we developed a Traditional Chinese Medicine Simplified Integrated Database (TCMSID) with high storage, high quality and standardization. The database includes 499 herbs registered in the Chinese pharmacopeia with 20,015 ingredients, 3270 targets as well as corresponding detailed information. TCMSID is not only a database of herbal ingredients, but also a TCM simplification platform. Key ingredients from TCM herbs are available to be screened out and regarded as representatives to explore the mechanism of TCM herbs by implementing multi-tool target prediction and multilevel network construction. TCMSID provides abundant data sources and analysis platforms for TCM simplification and drug discovery, which is expected to promote modernization and internationalization of TCM and enhance its international status in the future. TCMSID is freely available at https://tcm.scbdd.com .
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5
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Metwally AA, Nayel AA, Hathout RM. In silico prediction of siRNA ionizable-lipid nanoparticles In vivo efficacy: Machine learning modeling based on formulation and molecular descriptors. Front Mol Biosci 2022; 9:1042720. [PMID: 36619167 PMCID: PMC9811823 DOI: 10.3389/fmolb.2022.1042720] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 12/07/2022] [Indexed: 12/24/2022] Open
Abstract
In silico prediction of the in vivo efficacy of siRNA ionizable-lipid nanoparticles is desirable as it can save time and resources dedicated to wet-lab experimentation. This study aims to computationally predict siRNA nanoparticles in vivo efficacy. A data set containing 120 entries was prepared by combining molecular descriptors of the ionizable lipids together with two nanoparticles formulation characteristics. Input descriptor combinations were selected by an evolutionary algorithm. Artificial neural networks, support vector machines and partial least squares regression were used for QSAR modeling. Depending on how the data set is split, two training sets and two external validation sets were prepared. Training and validation sets contained 90 and 30 entries respectively. The results showed the successful predictions of validation set log (siRNA dose) with Rval 2= 0.86-0.89 and 0.75-80 for validation sets one and two, respectively. Artificial neural networks resulted in the best Rval 2 for both validation sets. For predictions that have high bias, improvement of Rval 2 from 0.47 to 0.96 was achieved by selecting the training set lipids lying within the applicability domain. In conclusion, in vivo performance of siRNA nanoparticles was successfully predicted by combining cheminformatics with machine learning techniques.
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Affiliation(s)
- Abdelkader A. Metwally
- Department of Pharmaceutics, Faculty of Pharmacy, Health Sciences Center, Kuwait University, Kuwait City, Kuwait,Department of Pharmaceutics and Industrial Pharmacy, Faculty of Pharmacy, Ain Shams University, Cairo, Egypt,*Correspondence: Abdelkader A. Metwally,
| | - Amira A. Nayel
- Clinical Pharmacy Department, Alexandria Ophthalmology Hospital, Alexandria, Egypt,Department of Clinical Pharmacy and Pharmacy Practice, Faculty of Pharmacy, Alexandria University, Alexandria, Egypt
| | - Rania M. Hathout
- Department of Pharmaceutics and Industrial Pharmacy, Faculty of Pharmacy, Ain Shams University, Cairo, Egypt
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6
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Liang L, Liu Y, Kang B, Wang R, Sun MY, Wu Q, Meng XF, Lin JP. Large-scale comparison of machine learning algorithms for target prediction of natural products. Brief Bioinform 2022; 23:6675751. [PMID: 36007240 DOI: 10.1093/bib/bbac359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 07/26/2022] [Accepted: 07/31/2022] [Indexed: 11/13/2022] Open
Abstract
Natural products (NPs) and their derivatives are important resources for drug discovery. There are many in silico target prediction methods that have been reported, however, very few of them distinguish NPs from synthetic molecules. Considering the fact that NPs and synthetic molecules are very different in many characteristics, it is necessary to build specific target prediction models of NPs. Therefore, we collected the activity data of NPs and their derivatives from the public databases and constructed four datasets, including the NP dataset, the NPs and its first-class derivatives dataset, the NPs and all its derivatives and the ChEMBL26 compounds dataset. Conditions, including activity thresholds and input features, were explored to access the performance of eight machine learning methods of target prediction of NPs, including support vector machines (SVM), extreme gradient boosting, random forests, K-nearest neighbor, naive Bayes, feedforward neural networks (FNN), convolutional neural networks and recurrent neural networks. As a result, the NPs and all their derivatives datasets were selected to build the best NP-specific models. Furthermore, the consensus models, as well as the voting models, were additionally applied to improve the prediction performance. More evaluations were made on the external validation set and the results demonstrated that (1) the NP-specific model performed better on the target prediction of NPs than the traditional models training on the whole compounds of ChEMBL26. (2) The consensus model of FNN + SVM possessed the best overall performance, and the voting model can significantly improve recall and specificity.
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Affiliation(s)
- Lu Liang
- State Key Laboratory of Medicinal Chemical Biology, College of Pharmacy and Tianjin Key Laboratory of Molecular Drug Research, Nankai University, Haihe Education Park, 38 Tongyan Road, Tianjin 300353, China
| | - Ye Liu
- State Key Laboratory of Medicinal Chemical Biology, College of Pharmacy and Tianjin Key Laboratory of Molecular Drug Research, Nankai University, Haihe Education Park, 38 Tongyan Road, Tianjin 300353, China
| | - Bo Kang
- National Supercomputer Center in Tianjin, 10 Xinhuanxi Road, Tianjin Binhai New Area, Tianjin 300457, China
| | - Ru Wang
- State Key Laboratory of Medicinal Chemical Biology, College of Pharmacy and Tianjin Key Laboratory of Molecular Drug Research, Nankai University, Haihe Education Park, 38 Tongyan Road, Tianjin 300353, China
| | - Meng-Yu Sun
- State Key Laboratory of Medicinal Chemical Biology, College of Pharmacy and Tianjin Key Laboratory of Molecular Drug Research, Nankai University, Haihe Education Park, 38 Tongyan Road, Tianjin 300353, China
| | - Qi Wu
- National Supercomputer Center in Tianjin, 10 Xinhuanxi Road, Tianjin Binhai New Area, Tianjin 300457, China
| | - Xiang-Fei Meng
- National Supercomputer Center in Tianjin, 10 Xinhuanxi Road, Tianjin Binhai New Area, Tianjin 300457, China
| | - Jian-Ping Lin
- State Key Laboratory of Medicinal Chemical Biology, College of Pharmacy and Tianjin Key Laboratory of Molecular Drug Research, Nankai University, Haihe Education Park, 38 Tongyan Road, Tianjin 300353, China.,Biodesign Center, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, 32 West 7th Avenue, Tianjin Airport Economic Area, Tianjin 300308, China.,Platform of Pharmaceutical Intelligence, Tianjin International Joint Academy of Biomedicine, Tianjin 300457, China
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7
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Flam-Shepherd D, Zhu K, Aspuru-Guzik A. Language models can learn complex molecular distributions. Nat Commun 2022; 13:3293. [PMID: 35672310 PMCID: PMC9174447 DOI: 10.1038/s41467-022-30839-x] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Accepted: 05/16/2022] [Indexed: 11/09/2022] Open
Abstract
Deep generative models of molecules have grown immensely in popularity, trained on relevant datasets, these models are used to search through chemical space. The downstream utility of generative models for the inverse design of novel functional compounds, depends on their ability to learn a training distribution of molecules. The most simple example is a language model that takes the form of a recurrent neural network and generates molecules using a string representation. Since their initial use, subsequent work has shown that language models are very capable, in particular, recent research has demonstrated their utility in the low data regime. In this work, we investigate the capacity of simple language models to learn more complex distributions of molecules. For this purpose, we introduce several challenging generative modeling tasks by compiling larger, more complex distributions of molecules and we evaluate the ability of language models on each task. The results demonstrate that language models are powerful generative models, capable of adeptly learning complex molecular distributions. Language models can accurately generate: distributions of the highest scoring penalized LogP molecules in ZINC15, multi-modal molecular distributions as well as the largest molecules in PubChem. The results highlight the limitations of some of the most popular and recent graph generative models- many of which cannot scale to these molecular distributions.
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Affiliation(s)
- Daniel Flam-Shepherd
- Department of Computer Science, University of Toronto, Toronto, ON, M5S 2E4, Canada.
- Vector Institute for Artificial Intelligence, Toronto, ON, M5S 1M1, Canada.
| | - Kevin Zhu
- Department of Computer Science, University of Toronto, Toronto, ON, M5S 2E4, Canada
| | - Alán Aspuru-Guzik
- Department of Computer Science, University of Toronto, Toronto, ON, M5S 2E4, Canada.
- Vector Institute for Artificial Intelligence, Toronto, ON, M5S 1M1, Canada.
- Department of Chemistry, University of Toronto, Toronto, ON, M5G 1Z8, Canada.
- Canadian Institute for Advanced Research, Toronto, ON, M5G 1Z8, Canada.
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8
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Saldívar-González FI, Aldas-Bulos VD, Medina-Franco JL, Plisson F. Natural product drug discovery in the artificial intelligence era. Chem Sci 2022; 13:1526-1546. [PMID: 35282622 PMCID: PMC8827052 DOI: 10.1039/d1sc04471k] [Citation(s) in RCA: 50] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Accepted: 12/10/2021] [Indexed: 12/19/2022] Open
Abstract
Natural products (NPs) are primarily recognized as privileged structures to interact with protein drug targets. Their unique characteristics and structural diversity continue to marvel scientists for developing NP-inspired medicines, even though the pharmaceutical industry has largely given up. High-performance computer hardware, extensive storage, accessible software and affordable online education have democratized the use of artificial intelligence (AI) in many sectors and research areas. The last decades have introduced natural language processing and machine learning algorithms, two subfields of AI, to tackle NP drug discovery challenges and open up opportunities. In this article, we review and discuss the rational applications of AI approaches developed to assist in discovering bioactive NPs and capturing the molecular "patterns" of these privileged structures for combinatorial design or target selectivity.
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Affiliation(s)
- F I Saldívar-González
- DIFACQUIM Research Group, School of Chemistry, Department of Pharmacy, Universidad Nacional Autónoma de México Avenida Universidad 3000 04510 Mexico Mexico
| | - V D Aldas-Bulos
- Unidad de Genómica Avanzada, Laboratorio Nacional de Genómica para la Biodiversidad (Langebio), Centro de Investigación y de Estudios Avanzados del IPN Irapuato Guanajuato Mexico
| | - J L Medina-Franco
- DIFACQUIM Research Group, School of Chemistry, Department of Pharmacy, Universidad Nacional Autónoma de México Avenida Universidad 3000 04510 Mexico Mexico
| | - F Plisson
- CONACYT - Unidad de Genómica Avanzada, Laboratorio Nacional de Genómica para la Biodiversidad (Langebio), Centro de Investigación y de Estudios Avanzados del IPN Irapuato Guanajuato Mexico
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9
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Llorach-Pares L, Nonell-Canals A, Avila C, Sanchez-Martinez M. Computer-Aided Drug Design (CADD) to De-Orphanize Marine Molecules: Finding Potential Therapeutic Agents for Neurodegenerative and Cardiovascular Diseases. Mar Drugs 2022; 20:53. [PMID: 35049908 PMCID: PMC8781171 DOI: 10.3390/md20010053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2021] [Revised: 12/24/2021] [Accepted: 12/27/2021] [Indexed: 11/30/2022] Open
Abstract
Computer-aided drug design (CADD) techniques allow the identification of compounds capable of modulating protein functions in pathogenesis-related pathways, which is a promising line on drug discovery. Marine natural products (MNPs) are considered a rich source of bioactive compounds, as the oceans are home to much of the planet's biodiversity. Biodiversity is directly related to chemodiversity, which can inspire new drug discoveries. Therefore, natural products (NPs) in general, and MNPs in particular, have been used for decades as a source of inspiration for the design of new drugs. However, NPs present both opportunities and challenges. These difficulties can be technical, such as the need to dive or trawl to collect the organisms possessing the compounds, or biological, due to their particular marine habitats and the fact that they can be uncultivable in the laboratory. For all these difficulties, the contributions of CADD can play a very relevant role in simplifying their study, since, for example, no biological sample is needed to carry out an in-silico analysis. Therefore, the amount of natural product that needs to be used in the entire preclinical and clinical study is significantly reduced. Here, we exemplify how this combination between CADD and MNPs can help unlock their therapeutic potential. In this study, using a set of marine invertebrate molecules, we elucidate their possible molecular targets and associated therapeutic potential, establishing a pipeline that can be replicated in future studies.
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Affiliation(s)
- Laura Llorach-Pares
- Mind the Byte S.L., 08028 Barcelona, Catalonia, Spain; (L.L.-P.); (A.N.-C.)
- Department of Evolutionary Biology, Ecology and Environmental Sciences, Faculty of Biology and Biodiversity Research Institute (IRBio), University of Barcelona, 08028 Barcelona, Catalonia, Spain;
| | | | - Conxita Avila
- Department of Evolutionary Biology, Ecology and Environmental Sciences, Faculty of Biology and Biodiversity Research Institute (IRBio), University of Barcelona, 08028 Barcelona, Catalonia, Spain;
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10
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Machine learning & deep learning in data-driven decision making of drug discovery and challenges in high-quality data acquisition in the pharmaceutical industry. Future Med Chem 2021; 14:245-270. [PMID: 34939433 DOI: 10.4155/fmc-2021-0243] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
Predicting novel small molecule bioactivities for the target deconvolution, hit-to-lead optimization in drug discovery research, requires molecular representation. Previous reports have demonstrated that machine learning (ML) and deep learning (DL) have substantial implications in virtual screening, peptide synthesis, drug ADMET screening and biomarker discovery. These strategies can increase the positive outcomes in the drug discovery process without false-positive rates and can be achieved in a cost-effective way with a minimum duration of time by high-quality data acquisition. This review substantially discusses the recent updates in AI tools as cheminformatics application in medicinal chemistry for the data-driven decision making of drug discovery and challenges in high-quality data acquisition in the pharmaceutical industry while improving small-molecule bioactivities and properties.
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11
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Li G, Lin P, Wang K, Gu CC, Kusari S. Artificial intelligence-guided discovery of anticancer lead compounds from plants and associated microorganisms. Trends Cancer 2021; 8:65-80. [PMID: 34750090 DOI: 10.1016/j.trecan.2021.10.002] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 10/02/2021] [Accepted: 10/08/2021] [Indexed: 12/20/2022]
Abstract
Plants and associated microorganisms are essential sources of natural products against human cancer diseases, partly exemplified by plant-derived anticancer drugs such as Taxol (paclitaxel). Natural products provide diverse mechanisms of action and can be used directly or as prodrugs for further anticancer optimization. Despite the success, major bottlenecks can delay anticancer lead discovery and implementation. Recent advances in sequencing and omics-related technology have provided a mine of information for developing new therapeutics from natural products. Artificial intelligence (AI), including machine learning (ML), has offered powerful techniques for extensive data analysis and prediction-making in anticancer leads discovery. This review presents an overview of current AI-guided solutions to discover anticancer lead compounds, focusing on natural products from plants and associated microorganisms.
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Affiliation(s)
- Gang Li
- Department of Natural Medicinal Chemistry and Pharmacognosy, School of Pharmacy, Qingdao University, Qingdao 266071, People's Republic of China.
| | - Ping Lin
- Department of Natural Medicinal Chemistry and Pharmacognosy, School of Pharmacy, Qingdao University, Qingdao 266071, People's Republic of China
| | - Ke Wang
- Department of Natural Medicinal Chemistry and Pharmacognosy, School of Pharmacy, Qingdao University, Qingdao 266071, People's Republic of China
| | - Chen-Chen Gu
- Department of Natural Medicinal Chemistry and Pharmacognosy, School of Pharmacy, Qingdao University, Qingdao 266071, People's Republic of China
| | - Souvik Kusari
- Center for Mass Spectrometry, Faculty of Chemistry and Chemical Biology, Technische Universität Dortmund, Dortmund 44227, Germany.
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12
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Antifungal activity of menthol alone and in combination on growth inhibition and biofilm formation of Candida albicans. J Herb Med 2021. [DOI: 10.1016/j.hermed.2021.100495] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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13
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Vaou N, Stavropoulou E, Voidarou C, Tsigalou C, Bezirtzoglou E. Towards Advances in Medicinal Plant Antimicrobial Activity: A Review Study on Challenges and Future Perspectives. Microorganisms 2021; 9:microorganisms9102041. [PMID: 34683362 PMCID: PMC8541629 DOI: 10.3390/microorganisms9102041] [Citation(s) in RCA: 169] [Impact Index Per Article: 56.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 09/22/2021] [Accepted: 09/23/2021] [Indexed: 12/17/2022] Open
Abstract
The increasing incidence of drug- resistant pathogens raises an urgent need to identify and isolate new bioactive compounds from medicinal plants using standardized modern analytical procedures. Medicinal plant-derived compounds could provide novel straightforward approaches against pathogenic bacteria. This review explores the antimicrobial activity of plant-derived components, their possible mechanisms of action, as well as their chemical potential. The focus is put on the current challenges and future perspectives surrounding medicinal plants antimicrobial activity. There are some inherent challenges regarding medicinal plant extracts and their antimicrobial efficacy. Appropriate and optimized extraction methodology plant species dependent leads to upgraded and selective extracted compounds. Antimicrobial susceptibility tests for the determination of the antimicrobial activity of plant extracts may show variations in obtained results. Moreover, there are several difficulties and problems that need to be overcome for the development of new antimicrobials from plant extracts, while efforts have been made to enhance the antimicrobial activity of chemical compounds. Research on the mechanisms of action, interplay with other substances, and the pharmacokinetic and/or pharmacodynamic profile of the medicinal plant extracts should be given high priority to characterize them as potential antimicrobial agents.
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Affiliation(s)
- Natalia Vaou
- Laboratory of Hygiene and Environmental Protection, Department of Medicine, Democritus University of Thrace, Dragana, 68100 Alexandroupolis, Greece;
- Correspondence: (N.V.); (E.S.)
| | - Elisavet Stavropoulou
- Department of Infectious Diseases, Centre Hospitalier Universitaire Vaudois (CHUV), Rue du Bugnon, 1011 Lausanne, Switzerland
- Correspondence: (N.V.); (E.S.)
| | - Chrysa Voidarou
- Department of Agriculture, University of Ioannina, 47132 Arta, Greece;
| | - Christina Tsigalou
- Laboratory of Microbiology, Department of Medicine, Democritus University of Thrace, Dragana, 68100 Alexandroupolis, Greece;
| | - Eugenia Bezirtzoglou
- Laboratory of Hygiene and Environmental Protection, Department of Medicine, Democritus University of Thrace, Dragana, 68100 Alexandroupolis, Greece;
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14
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Medema MH, de Rond T, Moore BS. Mining genomes to illuminate the specialized chemistry of life. Nat Rev Genet 2021; 22:553-571. [PMID: 34083778 PMCID: PMC8364890 DOI: 10.1038/s41576-021-00363-7] [Citation(s) in RCA: 97] [Impact Index Per Article: 32.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/09/2021] [Indexed: 02/07/2023]
Abstract
All organisms produce specialized organic molecules, ranging from small volatile chemicals to large gene-encoded peptides, that have evolved to provide them with diverse cellular and ecological functions. As natural products, they are broadly applied in medicine, agriculture and nutrition. The rapid accumulation of genomic information has revealed that the metabolic capacity of virtually all organisms is vastly underappreciated. Pioneered mainly in bacteria and fungi, genome mining technologies are accelerating metabolite discovery. Recent efforts are now being expanded to all life forms, including protists, plants and animals, and new integrative omics technologies are enabling the increasingly effective mining of this molecular diversity.
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Affiliation(s)
- Marnix H Medema
- Bioinformatics Group, Wageningen University, Wageningen, The Netherlands
| | - Tristan de Rond
- Center for Marine Biotechnology and Biomedicine, Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA
| | - Bradley S Moore
- Center for Marine Biotechnology and Biomedicine, Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA.
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA.
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15
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Friedrich L, Cingolani G, Ko Y, Iaselli M, Miciaccia M, Perrone MG, Neukirch K, Bobinger V, Merk D, Hofstetter RK, Werz O, Koeberle A, Scilimati A, Schneider G. Learning from Nature: From a Marine Natural Product to Synthetic Cyclooxygenase-1 Inhibitors by Automated De Novo Design. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2021; 8:e2100832. [PMID: 34176236 PMCID: PMC8373093 DOI: 10.1002/advs.202100832] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 05/16/2021] [Indexed: 05/03/2023]
Abstract
The repertoire of natural products offers tremendous opportunities for chemical biology and drug discovery. Natural product-inspired synthetic molecules represent an ecologically and economically sustainable alternative to the direct utilization of natural products. De novo design with machine intelligence bridges the gap between the worlds of bioactive natural products and synthetic molecules. On employing the compound Marinopyrrole A from marine Streptomyces as a design template, the algorithm constructs innovative small molecules that can be synthesized in three steps, following the computationally suggested synthesis route. Computational activity prediction reveals cyclooxygenase (COX) as a putative target of both Marinopyrrole A and the de novo designs. The molecular designs are experimentally confirmed as selective COX-1 inhibitors with nanomolar potency. X-ray structure analysis reveals the binding of the most selective compound to COX-1. This molecular design approach provides a blueprint for natural product-inspired hit and lead identification for drug discovery with machine intelligence.
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Affiliation(s)
- Lukas Friedrich
- Department of Chemistry and Applied BiosciencesETH ZurichVladimir‐Prelog‐Weg 4Zurich8093Switzerland
| | - Gino Cingolani
- Department of Biochemistry and Molecular BiologySidney Kimmel Cancer CenterThomas Jefferson University1020 Locust StreetPhiladelphiaPA19107USA
| | - Ying‐Hui Ko
- Department of Biochemistry and Molecular BiologySidney Kimmel Cancer CenterThomas Jefferson University1020 Locust StreetPhiladelphiaPA19107USA
| | - Mariaclara Iaselli
- Department of Pharmacy – Pharmaceutical SciencesUniversity of BariVia E. Orabona 4Bari70125Italy
| | - Morena Miciaccia
- Department of Pharmacy – Pharmaceutical SciencesUniversity of BariVia E. Orabona 4Bari70125Italy
| | - Maria Grazia Perrone
- Department of Pharmacy – Pharmaceutical SciencesUniversity of BariVia E. Orabona 4Bari70125Italy
| | - Konstantin Neukirch
- Michael Popp Institute and Center for Molecular Biosciences Innsbruck (CMBI)University of InnsbruckInnsbruck6020Austria
| | - Veronika Bobinger
- Department of Chemistry and Applied BiosciencesETH ZurichVladimir‐Prelog‐Weg 4Zurich8093Switzerland
| | - Daniel Merk
- Department of Chemistry and Applied BiosciencesETH ZurichVladimir‐Prelog‐Weg 4Zurich8093Switzerland
- Institute of Pharmaceutical ChemistryGoethe‐UniversityMax‐von‐Laue Straße 9Frankfurt am Main60438Germany
| | - Robert Klaus Hofstetter
- Department of Pharmaceutical/Medicinal ChemistryFriedrich‐Schiller‐University JenaPhilosophenweg 14Jena07743Germany
| | - Oliver Werz
- Department of Pharmaceutical/Medicinal ChemistryFriedrich‐Schiller‐University JenaPhilosophenweg 14Jena07743Germany
| | - Andreas Koeberle
- Michael Popp Institute and Center for Molecular Biosciences Innsbruck (CMBI)University of InnsbruckInnsbruck6020Austria
| | - Antonio Scilimati
- Department of Pharmacy – Pharmaceutical SciencesUniversity of BariVia E. Orabona 4Bari70125Italy
| | - Gisbert Schneider
- Department of Chemistry and Applied BiosciencesETH ZurichVladimir‐Prelog‐Weg 4Zurich8093Switzerland
- ETH Singapore SEC Ltd1 CREATE Way, #06‐01 CREATE TowerSingapore138602Singapore
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16
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Conde J, Pumroy RA, Baker C, Rodrigues T, Guerreiro A, Sousa BB, Marques MC, de Almeida BP, Lee S, Leites EP, Picard D, Samanta A, Vaz SH, Sieglitz F, Langini M, Remke M, Roque R, Weiss T, Weller M, Liu Y, Han S, Corzana F, Morais VA, Faria C, Carvalho T, Filippakopoulos P, Snijder B, Barbosa-Morais NL, Moiseenkova-Bell VY, Bernardes GJL. Allosteric Antagonist Modulation of TRPV2 by Piperlongumine Impairs Glioblastoma Progression. ACS CENTRAL SCIENCE 2021; 7:868-881. [PMID: 34079902 PMCID: PMC8161495 DOI: 10.1021/acscentsci.1c00070] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Indexed: 05/04/2023]
Abstract
The use of computational tools to identify biological targets of natural products with anticancer properties and unknown modes of action is gaining momentum. We employed self-organizing maps to deconvolute the phenotypic effects of piperlongumine (PL) and establish a link to modulation of the human transient receptor potential vanilloid 2 (hTRPV2) channel. The structure of the PL-bound full-length rat TRPV2 channel was determined by cryo-EM. PL binds to a transient allosteric pocket responsible for a new mode of anticancer activity against glioblastoma (GBM) in which hTRPV2 is overexpressed. Calcium imaging experiments revealed the importance of Arg539 and Thr522 residues on the antagonistic effect of PL and calcium influx modulation of the TRPV2 channel. Downregulation of hTRPV2 reduces sensitivity to PL and decreases ROS production. Analysis of GBM patient samples associates hTRPV2 overexpression with tumor grade, disease progression, and poor prognosis. Extensive tumor abrogation and long term survival was achieved in two murine models of orthotopic GBM by formulating PL in an implantable scaffold/hydrogel for sustained local therapy. Furthermore, in primary tumor samples derived from GBM patients, we observed a selective reduction of malignant cells in response to PL ex vivo. Our results establish a broadly applicable strategy, leveraging data-motivated research hypotheses for the discovery of novel means tackling cancer.
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Affiliation(s)
- João Conde
- Instituto
de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Av. Prof. Egas Moniz, 1649-028 Lisboa, Portugal
| | - Ruth A. Pumroy
- Department
of Systems Pharmacology and Translational Therapeutics, Perelman School
of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - Charlotte Baker
- Instituto
de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Av. Prof. Egas Moniz, 1649-028 Lisboa, Portugal
| | - Tiago Rodrigues
- Instituto
de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Av. Prof. Egas Moniz, 1649-028 Lisboa, Portugal
| | - Ana Guerreiro
- Instituto
de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Av. Prof. Egas Moniz, 1649-028 Lisboa, Portugal
| | - Bárbara B. Sousa
- Instituto
de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Av. Prof. Egas Moniz, 1649-028 Lisboa, Portugal
| | - Marta C. Marques
- Instituto
de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Av. Prof. Egas Moniz, 1649-028 Lisboa, Portugal
| | - Bernardo P. de Almeida
- Instituto
de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Av. Prof. Egas Moniz, 1649-028 Lisboa, Portugal
| | - Sohyon Lee
- Institute
of Molecular Systems Biology, ETH Zürich, Otto-Stern-Weg 3, 8093 Zürich, Switzerland
| | - Elvira P. Leites
- Instituto
de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Av. Prof. Egas Moniz, 1649-028 Lisboa, Portugal
| | - Daniel Picard
- Department
of Pediatric Neuro-Oncogenomics, DKFZ, Heidelberg 69120, Germany
- Department of Pediatric Neuro-Oncogenomics, DKTK, Essen D-45147, Germany
- Department
of Pediatric Oncology, Hematology, and Clinical Immunology, Medical
Faculty, University Hospital Düsseldorf, Düsseldorf 40225, Germany
| | - Amrita Samanta
- Department
of Systems Pharmacology and Translational Therapeutics, Perelman School
of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - Sandra H. Vaz
- Instituto
de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Av. Prof. Egas Moniz, 1649-028 Lisboa, Portugal
| | - Florian Sieglitz
- Instituto
de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Av. Prof. Egas Moniz, 1649-028 Lisboa, Portugal
| | - Maike Langini
- Department
of Pediatric Neuro-Oncogenomics, DKFZ, Heidelberg 69120, Germany
- Department of Pediatric Neuro-Oncogenomics, DKTK, Essen D-45147, Germany
- Department
of Pediatric Oncology, Hematology, and Clinical Immunology, Medical
Faculty, University Hospital Düsseldorf, Düsseldorf 40225, Germany
| | - Marc Remke
- Department
of Pediatric Neuro-Oncogenomics, DKFZ, Heidelberg 69120, Germany
- Department of Pediatric Neuro-Oncogenomics, DKTK, Essen D-45147, Germany
- Department
of Pediatric Oncology, Hematology, and Clinical Immunology, Medical
Faculty, University Hospital Düsseldorf, Düsseldorf 40225, Germany
| | - Rafael Roque
- Laboratório
de Neuropatologia, Hospital de Santa Maria, Centro Hospitalar Universitário Lisboa Norte (CHLN) EPE, Av. Prof. Egas Moniz, 1649-028 Lisboa, Portugal
| | - Tobias Weiss
- Department
of Neurology and Brain Tumour Center, University
Hospital Zürich and University of Zurich, Rämistrasse 100, 8091 Zürich, Switzerland
| | - Michael Weller
- Department
of Neurology and Brain Tumour Center, University
Hospital Zürich and University of Zurich, Rämistrasse 100, 8091 Zürich, Switzerland
| | - Yuhang Liu
- Discovery
Sciences, Worldwide Research and Development, Pfizer Inc., Eastern Point Road, Groton, Connecticut 06340, United States
| | - Seungil Han
- Discovery
Sciences, Worldwide Research and Development, Pfizer Inc., Eastern Point Road, Groton, Connecticut 06340, United States
| | - Francisco Corzana
- Departamento
de Química, Universidad de La Rioja, 26006 Logroño, Spain
| | - Vanessa A. Morais
- Instituto
de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Av. Prof. Egas Moniz, 1649-028 Lisboa, Portugal
| | - Cláudia
C. Faria
- Instituto
de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Av. Prof. Egas Moniz, 1649-028 Lisboa, Portugal
- Department
of Neurosurgery, Hospital de Santa Maria, Centro Hospitalar Universitário Lisboa Norte (CHULN) EPE, Av. Prof. Egas Moniz, 1649-028 Lisboa, Portugal
| | - Tânia Carvalho
- Instituto
de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Av. Prof. Egas Moniz, 1649-028 Lisboa, Portugal
| | - Panagis Filippakopoulos
- Structural
Genomics Consortium, Oxford University, Old Road Campus Research Building,
Roosevelt Drive, OX3 7DQ Oxford, United Kingdom
| | - Berend Snijder
- Institute
of Molecular Systems Biology, ETH Zürich, Otto-Stern-Weg 3, 8093 Zürich, Switzerland
| | - Nuno L. Barbosa-Morais
- Instituto
de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Av. Prof. Egas Moniz, 1649-028 Lisboa, Portugal
| | - Vera Y. Moiseenkova-Bell
- Department
of Systems Pharmacology and Translational Therapeutics, Perelman School
of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
- E-mail:
| | - Gonçalo J. L. Bernardes
- Instituto
de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Av. Prof. Egas Moniz, 1649-028 Lisboa, Portugal
- Yusuf
Hamied Department of Chemistry, University
of Cambridge, Lensfield Road, CB2 1EW Cambridge, United Kingdom
- E-mail: ;
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17
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Vatansever S, Schlessinger A, Wacker D, Kaniskan HÜ, Jin J, Zhou M, Zhang B. Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions. Med Res Rev 2021; 41:1427-1473. [PMID: 33295676 PMCID: PMC8043990 DOI: 10.1002/med.21764] [Citation(s) in RCA: 101] [Impact Index Per Article: 33.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 10/30/2020] [Accepted: 11/20/2020] [Indexed: 01/11/2023]
Abstract
Neurological disorders significantly outnumber diseases in other therapeutic areas. However, developing drugs for central nervous system (CNS) disorders remains the most challenging area in drug discovery, accompanied with the long timelines and high attrition rates. With the rapid growth of biomedical data enabled by advanced experimental technologies, artificial intelligence (AI) and machine learning (ML) have emerged as an indispensable tool to draw meaningful insights and improve decision making in drug discovery. Thanks to the advancements in AI and ML algorithms, now the AI/ML-driven solutions have an unprecedented potential to accelerate the process of CNS drug discovery with better success rate. In this review, we comprehensively summarize AI/ML-powered pharmaceutical discovery efforts and their implementations in the CNS area. After introducing the AI/ML models as well as the conceptualization and data preparation, we outline the applications of AI/ML technologies to several key procedures in drug discovery, including target identification, compound screening, hit/lead generation and optimization, drug response and synergy prediction, de novo drug design, and drug repurposing. We review the current state-of-the-art of AI/ML-guided CNS drug discovery, focusing on blood-brain barrier permeability prediction and implementation into therapeutic discovery for neurological diseases. Finally, we discuss the major challenges and limitations of current approaches and possible future directions that may provide resolutions to these difficulties.
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Affiliation(s)
- Sezen Vatansever
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Transformative Disease ModelingIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Icahn Institute for Data Science and Genomic TechnologyIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Avner Schlessinger
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Therapeutics DiscoveryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Daniel Wacker
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Therapeutics DiscoveryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of NeuroscienceIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - H. Ümit Kaniskan
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Therapeutics DiscoveryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Oncological Sciences, Tisch Cancer InstituteIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Jian Jin
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Therapeutics DiscoveryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Oncological Sciences, Tisch Cancer InstituteIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Ming‐Ming Zhou
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Oncological Sciences, Tisch Cancer InstituteIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Bin Zhang
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Transformative Disease ModelingIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Icahn Institute for Data Science and Genomic TechnologyIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
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18
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Assani I, Du Y, Wang CG, Chen L, Hou PL, Zhao SF, Feng Y, Liu LF, Sun B, Li Y, Liao ZX, Huang RZ. Anti-proliferative effects of diterpenoids from Sagittaria trifolia L. tubers on colon cancer cells by targeting the NF-κB pathway. Food Funct 2021; 11:7717-7726. [PMID: 32789317 DOI: 10.1039/d0fo00228c] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
A new labdane-type diterpenoid, ent-19-ol-13-epi-manoyl oxide,19-undecane ester, together with ten known diterpenes, were isolated from the ethanolic crude extract of the fresh tubers of Sagittaria trifolia L. The chemical structures of these compounds were determined by extensive 2-D NMR experiments and by comparison with the data reported in the literature. These compounds showed different inhibitory effects on various human cancer cells. Among these, compound 11 exhibited potential inhibition effects against human colon cancer cells. Moreover, flow cytometry demonstrated that compound 11 arrested the cell cycle at the G1 phase and induced cellular apoptosis, accompanied by mitochondrial membrane potential reduction. Mechanistic studies revealed that treatment with compound 11 inhibited IKKα/β phosphorylation and IκBα phosphorylation, which subsequently caused the blockage of NF-κB p65 phosphorylation and nuclear translocation. Compound 11 also inhibited the expression of c-Myc, Cyclin D1, and Bcl-2, the downstream targets of NF-κB. Therefore, our findings provided insight into the anticancer components of Sagittaria trifolia L. tubers, which could facilitate their utilization as functional food ingredients.
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Affiliation(s)
- Israa Assani
- Department of Pharmaceutical Engineering, School of Chemistry and Chemical Engineering, Jiangsu Province Hi-Tech Key Laboratory for Biomedical Research, Southeast University, Nanjing 211189, China.
| | - Ying Du
- Department of Pharmaceutical Engineering, School of Chemistry and Chemical Engineering, Jiangsu Province Hi-Tech Key Laboratory for Biomedical Research, Southeast University, Nanjing 211189, China.
| | - Chun-Gu Wang
- Department of Pharmaceutical Engineering, School of Chemistry and Chemical Engineering, Jiangsu Province Hi-Tech Key Laboratory for Biomedical Research, Southeast University, Nanjing 211189, China.
| | - Lei Chen
- Department of Pharmaceutical Engineering, School of Chemistry and Chemical Engineering, Jiangsu Province Hi-Tech Key Laboratory for Biomedical Research, Southeast University, Nanjing 211189, China.
| | - Pei-Lei Hou
- Department of Pharmaceutical Engineering, School of Chemistry and Chemical Engineering, Jiangsu Province Hi-Tech Key Laboratory for Biomedical Research, Southeast University, Nanjing 211189, China.
| | - Shi-Feng Zhao
- Department of Pharmaceutical Engineering, School of Chemistry and Chemical Engineering, Jiangsu Province Hi-Tech Key Laboratory for Biomedical Research, Southeast University, Nanjing 211189, China.
| | - Yan Feng
- Department of Pharmaceutical Engineering, School of Chemistry and Chemical Engineering, Jiangsu Province Hi-Tech Key Laboratory for Biomedical Research, Southeast University, Nanjing 211189, China.
| | - Ling-Fei Liu
- Department of Pharmaceutical Engineering, School of Chemistry and Chemical Engineering, Jiangsu Province Hi-Tech Key Laboratory for Biomedical Research, Southeast University, Nanjing 211189, China.
| | - Bo Sun
- Department of Pharmaceutical Engineering, School of Chemistry and Chemical Engineering, Jiangsu Province Hi-Tech Key Laboratory for Biomedical Research, Southeast University, Nanjing 211189, China.
| | - Yan Li
- Department of Pharmaceutical Engineering, School of Chemistry and Chemical Engineering, Jiangsu Province Hi-Tech Key Laboratory for Biomedical Research, Southeast University, Nanjing 211189, China.
| | - Zhi-Xin Liao
- Department of Pharmaceutical Engineering, School of Chemistry and Chemical Engineering, Jiangsu Province Hi-Tech Key Laboratory for Biomedical Research, Southeast University, Nanjing 211189, China.
| | - Ri-Zhen Huang
- College of Biotechnology, Guilin Medical University, Guilin 541100, China.
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19
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Chen Y, Kirchmair J. Cheminformatics in Natural Product-based Drug Discovery. Mol Inform 2020; 39:e2000171. [PMID: 32725781 PMCID: PMC7757247 DOI: 10.1002/minf.202000171] [Citation(s) in RCA: 58] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Accepted: 07/28/2020] [Indexed: 12/20/2022]
Abstract
This review seeks to provide a timely survey of the scope and limitations of cheminformatics methods in natural product-based drug discovery. Following an overview of data resources of chemical, biological and structural information on natural products, we discuss, among other aspects, in silico methods for (i) data curation and natural products dereplication, (ii) analysis, visualization, navigation and comparison of the chemical space, (iii) quantification of natural product-likeness, (iv) prediction of the bioactivities (virtual screening, target prediction), ADME and safety profiles (toxicity) of natural products, (v) natural products-inspired de novo design and (vi) prediction of natural products prone to cause interference with biological assays. Among the many methods discussed are rule-based, similarity-based, shape-based, pharmacophore-based and network-based approaches, docking and machine learning methods.
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Affiliation(s)
- Ya Chen
- Center for Bioinformatics (ZBH)Department of Computer ScienceFaculty of MathematicsInformatics and Natural SciencesUniversität Hamburg20146HamburgGermany
| | - Johannes Kirchmair
- Center for Bioinformatics (ZBH)Department of Computer ScienceFaculty of MathematicsInformatics and Natural SciencesUniversität Hamburg20146HamburgGermany
- Department of Pharmaceutical ChemistryFaculty of Life SciencesUniversity of Vienna1090ViennaAustria
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20
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Wei H, Guan YD, Zhang LX, Liu S, Lu AP, Cheng Y, Cao DS. A combinatorial target screening strategy for deorphaning macromolecular targets of natural product. Eur J Med Chem 2020; 204:112644. [PMID: 32738412 DOI: 10.1016/j.ejmech.2020.112644] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2020] [Revised: 06/02/2020] [Accepted: 07/02/2020] [Indexed: 11/24/2022]
Abstract
Natural products, as an ideal starting point for molecular design, play a pivotal role in drug discovery; however, ambiguous targets and mechanisms have limited their in-depth research and applications in a global dimension. In-silico target prediction methods have become an alternative to target identification experiments due to the high accuracy and speed, but most studies only use a single prediction method, which may reduce the accuracy and reliability of the prediction. Here, we firstly presented a combinatorial target screening strategy to facilitate multi-target screening of natural products considering the characteristics of diverse in-silico target prediction methods, which consists of ligand-based online approaches, consensus SAR modelling and target-specific re-scoring function modelling. To validate the practicability of the strategy, natural product neferine, a bisbenzylisoquinoline alkaloid isolated from the lotus seed, was taken as an example to illustrate the screening process and a series of corresponding experiments were implemented to explore the pharmacological mechanisms of neferine. The proposed computational method could be used for a complementary hypothesis generation and rapid analysis of potential targets of natural products.
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Affiliation(s)
- Hui Wei
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410013, Hunan, PR China
| | - Yi-Di Guan
- Xiangya Hospital, Central South University, Changsha, 410013, Hunan, PR China
| | - Liu-Xia Zhang
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410013, Hunan, PR China
| | - Shao Liu
- Xiangya Hospital, Central South University, Changsha, 410013, Hunan, PR China
| | - Ai-Ping Lu
- Institute for Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, PR China
| | - Yan Cheng
- The Second Xiangya Hospital, Central South University, Changsha, 410013, Hunan, PR China.
| | - Dong-Sheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410013, Hunan, PR China; Institute for Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, PR China.
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21
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Yang S, Ye Q, Ding J, Yin, Lu A, Chen X, Hou T, Cao D. Current advances in ligand‐based target prediction. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2020. [DOI: 10.1002/wcms.1504] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Affiliation(s)
- Su‐Qing Yang
- Xiangya School of Pharmaceutical Sciences Central South University Changsha Hunan China
| | - Qing Ye
- College of Pharmaceutical Sciences Innovation Institute for Artificial Intelligence in Medicine, Zhejiang University Hangzhou, Zhejiang China
| | - Jun‐Jie Ding
- Beijing Institute of Pharmaceutical Chemistry Beijing China
| | - Yin
- Department of Dermatology, Hunan Engineering Research Center of Skin Health and Disease, Hunan Key Laboratory of Skin Cancer and Psoriasis, Xiangya Hospital Central South University Changsha Hunan China
| | - Ai‐Ping Lu
- Institute for Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine Hong Kong Baptist University Hong Kong China
| | - Xiang Chen
- Department of Dermatology, Hunan Engineering Research Center of Skin Health and Disease, Hunan Key Laboratory of Skin Cancer and Psoriasis, Xiangya Hospital Central South University Changsha Hunan China
| | - Ting‐Jun Hou
- College of Pharmaceutical Sciences Innovation Institute for Artificial Intelligence in Medicine, Zhejiang University Hangzhou, Zhejiang China
| | - Dong‐Sheng Cao
- Xiangya School of Pharmaceutical Sciences Central South University Changsha Hunan China
- Institute for Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine Hong Kong Baptist University Hong Kong China
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22
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Mayr F, Möller G, Garscha U, Fischer J, Rodríguez Castaño P, Inderbinen SG, Temml V, Waltenberger B, Schwaiger S, Hartmann RW, Gege C, Martens S, Odermatt A, Pandey AV, Werz O, Adamski J, Stuppner H, Schuster D. Finding New Molecular Targets of Familiar Natural Products Using In Silico Target Prediction. Int J Mol Sci 2020; 21:E7102. [PMID: 32993084 PMCID: PMC7582679 DOI: 10.3390/ijms21197102] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 09/19/2020] [Accepted: 09/21/2020] [Indexed: 12/01/2022] Open
Abstract
Natural products comprise a rich reservoir for innovative drug leads and are a constant source of bioactive compounds. To find pharmacological targets for new or already known natural products using modern computer-aided methods is a current endeavor in drug discovery. Nature's treasures, however, could be used more effectively. Yet, reliable pipelines for the large-scale target prediction of natural products are still rare. We developed an in silico workflow consisting of four independent, stand-alone target prediction tools and evaluated its performance on dihydrochalcones (DHCs)-a well-known class of natural products. Thereby, we revealed four previously unreported protein targets for DHCs, namely 5-lipoxygenase, cyclooxygenase-1, 17β-hydroxysteroid dehydrogenase 3, and aldo-keto reductase 1C3. Moreover, we provide a thorough strategy on how to perform computational target predictions and guidance on using the respective tools.
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Affiliation(s)
- Fabian Mayr
- Institute of Pharmacy/Pharmacognosy, Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innrain 80/82, 6020 Innsbruck, Austria; (F.M.); (V.T.); (B.W.); (S.S.); (H.S.)
| | - Gabriele Möller
- Research Unit Molecular Endocrinology and Metabolism, Helmholtz Zentrum München, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany; (G.M.); (J.A.)
| | - Ulrike Garscha
- Department of Pharmaceutical/Medicinal Chemistry, Institute of Pharmacy, University Greifswald, Friedrich-Ludwig-Jahn-Straße 17, 17489 Greifswald, Germany; (U.G.); (J.F.)
| | - Jana Fischer
- Department of Pharmaceutical/Medicinal Chemistry, Institute of Pharmacy, University Greifswald, Friedrich-Ludwig-Jahn-Straße 17, 17489 Greifswald, Germany; (U.G.); (J.F.)
| | - Patricia Rodríguez Castaño
- Pediatric Endocrinology, Diabetology and Metabolism, University Children’s Hospital Bern, Freiburgstrasse 15, 3010 Bern, Switzerland; (P.R.C.); (A.V.P.)
- Department of Biomedical Research, University of Bern, Freiburgstrasse 15, 3010 Bern, Switzerland
| | - Silvia G. Inderbinen
- Division of Molecular and Systems Toxicology, Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 50, 4056 Basel, Switzerland; (S.G.I.); (A.O.)
| | - Veronika Temml
- Institute of Pharmacy/Pharmacognosy, Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innrain 80/82, 6020 Innsbruck, Austria; (F.M.); (V.T.); (B.W.); (S.S.); (H.S.)
| | - Birgit Waltenberger
- Institute of Pharmacy/Pharmacognosy, Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innrain 80/82, 6020 Innsbruck, Austria; (F.M.); (V.T.); (B.W.); (S.S.); (H.S.)
| | - Stefan Schwaiger
- Institute of Pharmacy/Pharmacognosy, Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innrain 80/82, 6020 Innsbruck, Austria; (F.M.); (V.T.); (B.W.); (S.S.); (H.S.)
| | - Rolf W. Hartmann
- Helmholtz Institute of Pharmaceutical Research Saarland (HIPS), Department for Drug Design and Optimization, Campus E8.1, 66123 Saarbrücken, Germany;
- Saarland University, Pharmaceutical and Medicinal Chemistry, Campus E8.1, 66123 Saarbrücken, Germany
| | - Christian Gege
- University of Heidelberg, Institute of Pharmacy and Molecular Biotechnology (IPMB), Medicinal Chemistry, Im Neuenheimer Feld 364, 69120 Heidelberg, Germany;
| | - Stefan Martens
- Research and Innovation Centre, Fondazione Edmund Mach (FEM), Via Mach 1, 38010 San Michele all’Adige, Italy;
| | - Alex Odermatt
- Division of Molecular and Systems Toxicology, Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 50, 4056 Basel, Switzerland; (S.G.I.); (A.O.)
| | - Amit V. Pandey
- Pediatric Endocrinology, Diabetology and Metabolism, University Children’s Hospital Bern, Freiburgstrasse 15, 3010 Bern, Switzerland; (P.R.C.); (A.V.P.)
- Department of Biomedical Research, University of Bern, Freiburgstrasse 15, 3010 Bern, Switzerland
| | - Oliver Werz
- Department of Pharmaceutical/Medicinal Chemistry, Institute of Pharmacy, Friedrich-Schiller-University Jena, Philosophenweg 14, 07743 Jena, Germany;
| | - Jerzy Adamski
- Research Unit Molecular Endocrinology and Metabolism, Helmholtz Zentrum München, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany; (G.M.); (J.A.)
- Lehrstuhl für Experimentelle Genetik, Technische Universität München, Emil-Erlenmeyer-Forum 5, 85356 Freising-Weihenstephan, Germany
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 8 Medical Drive, Singapore 117597, Singapore
| | - Hermann Stuppner
- Institute of Pharmacy/Pharmacognosy, Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innrain 80/82, 6020 Innsbruck, Austria; (F.M.); (V.T.); (B.W.); (S.S.); (H.S.)
| | - Daniela Schuster
- Institute of Pharmacy, Department of Pharmaceutical and Medicinal Chemistry, Paracelsus Medical University Salzburg, Strubergasse 21, 5020 Salzburg, Austria
- Institute of Pharmacy/Pharmaceutical Chemistry, Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innrain 80/82, 6020 Innsbruck, Austria
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23
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Mahú I, Barateiro A, Rial-Pensado E, Martinéz-Sánchez N, Vaz SH, Cal PMSD, Jenkins B, Rodrigues T, Cordeiro C, Costa MF, Mendes R, Seixas E, Pereira MMA, Kubasova N, Gres V, Morris I, Temporão C, Olivares M, Sanz Y, Koulman A, Corzana F, Sebastião AM, López M, Bernardes GJL, Domingos AI. Brain-Sparing Sympathofacilitators Mitigate Obesity without Adverse Cardiovascular Effects. Cell Metab 2020; 31:1120-1135.e7. [PMID: 32402266 PMCID: PMC7671941 DOI: 10.1016/j.cmet.2020.04.013] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/15/2019] [Revised: 03/03/2020] [Accepted: 04/14/2020] [Indexed: 02/02/2023]
Abstract
Anti-obesity drugs in the amphetamine (AMPH) class act in the brain to reduce appetite and increase locomotion. They are also characterized by adverse cardiovascular effects with origin that, despite absence of any in vivo evidence, is attributed to a direct sympathomimetic action in the heart. Here, we show that the cardiac side effects of AMPH originate from the brain and can be circumvented by PEGylation (PEGyAMPH) to exclude its central action. PEGyAMPH does not enter the brain and facilitates SNS activity via theβ2-adrenoceptor, protecting mice against obesity by increasing lipolysis and thermogenesis, coupled to higher heat dissipation, which acts as an energy sink to increase energy expenditure without altering food intake or locomotor activity. Thus, we provide proof-of-principle for a novel class of exclusively peripheral anti-obesity sympathofacilitators that are devoid of any cardiovascular and brain-related side effects.
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Affiliation(s)
- Inês Mahú
- Obesity Laboratory, Instituto Gulbenkian de Ciência, Oeiras 2780-156, Portugal
| | - Andreia Barateiro
- Obesity Laboratory, Instituto Gulbenkian de Ciência, Oeiras 2780-156, Portugal; Neuron Glia Biology in Health and Disease, Research Institute for Medicines (iMed.ULisboa), Faculty of Pharmacy, Universidade de Lisboa, Lisbon 1649-028, Portugal
| | - Eva Rial-Pensado
- NeurObesity Group, Department of Physiology, CIMUS, University of Santiago de Compostela, Instituto de Investigación Sanitaria, Santiago de Compostela, A Coruña 15782, Spain
| | - Noelia Martinéz-Sánchez
- Department of Physiology, Anatomy and Genetics, University of Oxford, Parks Road, Oxford OX1 3PT, UK
| | - Sandra H Vaz
- Instituto de Medicina Molecular, João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Av. Prof., Egas Moniz, Lisbon 1649-028, Portugal; Instituto de Farmacologia e Neurociências, Faculdade de Medicina, Universidade de Lisboa, Av. Prof. Egas Moniz, Lisboa 1649-028, Portugal
| | - Pedro M S D Cal
- Instituto de Medicina Molecular, João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Av. Prof., Egas Moniz, Lisbon 1649-028, Portugal
| | - Benjamin Jenkins
- NIHR BRC Core Metabolomics and Lipidomics Laboratory, Wellcome Trust, MRL Institute of Metabolic Science, University of Cambridge, Pathology building Level 4, Addenbrooke's Hospital, Cambridge CB2 0QQ, UK
| | - Tiago Rodrigues
- Instituto de Medicina Molecular, João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Av. Prof., Egas Moniz, Lisbon 1649-028, Portugal
| | - Carlos Cordeiro
- Laboratório de FT-ICR e Espectrometria de Massa Estrutural, Faculdade de Ciências da Universidade de Lisboa, Lisbon 1749-016, Portugal
| | - Miguel F Costa
- Obesity Laboratory, Instituto Gulbenkian de Ciência, Oeiras 2780-156, Portugal; Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon 1049-001, Portugal
| | - Raquel Mendes
- Obesity Laboratory, Instituto Gulbenkian de Ciência, Oeiras 2780-156, Portugal
| | - Elsa Seixas
- Obesity Laboratory, Instituto Gulbenkian de Ciência, Oeiras 2780-156, Portugal
| | - Mafalda M A Pereira
- Obesity Laboratory, Instituto Gulbenkian de Ciência, Oeiras 2780-156, Portugal
| | - Nadiya Kubasova
- Obesity Laboratory, Instituto Gulbenkian de Ciência, Oeiras 2780-156, Portugal
| | - Vitka Gres
- Obesity Laboratory, Instituto Gulbenkian de Ciência, Oeiras 2780-156, Portugal
| | - Imogen Morris
- Obesity Laboratory, Instituto Gulbenkian de Ciência, Oeiras 2780-156, Portugal
| | - Carolina Temporão
- Obesity Laboratory, Instituto Gulbenkian de Ciência, Oeiras 2780-156, Portugal
| | - Marta Olivares
- Microbial Ecology, Nutrition & Health Research Unit, Institute of Agrochemistry and Food Technology, National Research Council, Valencia (IATA-CSIC), Catedratico Agustin Escardino 7, 46980, Paterna, Valencia, Spain
| | - Yolanda Sanz
- Microbial Ecology, Nutrition & Health Research Unit, Institute of Agrochemistry and Food Technology, National Research Council, Valencia (IATA-CSIC), Catedratico Agustin Escardino 7, 46980, Paterna, Valencia, Spain
| | - Albert Koulman
- NIHR BRC Core Metabolomics and Lipidomics Laboratory, Wellcome Trust, MRL Institute of Metabolic Science, University of Cambridge, Pathology building Level 4, Addenbrooke's Hospital, Cambridge CB2 0QQ, UK
| | - Francisco Corzana
- Departamento de Química, Universidad de La Rioja, Centro de Investigación en Síntesis Química, 26006 Logroño, Spain
| | - Ana M Sebastião
- Instituto de Medicina Molecular, João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Av. Prof., Egas Moniz, Lisbon 1649-028, Portugal; Instituto de Farmacologia e Neurociências, Faculdade de Medicina, Universidade de Lisboa, Av. Prof. Egas Moniz, Lisboa 1649-028, Portugal
| | - Miguel López
- NeurObesity Group, Department of Physiology, CIMUS, University of Santiago de Compostela, Instituto de Investigación Sanitaria, Santiago de Compostela, A Coruña 15782, Spain
| | - Gonçalo J L Bernardes
- Instituto de Medicina Molecular, João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Av. Prof., Egas Moniz, Lisbon 1649-028, Portugal; Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK.
| | - Ana I Domingos
- Department of Physiology, Anatomy and Genetics, University of Oxford, Parks Road, Oxford OX1 3PT, UK; Obesity Laboratory, Instituto Gulbenkian de Ciência, Oeiras 2780-156, Portugal; Howard Hughes Medical Institute, IGC, Oeiras, Portugal.
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24
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Chen Y, Mathai N, Kirchmair J. Scope of 3D Shape-Based Approaches in Predicting the Macromolecular Targets of Structurally Complex Small Molecules Including Natural Products and Macrocyclic Ligands. J Chem Inf Model 2020; 60:2858-2875. [PMID: 32368908 PMCID: PMC7312400 DOI: 10.1021/acs.jcim.0c00161] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
![]()
A plethora
of similarity-based, network-based, machine learning,
docking and hybrid approaches for predicting the macromolecular targets
of small molecules are available today and recognized as valuable
tools for providing guidance in early drug discovery. With the increasing
maturity of target prediction methods, researchers have started to
explore ways to expand their scope to more challenging molecules such
as structurally complex natural products and macrocyclic small molecules.
In this work, we systematically explore the capacity of an alignment-based
approach to identify the targets of structurally complex small molecules
(including large and flexible natural products and macrocyclic compounds)
based on the similarity of their 3D molecular shape to noncomplex
molecules (i.e., more conventional, “drug-like”, synthetic
compounds). For this analysis, query sets of 10 representative, structurally
complex molecules were compiled for each of the 28 pharmaceutically
relevant proteins. Subsequently, ROCS, a leading shape-based screening
engine, was utilized to generate rank-ordered lists of the potential
targets of the 28 × 10 queries according to the similarity of
their 3D molecular shapes with those of compounds from a knowledge
base of 272 640 noncomplex small molecules active on a total of 3642
different proteins. Four of the scores implemented in ROCS were explored
for target ranking, with the TanimotoCombo score consistently outperforming
all others. The score successfully recovered the targets of 30% and
41% of the 280 queries among the top-5 and top-20 positions, respectively.
For 24 out of the 28 investigated targets (86%), the method correctly
assigned the first rank (out of 3642) to the target of interest for
at least one of the 10 queries. The shape-based target prediction
approach showed remarkable robustness, with good success rates obtained
even for compounds that are clearly distinct from any of the ligands
present in the knowledge base. However, complex natural products and
macrocyclic compounds proved to be challenging even with this approach,
although cases of complete failure were recorded only for a small
number of targets.
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Affiliation(s)
- Ya Chen
- Center for Bioinformatics (ZBH), Department of Computer Science, Faculty of Mathematics, Informatics and Natural Sciences, Universität Hamburg, 20146 Hamburg, Germany
| | - Neann Mathai
- Department of Chemistry and Computational Biology Unit (CBU), University of Bergen, N-5020 Bergen, Norway
| | - Johannes Kirchmair
- Center for Bioinformatics (ZBH), Department of Computer Science, Faculty of Mathematics, Informatics and Natural Sciences, Universität Hamburg, 20146 Hamburg, Germany.,Department of Chemistry and Computational Biology Unit (CBU), University of Bergen, N-5020 Bergen, Norway.,Department of Pharmaceutical Chemistry, Faculty of Life Sciences, University of Vienna, 1090 Vienna, Austria
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25
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Lin X, Li X, Lin X. A Review on Applications of Computational Methods in Drug Screening and Design. Molecules 2020; 25:E1375. [PMID: 32197324 PMCID: PMC7144386 DOI: 10.3390/molecules25061375] [Citation(s) in RCA: 230] [Impact Index Per Article: 57.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Revised: 03/16/2020] [Accepted: 03/16/2020] [Indexed: 12/27/2022] Open
Abstract
Drug development is one of the most significant processes in the pharmaceutical industry. Various computational methods have dramatically reduced the time and cost of drug discovery. In this review, we firstly discussed roles of multiscale biomolecular simulations in identifying drug binding sites on the target macromolecule and elucidating drug action mechanisms. Then, virtual screening methods (e.g., molecular docking, pharmacophore modeling, and QSAR) as well as structure- and ligand-based classical/de novo drug design were introduced and discussed. Last, we explored the development of machine learning methods and their applications in aforementioned computational methods to speed up the drug discovery process. Also, several application examples of combining various methods was discussed. A combination of different methods to jointly solve the tough problem at different scales and dimensions will be an inevitable trend in drug screening and design.
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Affiliation(s)
- Xiaoqian Lin
- Institute of Single Cell Engineering, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing 100191, China;
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Xiu Li
- School of Chemistry and Material Science, Shanxi Normal University, Linfen 041004, China;
| | - Xubo Lin
- Institute of Single Cell Engineering, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing 100191, China;
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
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26
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Friedrich L, Byrne R, Treder A, Singh I, Bauer C, Gudermann T, Mederos Y Schnitzler M, Storch U, Schneider G. Shape Similarity by Fractal Dimensionality: An Application in the de novo Design of (-)-Englerin A Mimetics. ChemMedChem 2020; 15:566-570. [PMID: 32162837 DOI: 10.1002/cmdc.202000017] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Revised: 02/09/2020] [Indexed: 12/22/2022]
Abstract
Molecular shape and pharmacological function are interconnected. To capture shape, the fractal dimensionality concept was employed, providing a natural similarity measure for the virtual screening of de novo generated small molecules mimicking the structurally complex natural product (-)-englerin A. Two of the top-ranking designs were synthesized and tested for their ability to modulate transient receptor potential (TRP) cation channels which are cellular targets of (-)-englerin A. Intracellular calcium assays and electrophysiological whole-cell measurements of TRPC4 and TRPM8 channels revealed potent inhibitory effects of one of the computer-generated compounds. Four derivatives of this identified hit compound had comparable effects on TRPC4 and TRPM8. The results of this study corroborate the use of fractal dimensionality as an innovative shape-based molecular representation for molecular scaffold-hopping.
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Affiliation(s)
- Lukas Friedrich
- Department of Chemistry and Applied Biosciences, Swiss Federal Institute of Technology (ETH), Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Ryan Byrne
- Department of Chemistry and Applied Biosciences, Swiss Federal Institute of Technology (ETH), Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Aaron Treder
- Walther Straub Institute of Pharmacology and Toxicology, Ludwig Maximilians University of Munich, Goethestrasse 33, 80336, Munich, Germany
| | - Inderjeet Singh
- Walther Straub Institute of Pharmacology and Toxicology, Ludwig Maximilians University of Munich, Goethestrasse 33, 80336, Munich, Germany
| | - Christoph Bauer
- Department of Chemistry and Applied Biosciences, Swiss Federal Institute of Technology (ETH), Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Thomas Gudermann
- Walther Straub Institute of Pharmacology and Toxicology, Ludwig Maximilians University of Munich, Goethestrasse 33, 80336, Munich, Germany.,DZHK (German Centre for Cardiovascular Research), Munich Heart Alliance, Biedersteiner Strasse 29, 80802, Munich, Germany.,Comprehensive Pneumology Center Munich (CPC-M), German Center for Lung Research, Max-Lebsche-Platz 31, 81377, Munich, Germany
| | - Michael Mederos Y Schnitzler
- Walther Straub Institute of Pharmacology and Toxicology, Ludwig Maximilians University of Munich, Goethestrasse 33, 80336, Munich, Germany.,DZHK (German Centre for Cardiovascular Research), Munich Heart Alliance, Biedersteiner Strasse 29, 80802, Munich, Germany
| | - Ursula Storch
- Walther Straub Institute of Pharmacology and Toxicology, Ludwig Maximilians University of Munich, Goethestrasse 33, 80336, Munich, Germany.,Institute for Cardiovascular Prevention (IPEK), Ludwig Maximilians University of Munich, Pettenkoferstrasse 8a & 9, 80336, Munich, Germany
| | - Gisbert Schneider
- Department of Chemistry and Applied Biosciences, Swiss Federal Institute of Technology (ETH), Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
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27
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Baldo F. Prediction of modes of action of components of traditional medicinal preparations. PHYSICAL SCIENCES REVIEWS 2020. [DOI: 10.1515/psr-2018-0115] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
AbstractTraditional medicine preparations are used to treat many ailments in multiple regions across the world. Despite their widespread use, the mode of action of these preparations and their constituents are not fully understood. Traditional methods of elucidating the modes of action of these natural products (NPs) can be expensive and time consuming e. g. biochemical methods, bioactivity guided fractionation, etc. In this review, we discuss some methods for the prediction of the modes of action of traditional medicine preparations, both in mixtures and as isolated NPs. These methods are useful to predict targets of NPs before they are experimentally validated. Case studies of the applications of these methods are also provided herein.
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28
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Rao Z, Jordan PM, Wang Y, Menche D, Pace S, Gerstmeier J, Werz O. Differential role of vacuolar (H +)-ATPase in the expression and activity of cyclooxygenase-2 in human monocytes. Biochem Pharmacol 2020; 175:113858. [PMID: 32061774 DOI: 10.1016/j.bcp.2020.113858] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Accepted: 02/11/2020] [Indexed: 10/25/2022]
Abstract
Monocytes are professional immune cells that produce abundant levels of pro-inflammatory eicosanoids including prostaglandins and leukotrienes during inflammation. Vacuolar (H+)-ATPase (V-ATPase) is critically involved in a variety of inflammatory processes including cytokine trafficking and lipid mediator biosynthesis. However, its role in eicosanoid biosynthetic pathways in monocytes remains elusive. Here, we present a differential role of V-ATPase in the expression and in the activity of cyclooxygenase (COX)-2 in human monocytes. Pharmacological targeting of V-ATPase increased the expression of COX-2 protein in lipopolysaccharide-stimulated primary monocytes, which was paralleled by enhanced phosphorylation of p38 MAPK and ERK-1/2, without impacting the NF-κB and SAPK/JNK pathways. Targeting of both p38 MAPK and ERK-1/2 pathways showed that the kinase pathways are crucial for COX-2 expression in human monocytes. Despite increased COX-2 protein levels, however, suppression of V-ATPase activity impaired the biosynthesis of COX- and also of 5-lipoxygenase (LOX)-derived lipid mediators in monocytes without affecting 12-/15-LOX products, assessed by a metabololipidomics approach using UPLC-MS-MS analysis. Our results indicate that changes in the intracellular pH may contribute to suppression of COX-2 and 5-LOX activities. We suggest that V-ATPase on one hand limits COX-2 protein levels via restricting p38 MAPK and ERK-1/2 activation, while on the other hand it governs the cellular activity of COX-2 through appropriate adjustment of the intracellular pH.
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Affiliation(s)
- Zhigang Rao
- Department of Pharmaceutical/Medicinal Chemistry, Institute of Pharmacy, Friedrich-Schiller-University Jena, Philosophenweg 14, D-07743 Jena, Germany; Michael Popp Research Institute, University of Innsbruck, Mitterweg 24, 6120, Innsbruck, Austria.
| | - Paul M Jordan
- Department of Pharmaceutical/Medicinal Chemistry, Institute of Pharmacy, Friedrich-Schiller-University Jena, Philosophenweg 14, D-07743 Jena, Germany.
| | - Yan Wang
- Department of Pharmaceutical/Medicinal Chemistry, Institute of Pharmacy, Friedrich-Schiller-University Jena, Philosophenweg 14, D-07743 Jena, Germany
| | - Dirk Menche
- Kekulé-Institut für Organische Chemie und Biochemie der Rheinischen Friedrich-Wilhelms-Universität Bonn, D-53121 Bonn, Germany.
| | - Simona Pace
- Department of Pharmaceutical/Medicinal Chemistry, Institute of Pharmacy, Friedrich-Schiller-University Jena, Philosophenweg 14, D-07743 Jena, Germany.
| | - Jana Gerstmeier
- Department of Pharmaceutical/Medicinal Chemistry, Institute of Pharmacy, Friedrich-Schiller-University Jena, Philosophenweg 14, D-07743 Jena, Germany.
| | - Oliver Werz
- Department of Pharmaceutical/Medicinal Chemistry, Institute of Pharmacy, Friedrich-Schiller-University Jena, Philosophenweg 14, D-07743 Jena, Germany.
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Scheeff S, Rivière S, Ruiz J, Abdelrahman A, Schulz-Fincke AC, Köse M, Tiburcy F, Wieczorek H, Gütschow M, Müller CE, Menche D. Synthesis of Novel Potent Archazolids: Pharmacology of an Emerging Class of Anticancer Drugs. J Med Chem 2020; 63:1684-1698. [DOI: 10.1021/acs.jmedchem.9b01887] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Affiliation(s)
- Stephan Scheeff
- Kekulé-Institut für Organische Chemie und Biochemie, Universität Bonn, Gerhard-Domagk-Str. 1, D-53121 Bonn, Germany
| | - Solenne Rivière
- Kekulé-Institut für Organische Chemie und Biochemie, Universität Bonn, Gerhard-Domagk-Str. 1, D-53121 Bonn, Germany
| | - Johal Ruiz
- Kekulé-Institut für Organische Chemie und Biochemie, Universität Bonn, Gerhard-Domagk-Str. 1, D-53121 Bonn, Germany
| | - Aliaa Abdelrahman
- Pharmazeutisches Institut, Universität Bonn, An der Immenburg 4, D-53121 Bonn, Germany
| | | | - Meryem Köse
- Pharmazeutisches Institut, Universität Bonn, An der Immenburg 4, D-53121 Bonn, Germany
| | - Felix Tiburcy
- Fachbereich Biologie/Chemie, Universität Osnabrück, D-49069 Osnabrück, Germany
| | - Helmut Wieczorek
- Fachbereich Biologie/Chemie, Universität Osnabrück, D-49069 Osnabrück, Germany
| | - Michael Gütschow
- Pharmazeutisches Institut, Universität Bonn, An der Immenburg 4, D-53121 Bonn, Germany
| | - Christa E. Müller
- Pharmazeutisches Institut, Universität Bonn, An der Immenburg 4, D-53121 Bonn, Germany
| | - Dirk Menche
- Kekulé-Institut für Organische Chemie und Biochemie, Universität Bonn, Gerhard-Domagk-Str. 1, D-53121 Bonn, Germany
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31
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Machine learning for target discovery in drug development. Curr Opin Chem Biol 2019; 56:16-22. [PMID: 31734566 DOI: 10.1016/j.cbpa.2019.10.003] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2019] [Revised: 10/01/2019] [Accepted: 10/03/2019] [Indexed: 12/15/2022]
Abstract
The discovery of macromolecular targets for bioactive agents is currently a bottleneck for the informed design of chemical probes and drug leads. Typically, activity profiling against genetically manipulated cell lines or chemical proteomics is pursued to shed light on their biology and deconvolute drug-target networks. By taking advantage of the ever-growing wealth of publicly available bioactivity data, learning algorithms now provide an attractive means to generate statistically motivated research hypotheses and thereby prioritize biochemical screens. Here, we highlight recent successes in machine intelligence for target identification and discuss challenges and opportunities for drug discovery.
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32
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Anand U, Jacobo-Herrera N, Altemimi A, Lakhssassi N. A Comprehensive Review on Medicinal Plants as Antimicrobial Therapeutics: Potential Avenues of Biocompatible Drug Discovery. Metabolites 2019; 9:E258. [PMID: 31683833 PMCID: PMC6918160 DOI: 10.3390/metabo9110258] [Citation(s) in RCA: 278] [Impact Index Per Article: 55.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Revised: 10/23/2019] [Accepted: 10/28/2019] [Indexed: 12/16/2022] Open
Abstract
The war on multidrug resistance (MDR) has resulted in the greatest loss to the world's economy. Antibiotics, the bedrock, and wonder drug of the 20th century have played a central role in treating infectious diseases. However, the inappropriate, irregular, and irrational uses of antibiotics have resulted in the emergence of antimicrobial resistance. This has resulted in an increased interest in medicinal plants since 30-50% of current pharmaceuticals and nutraceuticals are plant-derived. The question we address in this review is whether plants, which produce a rich diversity of secondary metabolites, may provide novel antibiotics to tackle MDR microbes and novel chemosensitizers to reclaim currently used antibiotics that have been rendered ineffective by the MDR microbes. Plants synthesize secondary metabolites and phytochemicals and have great potential to act as therapeutics. The main focus of this mini-review is to highlight the potential benefits of plant derived multiple compounds and the importance of phytochemicals for the development of biocompatible therapeutics. In addition, this review focuses on the diverse effects and efficacy of herbal compounds in controlling the development of MDR in microbes and hopes to inspire research into unexplored plants with a view to identify novel antibiotics for global health benefits.
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Affiliation(s)
- Uttpal Anand
- Department of Molecular and Cellular Engineering (MCE), Jacob Institute of Biotechnology and Bioengineering (JIBB), Sam Higginbottom University of Agriculture, Technology and Sciences, Prayagraj (Allahabad), Uttar Pradesh 211007, India.
| | - Nadia Jacobo-Herrera
- Unidad de Bioquímica, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán. Av. Vasco de Quiroga 15. Col. Belisario Domínguez Sección XVI. C.P. Tlalpan, Ciudad de México 14080, Mexico.
| | - Ammar Altemimi
- Department of Food Science, College of Agriculture, University of Basrah, Basrah 61004, Iraq.
| | - Naoufal Lakhssassi
- Department of Plant, Soil and Agricultural Systems, Southern Illinois University, Carbondale, IL 62901, USA.
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Cockroft NT, Cheng X, Fuchs JR. STarFish: A Stacked Ensemble Target Fishing Approach and its Application to Natural Products. J Chem Inf Model 2019; 59:4906-4920. [PMID: 31589422 DOI: 10.1021/acs.jcim.9b00489] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Target fishing is the process of identifying the protein target of a bioactive small molecule. To do so experimentally requires a significant investment of time and resources, which can be expedited with a reliable computational target fishing model. The development of computational target fishing models using machine learning has become very popular over the last several years because of the increased availability of large amounts of public bioactivity data. Unfortunately, the applicability and performance of such models for natural products has not yet been comprehensively assessed. This is, in part, due to the relative lack of bioactivity data available for natural products compared to synthetic compounds. Moreover, the databases commonly used to train such models do not annotate which compounds are natural products, which makes the collection of a benchmarking set difficult. To address this knowledge gap, a data set composed of natural product structures and their associated protein targets was generated by cross-referencing 20 publicly available natural product databases with the bioactivity database ChEMBL. This data set contains 5589 compound-target pairs for 1943 unique compounds and 1023 unique targets. A synthetic data set comprising 107 190 compound-target pairs for 88 728 unique compounds and 1907 unique targets was used to train k-nearest neighbors, random forest, and multilayer perceptron models. The predictive performance of each model was assessed by stratified 10-fold cross-validation and benchmarking on the newly collected natural product data set. Strong performance was observed for each model during cross-validation with area under the receiver operating characteristic (AUROC) scores ranging from 0.94 to 0.99 and Boltzmann-enhanced discrimination of receiver operating characteristic (BEDROC) scores from 0.89 to 0.94. When tested on the natural product data set, performance dramatically decreased with AUROC scores ranging from 0.70 to 0.85 and BEDROC scores from 0.43 to 0.59. However, the implementation of a model stacking approach, which uses logistic regression as a meta-classifier to combine model predictions, dramatically improved the ability to correctly predict the protein targets of natural products and increased the AUROC score to 0.94 and BEDROC score to 0.73. This stacked model was deployed as a web application, called STarFish, and has been made available for use to aid in target identification for natural products.
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Affiliation(s)
- Nicholas T Cockroft
- Division of Medicinal Chemistry & Pharmacognosy, College of Pharmacy , The Ohio State University , Columbus , Ohio 43210 , United States
| | - Xiaolin Cheng
- Division of Medicinal Chemistry & Pharmacognosy, College of Pharmacy , The Ohio State University , Columbus , Ohio 43210 , United States
| | - James R Fuchs
- Division of Medicinal Chemistry & Pharmacognosy, College of Pharmacy , The Ohio State University , Columbus , Ohio 43210 , United States
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Ouyang XL, Qin F, Huang RZ, Liang D, Wang CG, Wang HS, Liao ZX. NF-κB inhibitory and cytotoxic activities of hexacyclic triterpene acid constituents from Glechoma longituba. PHYTOMEDICINE : INTERNATIONAL JOURNAL OF PHYTOTHERAPY AND PHYTOPHARMACOLOGY 2019; 63:153037. [PMID: 31357075 DOI: 10.1016/j.phymed.2019.153037] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2019] [Revised: 07/13/2019] [Accepted: 07/19/2019] [Indexed: 06/10/2023]
Abstract
BACKGROUND Non-Small-Cell Lung Cancer (NSCLC) is the most-frequent cause of cancer death, and novel chemotherapeutic drugs for treating NSCLC are urgently needed. 2α, 3α, 23-trihydroxy-13α, 27-cyclours-11-en-28-oic acid (euscaphic acid G) is a new hexacyclic triterpene acid isolated by our group from Glechoma longituba (Nakai) Kupr. However, the underlying mechanisms responsible for the anticancer effects of hexacyclic triterpene acid have not been elucidated. PURPOSE In the present work, we evaluated growth inhibitory effect of the new isolated hexacyclic triterpene acid and explored the underlying molecular mechanisms. METHODS/STUDY DESIGNS Herbs were extracted and constituents were purified by chromatographic separation, including silica gel, ODS, MCI, Sephadex LH-20 and preparative HPLC. The compound structures were elucidated by the use of UV, NMR and MS spectral data. The anticancer activity of euscaphic acid G was evaluated by MTT assay. Cell cycle, apoptosis, reactive oxygen species and mitochondrial membrane potential were determined by flow cytometry. To display the possible mechanism of euscaphic acid G on NCI-H460 cells, RT-PCR, immunofluorescence and Western blot analysis were carried out. RESULTS A new hexacyclic triterpene acid, euscaphic acid G, together with fifteen known triterpenoids, was isolated from the aerial parts of G. longituba. Our results showed that euscaphic acid G exerted strong anti-proliferative activity against NCI-H460 cells in a concentration- and time-dependent manner. Flow cytometry demonstrated euscaphic acid G arrested the cell cycle at G1 phase, induced cellular apoptosis, accompanied by ROS generation and mitochondrial membrane potential reduction. Mechanistic studies revealed that euscaphic acid G treatment inhibited IKKα/β phosphorylation and IκBα phosphorylation, which subsequently caused the blockage of NF-κB p65 phosphorylation and nuclear translocation. CONCLUSION In conclusion, these results suggested that euscaphic acid G from G. longituba showed potential anticancer effects against lung cancer cells via inducing cell cycle arrest and apoptosis, at least partly, through NF-κB signaling pathways.
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Affiliation(s)
- Xi-Lin Ouyang
- State Key Laboratory for Chemistry and Molecular Engineering of Medicinal Resources, School of Chemistry and Pharmaceutical Science, Guangxi Normal University, Guilin, Guangxi, People's Republic of China; College of Public Health and Management, Youjiang Medical University for Nationalities, Baise, Guangxi, People's Republic of China
| | - Feng Qin
- State Key Laboratory for Chemistry and Molecular Engineering of Medicinal Resources, School of Chemistry and Pharmaceutical Science, Guangxi Normal University, Guilin, Guangxi, People's Republic of China
| | - Ri-Zhen Huang
- Pharmaceutical Research Center and School of Chemistry and Chemical Engineering, Southeast University, Nanjing, Jiangsu, People's Republic of China
| | - Dong Liang
- State Key Laboratory for Chemistry and Molecular Engineering of Medicinal Resources, School of Chemistry and Pharmaceutical Science, Guangxi Normal University, Guilin, Guangxi, People's Republic of China
| | - Chun-Gu Wang
- Pharmaceutical Research Center and School of Chemistry and Chemical Engineering, Southeast University, Nanjing, Jiangsu, People's Republic of China
| | - Heng-Shan Wang
- State Key Laboratory for Chemistry and Molecular Engineering of Medicinal Resources, School of Chemistry and Pharmaceutical Science, Guangxi Normal University, Guilin, Guangxi, People's Republic of China.
| | - Zhi-Xin Liao
- Pharmaceutical Research Center and School of Chemistry and Chemical Engineering, Southeast University, Nanjing, Jiangsu, People's Republic of China.
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Yang X, Wang Y, Byrne R, Schneider G, Yang S. Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery. Chem Rev 2019; 119:10520-10594. [PMID: 31294972 DOI: 10.1021/acs.chemrev.8b00728] [Citation(s) in RCA: 346] [Impact Index Per Article: 69.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides opportunities for the discovery and development of innovative drugs. Various machine learning approaches have recently (re)emerged, some of which may be considered instances of domain-specific AI which have been successfully employed for drug discovery and design. This review provides a comprehensive portrayal of these machine learning techniques and of their applications in medicinal chemistry. After introducing the basic principles, alongside some application notes, of the various machine learning algorithms, the current state-of-the art of AI-assisted pharmaceutical discovery is discussed, including applications in structure- and ligand-based virtual screening, de novo drug design, physicochemical and pharmacokinetic property prediction, drug repurposing, and related aspects. Finally, several challenges and limitations of the current methods are summarized, with a view to potential future directions for AI-assisted drug discovery and design.
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Affiliation(s)
- Xin Yang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital , Sichuan University , Chengdu , Sichuan 610041 , China
| | - Yifei Wang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital , Sichuan University , Chengdu , Sichuan 610041 , China
| | - Ryan Byrne
- ETH Zurich , Department of Chemistry and Applied Biosciences , Vladimir-Prelog-Weg 4 , CH-8093 Zurich , Switzerland
| | - Gisbert Schneider
- ETH Zurich , Department of Chemistry and Applied Biosciences , Vladimir-Prelog-Weg 4 , CH-8093 Zurich , Switzerland
| | - Shengyong Yang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital , Sichuan University , Chengdu , Sichuan 610041 , China
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Rodrigues T, de Almeida BP, Barbosa-Morais NL, Bernardes GJL. Dissecting celastrol with machine learning to unveil dark pharmacology. Chem Commun (Camb) 2019; 55:6369-6372. [PMID: 31089616 DOI: 10.1039/c9cc03116b] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
By coalescing bespoke machine learning and bioinformatics analyses with cell-based assays, we unveil the pharmacology of celastrol. Celastrol is a direct modulator of the progesterone and cannabinoid receptors, and its effects correlate with the antiproliferative activity. We demonstrate how in silico methods may drive systems biology studies for natural products.
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Affiliation(s)
- Tiago Rodrigues
- Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Av. Prof. Egas Moniz, 1649-028, Lisboa, Portugal.
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Kiefer A, Bader CD, Held J, Esser A, Rybniker J, Empting M, Müller R, Kazmaier U. Synthesis of New Cyclomarin Derivatives and Their Biological Evaluation towards
Mycobacterium Tuberculosis
and
Plasmodium Falciparum. Chemistry 2019; 25:8894-8902. [DOI: 10.1002/chem.201901640] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2019] [Indexed: 01/03/2023]
Affiliation(s)
- Alexander Kiefer
- Organic ChemistrySaarland University Campus C4.2 66123 Saarbrücken Germany
| | - Chantal D. Bader
- Department Microbial Natural Products (MINS)Helmholtz-Institute for Pharmaceutical Research Saarland (HIPS)–Helmholtz Centre for Infection Research (HZI) Campus E8.1 66123 Saarbrücken Germany
| | - Jana Held
- Department of Tropical MedicineUniversity of Tübingen Wilhelmstraße 27 72074 Tübingen Germany
| | - Anna Esser
- Center for Molecular Medicine CologneUniversity of Cologne Robert Koch Str. 21 50931 Cologne Germany
| | - Jan Rybniker
- Department I of Internal MedicineUniversity of Cologne 50937 Cologne (Germany) and German Center for Infection Research (DZIF), Partner Site Bonn-Cologne Germany
| | - Martin Empting
- Department of Drug Design and Optimization (DDOP)Helmholtz-Institute for Pharmaceutical Research Saarland, (HIPS)–Helmholtz Centre for Infection Research (HZI) Campus E8.1 66123 Saarbrücken Germany
| | - Rolf Müller
- Department Microbial Natural Products (MINS)Helmholtz-Institute for Pharmaceutical Research Saarland (HIPS)–Helmholtz Centre for Infection Research (HZI) Campus E8.1 66123 Saarbrücken Germany
- Department of PharmacySaarland University Campus E8.1 66123 Saarbrücken Germany
| | - Uli Kazmaier
- Organic ChemistrySaarland University Campus C4.2 66123 Saarbrücken Germany
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Reker D, Bernardes GJL, Rodrigues T. Computational advances in combating colloidal aggregation in drug discovery. Nat Chem 2019; 11:402-418. [PMID: 30988417 DOI: 10.1038/s41557-019-0234-9] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2018] [Accepted: 02/21/2019] [Indexed: 02/07/2023]
Abstract
Small molecule effectors are essential for drug discovery. Specific molecular recognition, reversible binding and dose-dependency are usually key requirements to ensure utility of a novel chemical entity. However, artefactual frequent-hitter and assay interference compounds may divert lead optimization and screening programmes towards attrition-prone chemical matter. Colloidal aggregates are the prime source of false positive readouts, either through protein sequestration or protein-scaffold mimicry. Nevertheless, assessment of colloidal aggregation remains somewhat overlooked and under-appreciated. In this Review, we discuss the impact of aggregation in drug discovery by analysing select examples from the literature and publicly-available datasets. We also examine and comment on technologies used to experimentally identify these potentially problematic entities. We focus on evidence-based computational filters and machine learning algorithms that may be swiftly deployed to flag chemical matter and mitigate the impact of aggregates in discovery programmes. We highlight the tools that can be used to scrutinize libraries, and identify and eliminate these problematic compounds.
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Affiliation(s)
- Daniel Reker
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA. .,Division of Gastroenterology, Hepatology and Endoscopy, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. .,MIT-IBM Watson AI Lab, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | - Gonçalo J L Bernardes
- Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, UK.,Instituto de Medicina Molecular, Faculdade de Medicina da Universidade de Lisboa, Lisboa, Portugal
| | - Tiago Rodrigues
- Instituto de Medicina Molecular, Faculdade de Medicina da Universidade de Lisboa, Lisboa, Portugal.
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Schneidewind T, Kapoor S, Garivet G, Karageorgis G, Narayan R, Vendrell-Navarro G, Antonchick AP, Ziegler S, Waldmann H. The Pseudo Natural Product Myokinasib Is a Myosin Light Chain Kinase 1 Inhibitor with Unprecedented Chemotype. Cell Chem Biol 2019; 26:512-523.e5. [DOI: 10.1016/j.chembiol.2018.11.014] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2016] [Revised: 09/14/2018] [Accepted: 11/26/2018] [Indexed: 12/20/2022]
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40
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Göbel T, Diehl O, Heering J, Merk D, Angioni C, Wittmann SK, Buscato EL, Kottke R, Weizel L, Schader T, Maier TJ, Geisslinger G, Schubert-Zsilavecz M, Steinhilber D, Proschak E, Kahnt AS. Zafirlukast Is a Dual Modulator of Human Soluble Epoxide Hydrolase and Peroxisome Proliferator-Activated Receptor γ. Front Pharmacol 2019; 10:263. [PMID: 30949053 PMCID: PMC6435570 DOI: 10.3389/fphar.2019.00263] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2018] [Accepted: 03/04/2019] [Indexed: 02/06/2023] Open
Abstract
Cysteinyl leukotriene receptor 1 antagonists (CysLT1RA) are frequently used as add-on medication for the treatment of asthma. Recently, these compounds have shown protective effects in cardiovascular diseases. This prompted us to investigate their influence on soluble epoxide hydrolase (sEH) and peroxisome proliferator activated receptor (PPAR) activities, two targets known to play an important role in CVD and the metabolic syndrome. Montelukast, pranlukast and zafirlukast inhibited human sEH with IC50 values of 1.9, 14.1, and 0.8 μM, respectively. In contrast, only montelukast and zafirlukast activated PPARγ in the reporter gene assay with EC50 values of 1.17 μM (21.9% max. activation) and 2.49 μM (148% max. activation), respectively. PPARα and δ were not affected by any of the compounds. The activation of PPARγ was further investigated in 3T3-L1 adipocytes. Analysis of lipid accumulation, mRNA and protein expression of target genes as well as PPARγ phosphorylation revealed that montelukast was not able to induce adipocyte differentiation. In contrast, zafirlukast triggered moderate lipid accumulation compared to rosiglitazone and upregulated PPARγ target genes. In addition, we found that montelukast and zafirlukast display antagonistic activities concerning recruitment of the PPARγ cofactor CBP upon ligand binding suggesting that both compounds act as PPARγ modulators. In addition, zafirlukast impaired the TNFα triggered phosphorylation of PPARγ2 on serine 273. Thus, zafirlukast is a novel dual sEH/PPARγ modulator representing an excellent starting point for the further development of this compound class.
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Affiliation(s)
- Tamara Göbel
- Institute of Pharmaceutical Chemistry/ZAFES, Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Olaf Diehl
- Institute of Pharmaceutical Chemistry/ZAFES, Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Jan Heering
- Branch for Translational Medicine and Pharmacology, Fraunhofer Institute for Molecular Biology and Applied Ecology IME, Frankfurt am Main, Germany
| | - Daniel Merk
- Institute of Pharmaceutical Chemistry/ZAFES, Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Carlo Angioni
- Faculty of Medicine, Institute of Clinical Pharmacology, Pharmazentrum Frankfurt, ZAFES, Frankfurt am Main, Germany
| | - Sandra K Wittmann
- Institute of Pharmaceutical Chemistry/ZAFES, Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Estel la Buscato
- Institute of Pharmaceutical Chemistry/ZAFES, Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Ramona Kottke
- Institute of Pharmaceutical Chemistry/ZAFES, Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Lilia Weizel
- Institute of Pharmaceutical Chemistry/ZAFES, Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Tim Schader
- Institute of Pharmaceutical Chemistry/ZAFES, Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Thorsten J Maier
- Department of Anesthesiology, Intensive Care Medicine and Pain Therapy, University Hospital Frankfurt, Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Gerd Geisslinger
- Branch for Translational Medicine and Pharmacology, Fraunhofer Institute for Molecular Biology and Applied Ecology IME, Frankfurt am Main, Germany.,Faculty of Medicine, Institute of Clinical Pharmacology, Pharmazentrum Frankfurt, ZAFES, Frankfurt am Main, Germany
| | | | - Dieter Steinhilber
- Institute of Pharmaceutical Chemistry/ZAFES, Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Ewgenij Proschak
- Institute of Pharmaceutical Chemistry/ZAFES, Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Astrid S Kahnt
- Institute of Pharmaceutical Chemistry/ZAFES, Goethe University Frankfurt, Frankfurt am Main, Germany
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Nogueira MS, Koch O. The Development of Target-Specific Machine Learning Models as Scoring Functions for Docking-Based Target Prediction. J Chem Inf Model 2019; 59:1238-1252. [DOI: 10.1021/acs.jcim.8b00773] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Mauro S. Nogueira
- Faculty of Chemistry and Chemical Biology, TU Dortmund University, Otto-Hahn-Straße 6, 44227, Dortmund, Germany
| | - Oliver Koch
- Faculty of Chemistry and Chemical Biology, TU Dortmund University, Otto-Hahn-Straße 6, 44227, Dortmund, Germany
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Chen Y, Stork C, Hirte S, Kirchmair J. NP-Scout: Machine Learning Approach for the Quantification and Visualization of the Natural Product-Likeness of Small Molecules. Biomolecules 2019; 9:biom9020043. [PMID: 30682850 PMCID: PMC6406893 DOI: 10.3390/biom9020043] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2018] [Revised: 01/21/2019] [Accepted: 01/21/2019] [Indexed: 01/11/2023] Open
Abstract
Natural products (NPs) remain the most prolific resource for the development of small-molecule drugs. Here we report a new machine learning approach that allows the identification of natural products with high accuracy. The method also generates similarity maps, which highlight atoms that contribute significantly to the classification of small molecules as a natural product or synthetic molecule. The method can hence be utilized to (i) identify natural products in large molecular libraries, (ii) quantify the natural product-likeness of small molecules, and (iii) visualize atoms in small molecules that are characteristic of natural products or synthetic molecules. The models are based on random forest classifiers trained on data sets consisting of more than 265,000 to 322,000 natural products and synthetic molecules. Two-dimensional molecular descriptors, MACCS keys and Morgan2 fingerprints were explored. On an independent test set the models reached areas under the receiver operating characteristic curve (AUC) of 0.997 and Matthews correlation coefficients (MCCs) of 0.954 and higher. The method was further tested on data from the Dictionary of Natural Products, ChEMBL and other resources. The best-performing models are accessible as a free web service at http://npscout.zbh.uni-hamburg.de/npscout.
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Affiliation(s)
- Ya Chen
- Center for Bioinformatics (ZBH), Department of Informatics, Faculty of Mathematics, Informatics and Natural Sciences, Universität Hamburg, 20146 Hamburg, Germany.
| | - Conrad Stork
- Center for Bioinformatics (ZBH), Department of Informatics, Faculty of Mathematics, Informatics and Natural Sciences, Universität Hamburg, 20146 Hamburg, Germany.
| | - Steffen Hirte
- Center for Bioinformatics (ZBH), Department of Informatics, Faculty of Mathematics, Informatics and Natural Sciences, Universität Hamburg, 20146 Hamburg, Germany.
| | - Johannes Kirchmair
- Center for Bioinformatics (ZBH), Department of Informatics, Faculty of Mathematics, Informatics and Natural Sciences, Universität Hamburg, 20146 Hamburg, Germany.
- Department of Chemistry, University of Bergen, 5007 Bergen, Norway.
- Computational Biology Unit (CBU), Department of Informatics, University of Bergen, 5008 Bergen, Norway.
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Vincent CT, Long ET, Jones HC, Young JC, Spiegel PC, O'Neil GW. Suzuki coupling-based synthesis of VATPase inhibitor archazolid natural product derived fragments. RSC Adv 2019; 9:32210-32218. [PMID: 35530773 PMCID: PMC9072946 DOI: 10.1039/c9ra07050h] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Revised: 10/29/2019] [Accepted: 10/02/2019] [Indexed: 11/21/2022] Open
Abstract
An archazolid natural product fragment that displays dose-dependent inhibition of the vacuolar-type ATPase (VATPase) has been synthesized by a high-yielding Suzuki coupling of two complex subunits. Similarly, a further simplified fragment was prepared and evaluated for VATPase inhibitory activity. This compound did inhibit the VATPase, as evidenced by growth inhibition of etiolated Arabidopsis seedlings, however at approximately 10× lower potency than the more complex fragment. Cyclooxygenase (COX) enzyme inhibition was not observed for either fragment. An archazolid natural product fragment that displays dose-dependent inhibition of the vacuolar-type ATPase (VATPase) has been synthesized by a high-yielding Suzuki coupling of two complex subunits.![]()
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Affiliation(s)
- Cooper T. Vincent
- Department of Chemistry
- Western Washington University
- Bellingham
- USA 98229
| | - Evan T. Long
- Department of Chemistry
- Western Washington University
- Bellingham
- USA 98229
| | - Holly C. Jones
- Department of Chemistry
- Western Washington University
- Bellingham
- USA 98229
| | - Jeffrey C. Young
- Department of Biology
- Western Washington University
- Bellingham
- USA 98229
| | - P. Clint Spiegel
- Department of Chemistry
- Western Washington University
- Bellingham
- USA 98229
| | - Gregory W. O'Neil
- Department of Chemistry
- Western Washington University
- Bellingham
- USA 98229
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A Strength-Weaknesses-Opportunities-Threats (SWOT) Analysis of Cheminformatics in Natural Product Research. PROGRESS IN THE CHEMISTRY OF ORGANIC NATURAL PRODUCTS 2019; 110:239-271. [PMID: 31621015 DOI: 10.1007/978-3-030-14632-0_7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Cheminformatics-based techniques, such as molecular modeling, docking, virtual screening, and machine learning, are well accepted for their usefulness in drug discovery and development of therapeutically relevant small molecules. Although delayed by several decades, their application in natural product research has led to outstanding findings. Combining information obtained from different sources, i.e., virtual predictions, traditional medicine, structural, biochemical, and biological data, and handling big data effectively will open up new possibilities, but also challenges in the future. Strategies and examples will be presented on how to integrate cheminformatics in pharmacognostic workflows to benefit from these two highly complementary disciplines toward streamlining experimental efforts. While considering their limits and pitfalls and by exploiting their potential, computer-aided strategies should successfully guide future studies and thereby augment our knowledge of bioactive natural lead structures.
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A Toolbox for the Identification of Modes of Action of Natural Products. PROGRESS IN THE CHEMISTRY OF ORGANIC NATURAL PRODUCTS 110 2019; 110:73-97. [DOI: 10.1007/978-3-030-14632-0_3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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Abstract
Abstract
The biological pre-validation of natural products (NPs) and their underlying frameworks ensures an unrivaled source of inspiration for chemical probe and drug design. However, the poor knowledge of their drug target counterparts critically hinders the broader exploration of NPs in chemical biology and molecular medicine. Cutting-edge algorithms now provide powerful means for the target deconvolution of phenotypic screen hits and generate motivated research hypotheses. Herein, we present recent progress in artificial intelligence applied to target identification that may accelerate future NP-inspired molecular medicine.
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Karageorgis G, Reckzeh ES, Ceballos J, Schwalfenberg M, Sievers S, Ostermann C, Pahl A, Ziegler S, Waldmann H. Chromopynones are pseudo natural product glucose uptake inhibitors targeting glucose transporters GLUT-1 and -3. Nat Chem 2018; 10:1103-1111. [DOI: 10.1038/s41557-018-0132-6] [Citation(s) in RCA: 65] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2018] [Accepted: 07/24/2018] [Indexed: 12/22/2022]
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Attiq A, Jalil J, Husain K, Ahmad W. Raging the War Against Inflammation With Natural Products. Front Pharmacol 2018; 9:976. [PMID: 30245627 PMCID: PMC6137277 DOI: 10.3389/fphar.2018.00976] [Citation(s) in RCA: 98] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2018] [Accepted: 08/08/2018] [Indexed: 12/31/2022] Open
Abstract
Over the last few decade Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) are the drugs of choice for treating numerous inflammatory diseases including rheumatoid arthritis. The NSAIDs produces anti-inflammatory activity via inhibiting cyclooxygenase enzyme, responsible for the conversation of arachidonic acid to prostaglandins. Likewise, cyclooxegenase-2 inhibitors (COX-2) selectively inhibit the COX-2 enzyme and produces significant anti-inflammatory, analgesic, and anti-pyretic activity without producing COX-1 associated gastrointestinal and renal side effects. In last two decades numerous selective COX-2 inhibitors (COXIBs) have been developed and approved for various inflammatory conditions. However, data from clinical trials have suggested that the prolong use of COX-2 inhibitors are also associated with life threatening cardiovascular side effects including ischemic heart failure and myocardial infection. In these scenario secondary metabolites from natural product offers a great hope for the development of novel anti-inflammatory compounds. Although majority of the natural product based compounds exhibit more selectively toward COX-1. However, the data suggest that slight structural modification can be helpful in developing COX-2 selective secondary metabolites with comparative efficacy and limited side effects. This review is an effort to highlight the secondary metabolites from terrestrial and marine source with significant COX-2 and COX-2 mediated PGE2 inhibitory activity, since it is anticipated that isolates with ability to inhibit COX-2 mediated PGE2 production would be useful in suppressing the inflammation and its classical sign and symptoms. Moreover, this review has highlighted the potential lead compounds including berberine, kaurenoic acid, α-cyperone, curcumin, and zedoarondiol for further development with the help of structure-activity relationship (SAR) studies and their current status.
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Affiliation(s)
- Ali Attiq
- Drug and Herbal Research Centre, Faculty of Pharmacy, University Kebangsaan Malaysia, Kuala Lumpur, Malaysia
| | - Juriyati Jalil
- Drug and Herbal Research Centre, Faculty of Pharmacy, University Kebangsaan Malaysia, Kuala Lumpur, Malaysia
| | - Khairana Husain
- Drug and Herbal Research Centre, Faculty of Pharmacy, University Kebangsaan Malaysia, Kuala Lumpur, Malaysia
| | - Waqas Ahmad
- School of Pharmaceutical Sciences, Universiti Sains Malaysia, Gelugor, Malaysia
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49
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Rodrigues T, Werner M, Roth J, da Cruz EHG, Marques MC, Akkapeddi P, Lobo SA, Koeberle A, Corzana F, da Silva Júnior EN, Werz O, Bernardes GJL. Machine intelligence decrypts β-lapachone as an allosteric 5-lipoxygenase inhibitor. Chem Sci 2018; 9:6899-6903. [PMID: 30310622 PMCID: PMC6138237 DOI: 10.1039/c8sc02634c] [Citation(s) in RCA: 49] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2018] [Accepted: 07/17/2018] [Indexed: 12/04/2022] Open
Abstract
Using machine learning, targets were identified for β-lapachone.
Using machine learning, targets were identified for β-lapachone. Resorting to biochemical assays, β-lapachone was validated as a potent, ligand efficient, allosteric and reversible modulator of 5-lipoxygenase (5-LO). Moreover, we provide a rationale for 5-LO modulation and show that inhibition of 5-LO is relevant for the anticancer activity of β-lapachone. This work demonstrates the power of machine intelligence to deconvolute complex phenotypes, as an alternative and/or complement to chemoproteomics and as a viable general approach for systems pharmacology studies.
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Affiliation(s)
- Tiago Rodrigues
- Instituto de Medicina Molecular , Faculdade de Medicina da Universidade de Lisboa , Av Prof Egaz Moniz , 1649-028 Lisboa , Portugal . ;
| | - Markus Werner
- Institute of Pharmacy , Friedrich-Schiller-University Jena , Philosophenweg 14 , D-07743 , Jena , Germany
| | - Jakob Roth
- Institute of Pharmacy , Friedrich-Schiller-University Jena , Philosophenweg 14 , D-07743 , Jena , Germany
| | - Eduardo H G da Cruz
- Institute of Exact Sciences , Department of Chemistry , Federal University of Minas Gerais , Belo Horizonte , Brazil
| | - Marta C Marques
- Instituto de Medicina Molecular , Faculdade de Medicina da Universidade de Lisboa , Av Prof Egaz Moniz , 1649-028 Lisboa , Portugal . ;
| | - Padma Akkapeddi
- Instituto de Medicina Molecular , Faculdade de Medicina da Universidade de Lisboa , Av Prof Egaz Moniz , 1649-028 Lisboa , Portugal . ;
| | - Susana A Lobo
- Instituto de Medicina Molecular , Faculdade de Medicina da Universidade de Lisboa , Av Prof Egaz Moniz , 1649-028 Lisboa , Portugal . ;
| | - Andreas Koeberle
- Institute of Pharmacy , Friedrich-Schiller-University Jena , Philosophenweg 14 , D-07743 , Jena , Germany
| | - Francisco Corzana
- Departamento de Química , Centro de Investigacíon en Síntesis Química , Universidad de la Rioja , 26006 Logroño , Spain
| | - Eufrânio N da Silva Júnior
- Institute of Exact Sciences , Department of Chemistry , Federal University of Minas Gerais , Belo Horizonte , Brazil
| | - Oliver Werz
- Institute of Pharmacy , Friedrich-Schiller-University Jena , Philosophenweg 14 , D-07743 , Jena , Germany
| | - Gonçalo J L Bernardes
- Instituto de Medicina Molecular , Faculdade de Medicina da Universidade de Lisboa , Av Prof Egaz Moniz , 1649-028 Lisboa , Portugal . ; .,Department of Chemistry , University of Cambridge , Lensfield Road , CB2 1EW Cambridge , UK .
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Chen Y, Garcia de Lomana M, Friedrich NO, Kirchmair J. Characterization of the Chemical Space of Known and Readily Obtainable Natural Products. J Chem Inf Model 2018; 58:1518-1532. [DOI: 10.1021/acs.jcim.8b00302] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Ya Chen
- Center for Bioinformatics, Department of Computer Science, Faculty of Mathematics, Informatics and Natural Sciences, Universität Hamburg, 20146 Hamburg, Germany
| | - Marina Garcia de Lomana
- Center for Bioinformatics, Department of Computer Science, Faculty of Mathematics, Informatics and Natural Sciences, Universität Hamburg, 20146 Hamburg, Germany
| | - Nils-Ole Friedrich
- Center for Bioinformatics, Department of Computer Science, Faculty of Mathematics, Informatics and Natural Sciences, Universität Hamburg, 20146 Hamburg, Germany
| | - Johannes Kirchmair
- Center for Bioinformatics, Department of Computer Science, Faculty of Mathematics, Informatics and Natural Sciences, Universität Hamburg, 20146 Hamburg, Germany
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