1
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Sindt F, Seyller A, Eguida M, Rognan D. Protein Structure-Based Organic Chemistry-Driven Ligand Design from Ultralarge Chemical Spaces. ACS CENTRAL SCIENCE 2024; 10:615-627. [PMID: 38559302 PMCID: PMC10979501 DOI: 10.1021/acscentsci.3c01521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 01/25/2024] [Accepted: 01/29/2024] [Indexed: 04/04/2024]
Abstract
Ultralarge chemical spaces describing several billion compounds are revolutionizing hit identification in early drug discovery. Because of their size, such chemical spaces cannot be fully enumerated and require ad-hoc computational tools to navigate them and pick potentially interesting hits. We here propose a structure-based approach to ultralarge chemical space screening in which commercial chemical reagents are first docked to the target of interest and then directly connected according to organic chemistry and topological rules, to enumerate drug-like compounds under three-dimensional constraints of the target. When applied to bespoke chemical spaces of different sizes and chemical complexity targeting two receptors of pharmaceutical interest (estrogen β receptor, dopamine D3 receptor), the computational method was able to quickly enumerate hits that were either known ligands (or very close analogs) of targeted receptors as well as chemically novel candidates that could be experimentally confirmed by in vitro binding assays. The proposed approach is generic, can be applied to any docking algorithm, and requires few computational resources to prioritize easily synthesizable hits from billion-sized chemical spaces.
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Affiliation(s)
- François Sindt
- Laboratoire d’innovation
thérapeutique, UMR7200 CNRS-Université de Strasbourg, Illkirch 67400, France
| | - Anthony Seyller
- Laboratoire d’innovation
thérapeutique, UMR7200 CNRS-Université de Strasbourg, Illkirch 67400, France
| | | | - Didier Rognan
- Laboratoire d’innovation
thérapeutique, UMR7200 CNRS-Université de Strasbourg, Illkirch 67400, France
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2
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Pikalyova R, Zabolotna Y, Horvath D, Marcou G, Varnek A. Chemical Library Space: Definition and DNA-Encoded Library Comparison Study Case. J Chem Inf Model 2023. [PMID: 37368824 DOI: 10.1021/acs.jcim.3c00520] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/29/2023]
Abstract
The development of DNA-encoded library (DEL) technology introduced new challenges for the analysis of chemical libraries. It is often useful to consider a chemical library as a stand-alone chemoinformatic object─represented both as a collection of independent molecules, and yet an individual entity─in particular, when they are inseparable mixtures, like DELs. Herein, we introduce the concept of chemical library space (CLS), in which resident items are individual chemical libraries. We define and compare four vectorial library representations obtained using generative topographic mapping. These allow for an effective comparison of libraries, with the ability to tune and chemically interpret the similarity relationships. In particular, property-tuned CLS encodings enable to simultaneously compare libraries with respect to both property and chemotype distributions. We apply the various CLS encodings for the selection problem of DELs that optimally "match" a reference collection (here ChEMBL28), showing how the choice of the CLS descriptors may help to fine-tune the "matching" (overlap) criteria. Hence, the proposed CLS may represent a new efficient way for polyvalent analysis of thousands of chemical libraries. Selection of an easily accessible compound collection for drug discovery, as a substitute for a difficult to produce reference library, can be tuned for either primary or target-focused screening, also considering property distributions of compounds. Alternatively, selection of libraries covering novel regions of the chemical space with respect to a reference compound subspace may serve for library portfolio enrichment.
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Affiliation(s)
- Regina Pikalyova
- Laboratory of Chemoinformatics, University of Strasbourg, 4, rue B. Pascal, Strasbourg 67081, France
| | - Yuliana Zabolotna
- Laboratory of Chemoinformatics, University of Strasbourg, 4, rue B. Pascal, Strasbourg 67081, France
| | - Dragos Horvath
- Laboratory of Chemoinformatics, University of Strasbourg, 4, rue B. Pascal, Strasbourg 67081, France
| | - Gilles Marcou
- Laboratory of Chemoinformatics, University of Strasbourg, 4, rue B. Pascal, Strasbourg 67081, France
| | - Alexandre Varnek
- Laboratory of Chemoinformatics, University of Strasbourg, 4, rue B. Pascal, Strasbourg 67081, France
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3
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Jones D, Allen JE, Yang Y, Drew Bennett WF, Gokhale M, Moshiri N, Rosing TS. Accelerators for Classical Molecular Dynamics Simulations of Biomolecules. J Chem Theory Comput 2022; 18:4047-4069. [PMID: 35710099 PMCID: PMC9281402 DOI: 10.1021/acs.jctc.1c01214] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Atomistic Molecular Dynamics (MD) simulations provide researchers the ability to model biomolecular structures such as proteins and their interactions with drug-like small molecules with greater spatiotemporal resolution than is otherwise possible using experimental methods. MD simulations are notoriously expensive computational endeavors that have traditionally required massive investment in specialized hardware to access biologically relevant spatiotemporal scales. Our goal is to summarize the fundamental algorithms that are employed in the literature to then highlight the challenges that have affected accelerator implementations in practice. We consider three broad categories of accelerators: Graphics Processing Units (GPUs), Field-Programmable Gate Arrays (FPGAs), and Application Specific Integrated Circuits (ASICs). These categories are comparatively studied to facilitate discussion of their relative trade-offs and to gain context for the current state of the art. We conclude by providing insights into the potential of emerging hardware platforms and algorithms for MD.
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Affiliation(s)
- Derek Jones
- Department of Computer Science and Engineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, California 92093, United States.,Global Security Computing Applications Division, Lawrence Livermore National Laboratory, 7000 East Avenue, Livermore, California 94550, United States
| | - Jonathan E Allen
- Global Security Computing Applications Division, Lawrence Livermore National Laboratory, 7000 East Avenue, Livermore, California 94550, United States
| | - Yue Yang
- Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory, 7000 East Avenue, Livermore, California 94550, United States
| | - William F Drew Bennett
- Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory, 7000 East Avenue, Livermore, California 94550, United States
| | - Maya Gokhale
- Center for Applied Scientific Computing, Lawrence Livermore National Laboratory, 7000 East Avenue, Livermore, California 94550, United States
| | - Niema Moshiri
- Department of Computer Science and Engineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, California 92093, United States
| | - Tajana S Rosing
- Department of Computer Science and Engineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, California 92093, United States
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4
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Zabolotna Y, Volochnyuk DM, Ryabukhin SV, Horvath D, Gavrilenko KS, Marcou G, Moroz YS, Oksiuta O, Varnek A. A Close-up Look at the Chemical Space of Commercially Available Building Blocks for Medicinal Chemistry. J Chem Inf Model 2021; 62:2171-2185. [PMID: 34928600 DOI: 10.1021/acs.jcim.1c00811] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The ability to efficiently synthesize desired compounds can be a limiting factor for chemical space exploration in drug discovery. This ability is conditioned not only by the existence of well-studied synthetic protocols but also by the availability of corresponding reagents, so-called building blocks (BBs). In this work, we present a detailed analysis of the chemical space of 400 000 purchasable BBs. The chemical space was defined by corresponding synthons─fragments contributed to the final molecules upon reaction. They allow an analysis of BB physicochemical properties and diversity, unbiased by the leaving and protective groups in actual reagents. The main classes of BBs were analyzed in terms of their availability, rule-of-two-defined quality, and diversity. Available BBs were eventually compared to a reference set of biologically relevant synthons derived from ChEMBL fragmentation, in order to illustrate how well they cover the actual medicinal chemistry needs. This was performed on a newly constructed universal generative topographic map of synthon chemical space that enables visualization of both libraries and analysis of their overlapped and library-specific regions.
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Affiliation(s)
- Yuliana Zabolotna
- University of Strasbourg, Laboratoire de Chemoinformatique, 4, rue B. Pascal, Strasbourg 67081, France
| | - Dmitriy M Volochnyuk
- Institute of Organic Chemistry, National Academy of Sciences of Ukraine, Murmanska Street 5, Kyiv 02660, Ukraine.,Enamine Ltd., 78 Chervonotkatska str., 02660 Kiev, Ukraine
| | - Sergey V Ryabukhin
- The Institute of High Technologies, Kyiv National Taras Shevchenko University, 64 Volodymyrska Street, Kyiv 01601, Ukraine.,Enamine Ltd., 78 Chervonotkatska str., 02660 Kiev, Ukraine
| | - Dragos Horvath
- University of Strasbourg, Laboratoire de Chemoinformatique, 4, rue B. Pascal, Strasbourg 67081, France
| | - Konstantin S Gavrilenko
- Research-And-Education ChemBioCenter, National Taras Shevchenko University of Kyiv, Chervonotkatska str., 61, 03022 Kiev, Ukraine.,Enamine Ltd., 78 Chervonotkatska str., 02660 Kiev, Ukraine
| | - Gilles Marcou
- University of Strasbourg, Laboratoire de Chemoinformatique, 4, rue B. Pascal, Strasbourg 67081, France
| | - Yurii S Moroz
- Research-And-Education ChemBioCenter, National Taras Shevchenko University of Kyiv, Chervonotkatska str., 61, 03022 Kiev, Ukraine.,Chemspace, Chervonotkatska Street 78, 02094 Kyiv, Ukraine
| | - Oleksandr Oksiuta
- Institute of Organic Chemistry, National Academy of Sciences of Ukraine, Murmanska Street 5, Kyiv 02660, Ukraine.,Chemspace, Chervonotkatska Street 78, 02094 Kyiv, Ukraine
| | - Alexandre Varnek
- University of Strasbourg, Laboratoire de Chemoinformatique, 4, rue B. Pascal, Strasbourg 67081, France.,Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Kita 21 Nishi 10, Kita-ku, 001-0021 Sapporo, Japan
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5
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Zabolotna Y, Ertl P, Horvath D, Bonachera F, Marcou G, Varnek A. NP Navigator: A New Look at the Natural Product Chemical Space. Mol Inform 2021; 40:e2100068. [PMID: 34170632 DOI: 10.1002/minf.202100068] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Accepted: 05/15/2021] [Indexed: 11/08/2022]
Abstract
Natural products (NPs), being evolutionary selected over millions of years to bind to biological macromolecules, remained an important source of inspiration for medicinal chemists even after the advent of efficient drug discovery technologies such as combinatorial chemistry and high-throughput screening. Thus, there is a strong demand for efficient and user-friendly computational tools that allow to analyze large libraries of NPs. In this context, we introduce NP Navigator - a freely available intuitive online tool for visualization and navigation through the chemical space of NPs and NP-like molecules. It is based on the hierarchical ensemble of generative topographic maps, featuring NPs from the COlleCtion of Open NatUral producTs (COCONUT), bioactive compounds from ChEMBL and commercially available molecules from ZINC. NP Navigator allows to efficiently analyze different aspects of NPs - chemotype distribution, physicochemical properties, biological activity and commercial availability of NPs. The latter concerns not only purchasable NPs but also their close analogs that can be considered as synthetic mimetics of NPs or pseudo-NPs.
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Affiliation(s)
- Yuliana Zabolotna
- University of Strasbourg, Laboratory of Chemoinformatics, 4, rue B. Pascal, 67081, Strasbourg, France
| | - Peter Ertl
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4056, Basel, Switzerland
| | - Dragos Horvath
- University of Strasbourg, Laboratory of Chemoinformatics, 4, rue B. Pascal, 67081, Strasbourg, France
| | - Fanny Bonachera
- University of Strasbourg, Laboratory of Chemoinformatics, 4, rue B. Pascal, 67081, Strasbourg, France
| | - Gilles Marcou
- University of Strasbourg, Laboratory of Chemoinformatics, 4, rue B. Pascal, 67081, Strasbourg, France
| | - Alexandre Varnek
- University of Strasbourg, Laboratory of Chemoinformatics, 4, rue B. Pascal, 67081, Strasbourg, France.,Institute for Chemical Reaction Design and Discovery, WPI-ICReDD), Hokkaido University, Kita 21 Nishi 10, Sapporo, Kita-ku, 001-0021 Sapporo, Japan
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6
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Martorell M, Lucas X, Alarcón-Zapata P, Capó X, Quetglas-Llabrés MM, Tejada S, Sureda A. Targeting Xanthine Oxidase by Natural Products as a Therapeutic Approach for Mental Disorders. Curr Pharm Des 2021; 27:367-382. [PMID: 32564744 DOI: 10.2174/1381612826666200621165839] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Accepted: 05/08/2020] [Indexed: 11/22/2022]
Abstract
Mental disorders comprise diverse human pathologies, including depression, bipolar affective disorder, schizophrenia, and dementia that affect millions of people around the world. The causes of mental disorders are unclear, but growing evidence suggests that oxidative stress and the purine/adenosine system play a key role in their development and progression. Xanthine oxidase (XO) is a flavoprotein enzyme essential for the catalysis of the oxidative hydroxylation of purines -hypoxanthine and xanthine- to generate uric acid. As a consequence of the oxidative reaction of XO, reactive oxygen species (ROS) such as superoxide and hydrogen peroxide are produced and, further, contribute to the pathogenesis of mental disorders. Altered XO activity has been associated with free radical-mediated neurotoxicity inducing cell damage and inflammation. Diverse studies reported a direct association between an increased activity of XO and diverse mental diseases including depression or schizophrenia. Small-molecule inhibitors, such as the well-known allopurinol, and dietary flavonoids, can modulate the XO activity and subsequent ROS production. In the present work, we review the available literature on XO inhibition by small molecules and their potential therapeutic application in mental disorders. In addition, we discuss the chemistry and molecular mechanism of XO inhibitors, as well as the use of structure-based and computational methods to design specific inhibitors with the capability of modulating XO activity.
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Affiliation(s)
- Miquel Martorell
- Department of Nutrition and Dietetics, Faculty of Pharmacy, and Centre for Healthy Living, University of Concepcion, 4070386 Concepcion, Chile
| | - Xavier Lucas
- Roche Pharma Research and Early Development, Roche Innovation Center, Basel CH-4070, Switzerland
| | - Pedro Alarcón-Zapata
- Clinical Biochemistry and Immunology Department, Faculty of Pharmacy, University of Concepcion, 4070386 Concepcion, Chile
| | - Xavier Capó
- Research Group in Community Nutrition and Oxidative Stress, University of Balearic Islands & Health Research Institute of the Balearic Islands (IdISBa), E-07122, Palma, Balearic Islands, Spain
| | - Maria Magdalena Quetglas-Llabrés
- Laboratory of Neurophysiology, Department of Biology, University of Balearic Islands & Health Research Institute of the Balearic Islands (IdISBa), E-07122, Palma, Balearic Islands, Spain
| | - Silvia Tejada
- Laboratory of Neurophysiology, Department of Biology, University of Balearic Islands & Health Research Institute of the Balearic Islands (IdISBa), E-07122, Palma, Balearic Islands, Spain
| | - Antoni Sureda
- Research Group in Community Nutrition and Oxidative Stress, University of Balearic Islands & Health Research Institute of the Balearic Islands (IdISBa), E-07122, Palma, Balearic Islands, Spain
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7
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Shrivastava AD, Kell DB. FragNet, a Contrastive Learning-Based Transformer Model for Clustering, Interpreting, Visualizing, and Navigating Chemical Space. Molecules 2021; 26:2065. [PMID: 33916824 PMCID: PMC8038408 DOI: 10.3390/molecules26072065] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 03/29/2021] [Accepted: 04/01/2021] [Indexed: 12/12/2022] Open
Abstract
The question of molecular similarity is core in cheminformatics and is usually assessed via a pairwise comparison based on vectors of properties or molecular fingerprints. We recently exploited variational autoencoders to embed 6M molecules in a chemical space, such that their (Euclidean) distance within the latent space so formed could be assessed within the framework of the entire molecular set. However, the standard objective function used did not seek to manipulate the latent space so as to cluster the molecules based on any perceived similarity. Using a set of some 160,000 molecules of biological relevance, we here bring together three modern elements of deep learning to create a novel and disentangled latent space, viz transformers, contrastive learning, and an embedded autoencoder. The effective dimensionality of the latent space was varied such that clear separation of individual types of molecules could be observed within individual dimensions of the latent space. The capacity of the network was such that many dimensions were not populated at all. As before, we assessed the utility of the representation by comparing clozapine with its near neighbors, and we also did the same for various antibiotics related to flucloxacillin. Transformers, especially when as here coupled with contrastive learning, effectively provide one-shot learning and lead to a successful and disentangled representation of molecular latent spaces that at once uses the entire training set in their construction while allowing "similar" molecules to cluster together in an effective and interpretable way.
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Affiliation(s)
- Aditya Divyakant Shrivastava
- Department of Computer Science and Engineering, Nirma University, Ahmedabad 382481, India;
- Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Crown St., Liverpool L69 7ZB, UK
| | - Douglas B. Kell
- Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Crown St., Liverpool L69 7ZB, UK
- Novo Nordisk Foundation Centre for Biosustainability, Technical University of Denmark, Building 220, Kemitorvet, 2800 Kgs Lyngby, Denmark
- Mellizyme Ltd., Liverpool Science Park, IC1, 131 Mount Pleasant, Liverpool L3 5TF, UK
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8
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Zabolotna Y, Lin A, Horvath D, Marcou G, Volochnyuk DM, Varnek A. Chemography: Searching for Hidden Treasures. J Chem Inf Model 2020; 61:179-188. [PMID: 33334102 DOI: 10.1021/acs.jcim.0c00936] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
The days when medicinal chemistry was limited to a few series of compounds of therapeutic interest are long gone. Nowadays, no human may succeed to acquire a complete overview of more than a billion existing or feasible compounds within which the potential "blockbuster drugs" are well hidden and yet only a few mouse clicks away. To reach these "hidden treasures", we adapted the generative topographic mapping method to enable efficient navigation through the chemical space, from a global overview to a structural pattern detection, covering, for the first time, the complete ZINC library of purchasable compounds, relative to 1.6 million biologically relevant ChEMBL molecules. About 40 000 hierarchical maps of the chemical space were constructed. Structural motifs inherent to only one library were identified. Roughly 20 000 off-market ChEMBL compound families represent incentives to enrich commercial catalogs. Alternatively, 125 000 ZINC-specific compound classes, absent in structure-activity bases, are novel paths to explore in medicinal chemistry. The complete list of these chemotypes can be downloaded using the link https://forms.gle/B6bUJj82t9EfmttV6.
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Affiliation(s)
- Yuliana Zabolotna
- University of Strasbourg, Laboratoire de Chemoinformatique, 4, rue B. Pascal, Strasbourg 67081 France
| | - Arkadii Lin
- University of Strasbourg, Laboratoire de Chemoinformatique, 4, rue B. Pascal, Strasbourg 67081 France
| | - Dragos Horvath
- University of Strasbourg, Laboratoire de Chemoinformatique, 4, rue B. Pascal, Strasbourg 67081 France
| | - Gilles Marcou
- University of Strasbourg, Laboratoire de Chemoinformatique, 4, rue B. Pascal, Strasbourg 67081 France
| | - Dmitriy M Volochnyuk
- Institute of Organic Chemistry National Academy of Sciences of Ukraine, Murmanska Street 5, Kyiv 02660, Ukraine.,Enamine Ltd., Chervonotkatska Street 78, Kyiv 02094, Ukraine
| | - Alexandre Varnek
- University of Strasbourg, Laboratoire de Chemoinformatique, 4, rue B. Pascal, Strasbourg 67081 France
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9
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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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10
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Yang ZY, He JH, Lu AP, Hou TJ, Cao DS. Application of Negative Design To Design a More Desirable Virtual Screening Library. J Med Chem 2020; 63:4411-4429. [DOI: 10.1021/acs.jmedchem.9b01476] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Affiliation(s)
- Zi-Yi Yang
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, P. R. China
| | - Jun-Hong He
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, P. R. China
| | - Ai-Ping Lu
- Institute for Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, SAR, P. R. China
| | - Ting-Jun Hou
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, P. R. China
| | - Dong-Sheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, P. R. China
- Institute for Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, SAR, P. R. China
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11
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Schneider P, Walters WP, Plowright AT, Sieroka N, Listgarten J, Goodnow RA, Fisher J, Jansen JM, Duca JS, Rush TS, Zentgraf M, Hill JE, Krutoholow E, Kohler M, Blaney J, Funatsu K, Luebkemann C, Schneider G. Rethinking drug design in the artificial intelligence era. Nat Rev Drug Discov 2019. [DOI: 78495111110.1038/s41573-019-0050-3' target='_blank'>'"<>78495111110.1038/s41573-019-0050-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [78495111110.1038/s41573-019-0050-3','', '10.1021/acs.jcim.5b00116')">Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/29/2022]
78495111110.1038/s41573-019-0050-3" />
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12
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Rethinking drug design in the artificial intelligence era. Nat Rev Drug Discov 2019; 19:353-364. [DOI: 10.1038/s41573-019-0050-3] [Citation(s) in RCA: 222] [Impact Index Per Article: 44.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/28/2019] [Indexed: 12/17/2022]
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13
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Grygorenko OO, Volochnyuk DM, Ryabukhin SV, Judd DB. The Symbiotic Relationship Between Drug Discovery and Organic Chemistry. Chemistry 2019; 26:1196-1237. [PMID: 31429510 DOI: 10.1002/chem.201903232] [Citation(s) in RCA: 86] [Impact Index Per Article: 17.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Revised: 08/19/2019] [Indexed: 12/20/2022]
Abstract
All pharmaceutical products contain organic molecules; the source may be a natural product or a fully synthetic molecule, or a combination of both. Thus, it follows that organic chemistry underpins both existing and upcoming pharmaceutical products. The reverse relationship has also affected organic synthesis, changing its landscape towards increasingly complex targets. This Review article sets out to give a concise appraisal of this symbiotic relationship between organic chemistry and drug discovery, along with a discussion of the design concepts and highlighting key milestones along the journey. In particular, criteria for a high-quality compound library design enabling efficient virtual navigation of chemical space, as well as rise and fall of concepts for its synthetic exploration (such as combinatorial chemistry; diversity-, biology-, lead-, or fragment-oriented syntheses; and DNA-encoded libraries) are critically surveyed.
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Affiliation(s)
- Oleksandr O Grygorenko
- Enamine Ltd., Chervonotkatska Street 78, Kiev, 02094, Ukraine.,Taras Shevchenko National University of Kiev, Volodymyrska Street 60, Kiev, 01601, Ukraine
| | - Dmitriy M Volochnyuk
- Enamine Ltd., Chervonotkatska Street 78, Kiev, 02094, Ukraine.,Taras Shevchenko National University of Kiev, Volodymyrska Street 60, Kiev, 01601, Ukraine.,Institute of Organic Chemistry, National Academy of Sciences of Ukraine, Murmanska Street 5, Kiev, 02660, Ukraine
| | - Sergey V Ryabukhin
- Enamine Ltd., Chervonotkatska Street 78, Kiev, 02094, Ukraine.,Taras Shevchenko National University of Kiev, Volodymyrska Street 60, Kiev, 01601, Ukraine
| | - Duncan B Judd
- Awridian Ltd., Stevenage Bioscience Catalyst, Gunnelswood Road, Stevenage, Herts, SG1 2FX, UK
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14
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Abstract
Abstract
Natural product (NP)-derived drugs can be extracts, biological macromolecules, or purified small molecule substances. Small molecule drugs can be originally purified from NPs, can represent semisynthetic molecules, natural fragments containing small molecules, or are fully synthetic molecules that mimic natural compounds. New semisynthetic NP-like drugs are entering the pharmaceutical market almost every year and reveal growing interests in the application of fragment-based approaches for NPs. Thus, several NP databases were constructed to be implemented in the fragment-based drug design (FBDD) workflows. FBDD has been established previously as an approach for hit identification and lead generation. Several biophysical and computational methods are used for fragment screening to identify potential hits. Once the fragments within the binding pocket of the protein are identified, they can be grown, linked, or merged to design more active compounds. This work discusses applications of NPs and NP scaffolds to FBDD. Moreover, it briefly reviews NP databases containing fragments and reports on case studies where the approach has been successfully applied for the design of antimalarial and anticancer drug candidates.
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15
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Klingler FM, Gastreich M, Grygorenko OO, Savych O, Borysko P, Griniukova A, Gubina KE, Lemmen C, Moroz YS. SAR by Space: Enriching Hit Sets from the Chemical Space. Molecules 2019; 24:molecules24173096. [PMID: 31454992 PMCID: PMC6749418 DOI: 10.3390/molecules24173096] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2019] [Revised: 08/14/2019] [Accepted: 08/23/2019] [Indexed: 12/26/2022] Open
Abstract
We introduce SAR by Space, a concept to drastically accelerate structure-activity relationship (SAR) elucidation by synthesizing neighboring compounds that originate from vast chemical spaces. The space navigation is accomplished within minutes on affordable standard computer hardware using a tree-based molecule descriptor and dynamic programming. Maximizing the synthetic accessibility of the results from the computer is achieved by applying a careful selection of building blocks in combination with suitably chosen reactions; a decade of in-house quality control shows that this is a crucial part in the process. The REAL Space is the largest chemical space of commercially available compounds, counting 11 billion molecules as of today. It was used to mine actives against bromodomain 4 (BRD4). Before synthesis, compounds were docked into the binding site using a scoring function, which incorporates intrinsic desolvation terms, thus avoiding time-consuming simulations. Five micromolar hits have been identified and verified within less than six weeks, including the measurement of IC50 values. We conclude that this procedure is a substantial time-saver, accelerating both ligand and structure-based approaches in hit generation and lead optimization stages.
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Affiliation(s)
| | - Marcus Gastreich
- BioSolveIT GmbH, An der Ziegelei 79, 53757 Sankt Augustin, Germany
| | - Oleksandr O Grygorenko
- Enamine Ltd., Chervonotkatska Street 78, 02094 Kyiv, Ukraine
- Taras Shevchenko National University of Kyiv, Volodymyrska Street 60, 01601 Kyiv, Ukraine
| | - Olena Savych
- Enamine Ltd., Chervonotkatska Street 78, 02094 Kyiv, Ukraine
| | - Petro Borysko
- Bienta/Enamine Ltd., Chervonotkatska Street 78, 02094 Kyiv, Ukraine
| | | | - Kateryna E Gubina
- Taras Shevchenko National University of Kyiv, Volodymyrska Street 60, 01601 Kyiv, Ukraine
| | - Christian Lemmen
- BioSolveIT GmbH, An der Ziegelei 79, 53757 Sankt Augustin, Germany
| | - Yurii S Moroz
- Taras Shevchenko National University of Kyiv, Volodymyrska Street 60, 01601 Kyiv, Ukraine.
- Chemspace, Ilukstes iela 38-5, LV-1082 Riga, Latvia.
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16
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Koulouridi E, Valli M, Ntie-Kang F, Bolzani VDS. A primer on natural product-based virtual screening. PHYSICAL SCIENCES REVIEWS 2019. [DOI: 10.1515/psr-2018-0105] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Abstract
Databases play an important role in various computational techniques, including virtual screening (VS) and molecular modeling in general. These collections of molecules can contain a large amount of information, making them suitable for several drug discovery applications. For example, vendor, bioactivity data or target type can be found when searching a database. The introduction of these data resources and their characteristics is used for the design of an experiment. The description of the construction of a database can also be a good advisor for the creation of a new one. There are free available databases and commercial virtual libraries of molecules. Furthermore, a computational chemist can find databases for a general purpose or a specific subset such as natural products (NPs). In this chapter, NP database resources are presented, along with some guidelines when preparing an NP database for drug discovery purposes.
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17
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The next level in chemical space navigation: going far beyond enumerable compound libraries. Drug Discov Today 2019; 24:1148-1156. [PMID: 30851414 DOI: 10.1016/j.drudis.2019.02.013] [Citation(s) in RCA: 127] [Impact Index Per Article: 25.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2018] [Revised: 02/01/2019] [Accepted: 02/28/2019] [Indexed: 10/27/2022]
Abstract
Recent innovations have brought pharmacophore-driven methods for navigating virtual chemical spaces, the size of which can reach into the billions of molecules, to the fingertips of every chemist. There has been a paradigm shift in the underlying computational chemistry that drives chemical space search applications, incorporating intelligent reaction knowledge into their core so that they can readily deliver commercially available molecules as nearest neighbor hits from within giant virtual spaces. These vast resources enable medicinal chemists to execute rapid scaffold-hopping experiments, rapid hit expansion, and structure-activity relationship (SAR) exploitation in largely intellectual property (IP)-free territory and at unparalleled low cost.
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18
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Volochnyuk DM, Ryabukhin SV, Moroz YS, Savych O, Chuprina A, Horvath D, Zabolotna Y, Varnek A, Judd DB. Evolution of commercially available compounds for HTS. Drug Discov Today 2019; 24:390-402. [DOI: 10.1016/j.drudis.2018.10.016] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2018] [Revised: 10/02/2018] [Accepted: 10/30/2018] [Indexed: 12/17/2022]
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19
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Chen Y, de Bruyn Kops C, Kirchmair J. Resources for Chemical, Biological, and Structural Data on Natural Products. PROGRESS IN THE CHEMISTRY OF ORGANIC NATURAL PRODUCTS 2019; 110:37-71. [PMID: 31621010 DOI: 10.1007/978-3-030-14632-0_2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Natural products from plants, marine life, animals, fungi, bacteria, and other organisms remain the most productive source of inspiration for small-molecule drug discovery. Today, a wealth of information on natural products that is particularly valuable to applications in cheminformatics is at our disposal. In this contribution, we provide a timely overview of relevant resources for measured chemical, biological, and structural data on natural products. In particular, we comment on the accessibility, scope, chemical space, and limitations of the individual data sources. The bottleneck of natural products remains the limited availability of material for testing. In this context, we analyze the number of natural products readily obtainable from commercial and other sources.
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Affiliation(s)
- Ya Chen
- Faculty of Mathematics, Informatics, and Natural Sciences, Department of Computer Science, Center for Bioinformatics, Universität Hamburg, Hamburg, Germany
| | | | - Johannes Kirchmair
- Faculty of Mathematics, Informatics, and Natural Sciences, Department of Computer Science, Center for Bioinformatics, Universität Hamburg, Hamburg, Germany. .,Computational Biology Unit (CBU), University of Bergen, Bergen, Norway.
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20
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Silva DG, Emery FDS. Strategies towards expansion of chemical space of natural product-based compounds to enable drug discovery. BRAZ J PHARM SCI 2018. [DOI: 10.1590/s2175-97902018000001004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
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21
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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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22
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Opassi G, Gesù A, Massarotti A. The hitchhiker’s guide to the chemical-biological galaxy. Drug Discov Today 2018; 23:565-574. [DOI: 10.1016/j.drudis.2018.01.007] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2017] [Revised: 11/25/2017] [Accepted: 01/04/2018] [Indexed: 12/21/2022]
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23
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Batiste L, Unzue A, Dolbois A, Hassler F, Wang X, Deerain N, Zhu J, Spiliotopoulos D, Nevado C, Caflisch A. Chemical Space Expansion of Bromodomain Ligands Guided by in Silico Virtual Couplings (AutoCouple). ACS CENTRAL SCIENCE 2018; 4:180-188. [PMID: 29532017 PMCID: PMC5833004 DOI: 10.1021/acscentsci.7b00401] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/29/2017] [Indexed: 10/24/2023]
Abstract
Expanding the chemical space and simultaneously ensuring synthetic accessibility is of upmost importance, not only for the discovery of effective binders for novel protein classes but, more importantly, for the development of compounds against hard-to-drug proteins. Here, we present AutoCouple, a de novo approach to computational ligand design focused on the diversity-oriented generation of chemical entities via virtual couplings. In a benchmark application, chemically diverse compounds with low-nanomolar potency for the CBP bromodomain and high selectivity against the BRD4(1) bromodomain were achieved by the synthesis of about 50 derivatives of the original fragment. The binding mode was confirmed by X-ray crystallography, target engagement in cells was demonstrated, and antiproliferative activity was showcased in three cancer cell lines. These results reveal AutoCouple as a useful in silico coupling method to expand the chemical space in hit optimization campaigns resulting in potent, selective, and cell permeable bromodomain ligands.
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Affiliation(s)
- Laurent Batiste
- Department
of Biochemistry, University of Zurich, Winterthurerstrasse 190, CH-8057, Zürich, Switzerland
| | - Andrea Unzue
- Department
of Chemistry, University of Zurich, Winterthurerstrasse 190, CH-8057, Zürich, Switzerland
| | - Aymeric Dolbois
- Department
of Chemistry, University of Zurich, Winterthurerstrasse 190, CH-8057, Zürich, Switzerland
| | - Fabrice Hassler
- Department
of Chemistry, University of Zurich, Winterthurerstrasse 190, CH-8057, Zürich, Switzerland
| | - Xuan Wang
- Department
of Biochemistry, University of Zurich, Winterthurerstrasse 190, CH-8057, Zürich, Switzerland
- Department
of Chemistry, University of Zurich, Winterthurerstrasse 190, CH-8057, Zürich, Switzerland
| | - Nicholas Deerain
- Department
of Biochemistry, University of Zurich, Winterthurerstrasse 190, CH-8057, Zürich, Switzerland
| | - Jian Zhu
- Department
of Biochemistry, University of Zurich, Winterthurerstrasse 190, CH-8057, Zürich, Switzerland
| | - Dimitrios Spiliotopoulos
- Department
of Biochemistry, University of Zurich, Winterthurerstrasse 190, CH-8057, Zürich, Switzerland
| | - Cristina Nevado
- Department
of Chemistry, University of Zurich, Winterthurerstrasse 190, CH-8057, Zürich, Switzerland
| | - Amedeo Caflisch
- Department
of Biochemistry, University of Zurich, Winterthurerstrasse 190, CH-8057, Zürich, Switzerland
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24
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Wu Z, Cheng F, Li J, Li W, Liu G, Tang Y. SDTNBI: an integrated network and chemoinformatics tool for systematic prediction of drug-target interactions and drug repositioning. Brief Bioinform 2017; 18:333-347. [PMID: 26944082 DOI: 10.1093/bib/bbw012] [Citation(s) in RCA: 54] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2015] [Indexed: 01/11/2023] Open
Abstract
Computational prediction of drug-target interactions (DTIs) and drug repositioning provides a low-cost and high-efficiency approach for drug discovery and development. The traditional social network-derived methods based on the naïve DTI topology information cannot predict potential targets for new chemical entities or failed drugs in clinical trials. There are currently millions of commercially available molecules with biologically relevant representations in chemical databases. It is urgent to develop novel computational approaches to predict targets for new chemical entities and failed drugs on a large scale. In this study, we developed a useful tool, namely substructure-drug-target network-based inference (SDTNBI), to prioritize potential targets for old drugs, failed drugs and new chemical entities. SDTNBI incorporates network and chemoinformatics to bridge the gap between new chemical entities and known DTI network. High performance was yielded in 10-fold and leave-one-out cross validations using four benchmark data sets, covering G protein-coupled receptors, kinases, ion channels and nuclear receptors. Furthermore, the highest areas under the receiver operating characteristic curve were 0.797 and 0.863 for two external validation sets, respectively. Finally, we identified thousands of new potential DTIs via implementing SDTNBI on a global network. As a proof-of-principle, we showcased the use of SDTNBI to identify novel anticancer indications for nonsteroidal anti-inflammatory drugs by inhibiting AKR1C3, CA9 or CA12. In summary, SDTNBI is a powerful network-based approach that predicts potential targets for new chemical entities on a large scale and will provide a new tool for DTI prediction and drug repositioning. The program and predicted DTIs are available on request.
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Affiliation(s)
- Zengrui Wu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai, China
| | - Feixiong Cheng
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Jie Li
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai, China
| | - Weihua Li
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai, China
| | - Guixia Liu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Yun Tang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai, China
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25
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Pertusi DA, Moura ME, Jeffryes JG, Prabhu S, Walters Biggs B, Tyo KEJ. Predicting novel substrates for enzymes with minimal experimental effort with active learning. Metab Eng 2017; 44:171-181. [PMID: 29030274 DOI: 10.1016/j.ymben.2017.09.016] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2017] [Revised: 08/20/2017] [Accepted: 09/18/2017] [Indexed: 01/26/2023]
Abstract
Enzymatic substrate promiscuity is more ubiquitous than previously thought, with significant consequences for understanding metabolism and its application to biocatalysis. This realization has given rise to the need for efficient characterization of enzyme promiscuity. Enzyme promiscuity is currently characterized with a limited number of human-selected compounds that may not be representative of the enzyme's versatility. While testing large numbers of compounds may be impractical, computational approaches can exploit existing data to determine the most informative substrates to test next, thereby more thoroughly exploring an enzyme's versatility. To demonstrate this, we used existing studies and tested compounds for four different enzymes, developed support vector machine (SVM) models using these datasets, and selected additional compounds for experiments using an active learning approach. SVMs trained on a chemically diverse set of compounds were discovered to achieve maximum accuracies of ~80% using ~33% fewer compounds than datasets based on all compounds tested in existing studies. Active learning-selected compounds for testing resolved apparent conflicts in the existing training data, while adding diversity to the dataset. The application of these algorithms to wide arrays of metabolic enzymes would result in a library of SVMs that can predict high-probability promiscuous enzymatic reactions and could prove a valuable resource for the design of novel metabolic pathways.
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Affiliation(s)
- Dante A Pertusi
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, United States
| | - Matthew E Moura
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, United States
| | - James G Jeffryes
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, United States; Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL, United States
| | - Siddhant Prabhu
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, United States
| | - Bradley Walters Biggs
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, United States
| | - Keith E J Tyo
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, United States.
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26
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Abstract
Natural products (NPs) have been used as traditional medicines since antiquity. With more than 1060 estimated compounds with molecular weights less than 500 Da representing chemical space, NPs occupy a very small percentage; however, they are significantly overrepresented in biologically relevant chemical space. The classical approach concentrates on identifying one or more NPs with biological activity from a source organism. There is much more to be learned from NPs than we can discover this narrow view. In this review, we discuss ways to harness the global properties of NPs.
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Affiliation(s)
- Asmaa Boufridi
- Griffith Institute for Drug Discovery, Griffith University, Brisbane, Queensland 4111, Australia; ,
| | - Ronald J Quinn
- Griffith Institute for Drug Discovery, Griffith University, Brisbane, Queensland 4111, Australia; ,
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27
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Chen Y, de Bruyn Kops C, Kirchmair J. Data Resources for the Computer-Guided Discovery of Bioactive Natural Products. J Chem Inf Model 2017; 57:2099-2111. [DOI: 10.1021/acs.jcim.7b00341] [Citation(s) in RCA: 103] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Ya Chen
- Center
for Bioinformatics, Department of Computer Science, Faculty of Mathematics,
Informatics and Natural Sciences, Universität Hamburg, Hamburg 20146, Germany
| | - Christina de Bruyn Kops
- Center
for Bioinformatics, Department of Computer Science, Faculty of Mathematics,
Informatics and Natural Sciences, Universität Hamburg, Hamburg 20146, Germany
| | - Johannes Kirchmair
- Center
for Bioinformatics, Department of Computer Science, Faculty of Mathematics,
Informatics and Natural Sciences, Universität Hamburg, Hamburg 20146, Germany
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28
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Wu Z, Lu W, Wu D, Luo A, Bian H, Li J, Li W, Liu G, Huang J, Cheng F, Tang Y. In silico prediction of chemical mechanism of action via an improved network-based inference method. Br J Pharmacol 2016; 173:3372-3385. [PMID: 27646592 DOI: 10.1111/bph.13629] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2016] [Revised: 08/26/2016] [Accepted: 09/10/2016] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND AND PURPOSE Deciphering chemical mechanism of action (MoA) enables the development of novel therapeutics (e.g. drug repositioning) and evaluation of drug side effects. Development of novel computational methods for chemical MoA assessment under a systems pharmacology framework would accelerate drug discovery and development with greater efficiency and low cost. EXPERIMENTAL APPROACH In this study, we proposed an improved network-based inference method, balanced substructure-drug-target network-based inference (bSDTNBI), to predict MoA for old drugs, clinically failed drugs and new chemical entities. Specifically, three parameters were introduced into network-based resource diffusion processes to adjust the initial resource allocation of different node types, the weighted values of different edge types and the influence of hub nodes. The performance of the method was systematically validated by benchmark datasets and bioassays. KEY RESULTS High performance was yielded for bSDTNBI in both 10-fold and leave-one-out cross validations. A global drug-target network was built to explore MoA of anticancer drugs and repurpose old drugs for 15 cancer types/subtypes. In a case study, 27 predicted candidates among 56 commercially available compounds were experimentally validated to have binding affinities on oestrogen receptor α with IC50 or EC50 values ≤10 μM. Furthermore, two dual ligands with both agonistic and antagonistic activities ≤1 μM would provide potential lead compounds for the development of novel targeted therapy in breast cancer or osteoporosis. CONCLUSION AND IMPLICATIONS In summary, bSDTNBI would provide a powerful tool for the MoA assessment on both old drugs and novel compounds in drug discovery and development.
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Affiliation(s)
- Zengrui Wu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Weiqiang Lu
- Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, China
| | - Dang Wu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Anqi Luo
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Hanping Bian
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Jie Li
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Weihua Li
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Guixia Liu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Jin Huang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Feixiong Cheng
- State Key Laboratory of Biotherapy/Collaborative Innovation Center for Biotherapy, West China Hospital, West China Medical School, Sichuan University, Chengdu, Sichuan, China.,Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA.,Center for Complex Networks Research, Northeastern University, Boston, Massachusetts, USA
| | - Yun Tang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
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Abstract
AIM Fungi are valuable resources for bioactive secondary metabolites. However, the chemical space of fungal secondary metabolites has been studied only on a limited basis. Herein, we report a comprehensive chemoinformatic analysis of a unique set of 207 fungal metabolites isolated and characterized in a USA National Cancer Institute funded drug discovery project. RESULTS Comparison of the molecular complexity of the 207 fungal metabolites with approved anticancer and nonanticancer drugs, compounds in clinical studies, general screening compounds and molecules Generally Recognized as Safe revealed that fungal metabolites have high degree of complexity. Molecular fingerprints showed that fungal metabolites are as structurally diverse as other natural products and have, in general, drug-like physicochemical properties. CONCLUSION Fungal products represent promising candidates to expand the medicinally relevant chemical space. This work is a significant expansion of an analysis reported years ago for a smaller set of compounds (less than half of the ones included in the present work) from filamentous fungi using different structural properties.
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30
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Barnett CB, Aoki-Kinoshita KF, Naidoo KJ. The Glycome Analytics Platform: an integrative framework for glycobioinformatics. Bioinformatics 2016; 32:3005-11. [PMID: 27288496 DOI: 10.1093/bioinformatics/btw341] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2015] [Accepted: 05/26/2016] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Complex carbohydrates play a central role in cellular communication and in disease development. O- and N-glycans, which are post-translationally attached to proteins and lipids, are sugar chains that are rooted, tree structures. Independent efforts to develop computational tools for analyzing complex carbohydrate structures have been designed to exploit specific databases requiring unique formatting and limited transferability. Attempts have been made at integrating these resources, yet it remains difficult to communicate and share data across several online resources. A disadvantage of the lack of coordination between development efforts is the inability of the user community to create reproducible analyses (workflows). The latter results in the more serious unreliability of glycomics metadata. RESULTS In this paper, we realize the significance of connecting multiple online glycan resources that can be used to design reproducible experiments for obtaining, generating and analyzing cell glycomes. To address this, a suite of tools and utilities, have been integrated into the analytic functionality of the Galaxy bioinformatics platform to provide a Glycome Analytics Platform (GAP).Using this platform, users can design in silico workflows to manipulate various formats of glycan sequences and analyze glycomes through access to web data and services. We illustrate the central functionality and features of the GAP by way of example; we analyze and compare the features of the N-glycan glycome of monocytic cells sourced from two separate data depositions.This paper highlights the use of reproducible research methods for glycomics analysis and the GAP presents an opportunity for integrating tools in glycobioinformatics. AVAILABILITY AND IMPLEMENTATION This software is open-source and available online at https://bitbucket.org/scientificomputing/glycome-analytics-platform CONTACTS chris.barnett@uct.ac.za or kevin.naidoo@uct.ac.za SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Christopher B Barnett
- Scientific Computing Research Unit and Department of Chemistry, University of Cape Town, Rondebosch 7701, South Africa
| | - Kiyoko F Aoki-Kinoshita
- Department of Bioinformatics, Faculty of Engineering, Soka University, Hachioji, Tokyo 192-8577, Japan
| | - Kevin J Naidoo
- Scientific Computing Research Unit and Department of Chemistry, University of Cape Town, Rondebosch 7701, South Africa
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31
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Hügle M, Lucas X, Weitzel G, Ostrovskyi D, Breit B, Gerhardt S, Einsle O, Günther S, Wohlwend D. 4-Acyl Pyrrole Derivatives Yield Novel Vectors for Designing Inhibitors of the Acetyl-Lysine Recognition Site of BRD4(1). J Med Chem 2016; 59:1518-30. [PMID: 26731611 DOI: 10.1021/acs.jmedchem.5b01267] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Several human diseases, including cancer, show altered signaling pathways resulting from changes in the activity levels of epigenetic modulators. In the past few years, small-molecule inhibitors against specific modulators, including the bromodomain and extra-terminal (BET) bromodomain family of acetylation readers, have shown early promise in the treatment of the genetically defined midline carcinoma and hematopoietic malignancies. We have recently developed a novel potent inhibitor of BET proteins, 1 (XD14[ Angew. Chem., Int. Ed. 2013, 52, 14055]), which exerts a strong inhibitory potential on the proliferation of specific leukemia cell lines. In the study presented here, we designed analogues of 1 to study the potential of substitutions on the 4-acyl pyrrole backbone to occupy additional sites within the substrate recognition site of BRD4(1). The compounds were profiled using ITC, DSF, and X-ray crystallography. We could introduce several substitutions that address previously untargeted areas of the substrate recognition site. This work may substantially contribute to the development of therapeutics with increased target specificity against BRD4-related malignancies.
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Affiliation(s)
- Martin Hügle
- Institut für Biochemie, Albert-Ludwigs-Universität Freiburg , Albertstrasse 21, D-79104 Freiburg, Germany
| | - Xavier Lucas
- College of Life Sciences, Division of Biological Chemistry and Drug Discovery, University of Dundee , James Black Centre, Dow Street, Dundee, DD1 5EH, United Kingdom
| | - Gerhard Weitzel
- Institut für Organische Chemie, Albert-Ludwigs-Universität Freiburg , Albertstrasse 21, D-79104 Freiburg, Germany
| | - Dmytro Ostrovskyi
- Institut für Organische Chemie, Albert-Ludwigs-Universität Freiburg , Albertstrasse 21, D-79104 Freiburg, Germany
| | - Bernhard Breit
- Institut für Organische Chemie, Albert-Ludwigs-Universität Freiburg , Albertstrasse 21, D-79104 Freiburg, Germany
| | - Stefan Gerhardt
- Institut für Biochemie, Albert-Ludwigs-Universität Freiburg , Albertstrasse 21, D-79104 Freiburg, Germany
| | - Oliver Einsle
- Institut für Biochemie, Albert-Ludwigs-Universität Freiburg , Albertstrasse 21, D-79104 Freiburg, Germany
| | - Stefan Günther
- Institut für Pharmazeutische Wissenschaften, Albert-Ludwigs-Universität Freiburg , Hermann-Herder-Strasse 9, D-79104 Freiburg, Germany
| | - Daniel Wohlwend
- Institut für Biochemie, Albert-Ludwigs-Universität Freiburg , Albertstrasse 21, D-79104 Freiburg, Germany
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Prieto-Martínez FD, Gortari EFD, Méndez-Lucio O, Medina-Franco JL. A chemical space odyssey of inhibitors of histone deacetylases and bromodomains. RSC Adv 2016. [DOI: 10.1039/c6ra07224k] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
The interest in epigenetic drug and probe discovery is growing as reflected in the large amount of structure-epigenetic activity information available.
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Affiliation(s)
| | - Eli Fernández-de Gortari
- Facultad de Química
- Departamento de Farmacia
- Universidad Nacional Autónoma de México
- Mexico City 04510
- Mexico
| | - Oscar Méndez-Lucio
- Facultad de Química
- Departamento de Farmacia
- Universidad Nacional Autónoma de México
- Mexico City 04510
- Mexico
| | - José L. Medina-Franco
- Facultad de Química
- Departamento de Farmacia
- Universidad Nacional Autónoma de México
- Mexico City 04510
- Mexico
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Klementz D, Döring K, Lucas X, Telukunta KK, Erxleben A, Deubel D, Erber A, Santillana I, Thomas OS, Bechthold A, Günther S. StreptomeDB 2.0--an extended resource of natural products produced by streptomycetes. Nucleic Acids Res 2015; 44:D509-14. [PMID: 26615197 PMCID: PMC4702922 DOI: 10.1093/nar/gkv1319] [Citation(s) in RCA: 58] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2015] [Accepted: 11/10/2015] [Indexed: 12/03/2022] Open
Abstract
Over the last decades, the genus Streptomyces has stirred huge interest in the scientific community as a source of bioactive compounds. The majority of all known antibiotics is isolated from these bacterial strains, as well as a variety of other drugs such as antitumor agents, immunosuppressants and antifungals. To the best of our knowledge, StreptomeDB was the first database focusing on compounds produced by streptomycetes. The new version presented herein represents a major step forward: its content has been increased to over 4000 compounds and more than 2500 host organisms. In addition, we have extended the background information and included hundreds of new manually curated references to literature. The latest update features a unique scaffold-based navigation system, which enables the exploration of the chemical diversity of StreptomeDB on a structural basis. We have included a phylogenetic tree, based on 16S rRNA sequences, which comprises more than two-thirds of the included host organisms. It enables visualizing the frequency, appearance, and persistence of compounds and scaffolds in an evolutionary context. Additionally, we have included predicted MS- and NMR-spectra of thousands of compounds for assignment of experimental data. The database is freely accessible via http://www.pharmaceutical-bioinformatics.org/streptomedb.
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Affiliation(s)
- Dennis Klementz
- Pharmaceutical Bioinformatics, Institute of Pharmaceutical Sciences, Albert-Ludwigs-University, Hermann-Herder-Strasse 9, Freiburg 79104, Germany
| | - Kersten Döring
- Pharmaceutical Bioinformatics, Institute of Pharmaceutical Sciences, Albert-Ludwigs-University, Hermann-Herder-Strasse 9, Freiburg 79104, Germany
| | - Xavier Lucas
- Pharmaceutical Bioinformatics, Institute of Pharmaceutical Sciences, Albert-Ludwigs-University, Hermann-Herder-Strasse 9, Freiburg 79104, Germany School of Life Sciences, Division of Biological Chemistry and Drug Discovery, University of Dundee, James Black Centre, Dow Street, Dundee DD1 5EH, UK
| | - Kiran K Telukunta
- Pharmaceutical Bioinformatics, Institute of Pharmaceutical Sciences, Albert-Ludwigs-University, Hermann-Herder-Strasse 9, Freiburg 79104, Germany
| | - Anika Erxleben
- Pharmaceutical Bioinformatics, Institute of Pharmaceutical Sciences, Albert-Ludwigs-University, Hermann-Herder-Strasse 9, Freiburg 79104, Germany Chair for Bioinformatics, Department of Computer Science, University of Freiburg, Georges-Koehler-Allee 106, Freiburg 79110, Germany
| | - Denise Deubel
- Pharmaceutical Biology, Institute of Pharmaceutical Sciences, Albert-Ludwigs-University, 79104 Freiburg, Germany
| | - Astrid Erber
- Pharmaceutical Biology, Institute of Pharmaceutical Sciences, Albert-Ludwigs-University, 79104 Freiburg, Germany
| | - Irene Santillana
- Pharmaceutical Biology, Institute of Pharmaceutical Sciences, Albert-Ludwigs-University, 79104 Freiburg, Germany
| | - Oliver S Thomas
- Pharmaceutical Bioinformatics, Institute of Pharmaceutical Sciences, Albert-Ludwigs-University, Hermann-Herder-Strasse 9, Freiburg 79104, Germany
| | - Andreas Bechthold
- Pharmaceutical Biology, Institute of Pharmaceutical Sciences, Albert-Ludwigs-University, 79104 Freiburg, Germany
| | - Stefan Günther
- Pharmaceutical Bioinformatics, Institute of Pharmaceutical Sciences, Albert-Ludwigs-University, Hermann-Herder-Strasse 9, Freiburg 79104, Germany
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