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Sidorov P, Davioud-Charvet E, Marcou G, Horvath D, Varnek A. AntiMalarial Mode of Action (AMMA) Database: Data Selection, Verification and Chemical Space Analysis. Mol Inform 2018; 37:e1800021. [DOI: 10.1002/minf.201800021] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2018] [Accepted: 04/14/2018] [Indexed: 12/15/2022]
Affiliation(s)
- Pavel Sidorov
- Laboratoire de Chemoinformatique; UMR 7140 CNRS-Univ. Strasbourg; 1 rue Blaise Pascal Strasbourg 67000 France
| | - Elisabeth Davioud-Charvet
- Laboratoire d'Innovation Moléculaire et Applications (LIMA); UMR7042 CNRS-Unistra-UHA; Bioorganic and Medicinal Chemistry Team, European School of Chemistry, Polymers and Materials (ECPM); 25, rue Becquerel Strasbourg F-67087 France
| | - Gilles Marcou
- Laboratoire de Chemoinformatique; UMR 7140 CNRS-Univ. Strasbourg; 1 rue Blaise Pascal Strasbourg 67000 France
| | - Dragos Horvath
- Laboratoire de Chemoinformatique; UMR 7140 CNRS-Univ. Strasbourg; 1 rue Blaise Pascal Strasbourg 67000 France
| | - Alexandre Varnek
- Laboratoire de Chemoinformatique; UMR 7140 CNRS-Univ. Strasbourg; 1 rue Blaise Pascal Strasbourg 67000 France
- Laboratory of Chemoinformatics, Butlerov Institute of Chemistry; Kazan Federal University; Kazan Russia
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From bird’s eye views to molecular communities: two-layered visualization of structure–activity relationships in large compound data sets. J Comput Aided Mol Des 2017; 31:961-977. [DOI: 10.1007/s10822-017-0070-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2017] [Accepted: 09/21/2017] [Indexed: 01/18/2023]
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QSAR modeling and chemical space analysis of antimalarial compounds. J Comput Aided Mol Des 2017; 31:441-451. [PMID: 28374255 DOI: 10.1007/s10822-017-0019-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2016] [Accepted: 03/18/2017] [Indexed: 10/19/2022]
Abstract
Generative topographic mapping (GTM) has been used to visualize and analyze the chemical space of antimalarial compounds as well as to build predictive models linking structure of molecules with their antimalarial activity. For this, a database, including ~3000 molecules tested in one or several of 17 anti-Plasmodium activity assessment protocols, has been compiled by assembling experimental data from in-house and ChEMBL databases. GTM classification models built on subsets corresponding to individual bioassays perform similarly to the earlier reported SVM models. Zones preferentially populated by active and inactive molecules, respectively, clearly emerge in the class landscapes supported by the GTM model. Their analysis resulted in identification of privileged structural motifs of potential antimalarial compounds. Projection of marketed antimalarial drugs on this map allowed us to delineate several areas in the chemical space corresponding to different mechanisms of antimalarial activity. This helped us to make a suggestion about the mode of action of the molecules populating these zones.
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Hu Y, Bajorath J. Follow up: Compound data sets and software tools for chemoinformatics and medicinal chemistry applications: update and data transfer. F1000Res 2014; 3:69. [PMID: 25520777 PMCID: PMC4264635 DOI: 10.12688/f1000research.3713.1] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/07/2014] [Indexed: 12/12/2022] Open
Abstract
In 2012, we reported 30 compound data sets and/or programs developed in our laboratory in a data article and made them freely available to the scientific community to support chemoinformatics and computational medicinal chemistry applications. These data sets and computational tools were provided for download from our website. Since publication of this data article, we have generated 13 new data sets with which we further extend our collection of publicly available data and tools. Due to changes in web servers and website architectures, data accessibility has recently been limited at times. Therefore, we have also transferred our data sets and tools to a public repository to ensure full and stable accessibility. To aid in data selection, we have classified the data sets according to scientific subject areas. Herein, we describe new data sets, introduce the data organization scheme, summarize the database content and provide detailed access information in ZENODO (doi: 10.5281/zenodo.8451 and doi:10.5281/zenodo.8455).
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Affiliation(s)
- Ye Hu
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms University, Bonn, D-53113, Germany
| | - Jürgen Bajorath
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms University, Bonn, D-53113, Germany
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Cruz-Monteagudo M, Cordeiro MNDS. Chemoinformatics Profiling of Ionic Liquids—Uncovering Structure-Cytotoxicity Relationships With Network-like Similarity Graphs. Toxicol Sci 2013; 138:191-204. [DOI: 10.1093/toxsci/kft210] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
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Abstract
The analysis of structure–activity relationships (SARs) is a central task in medicinal chemistry. Traditionally, SAR exploration has concentrated on individual compound series. This conventional approach is complemented by large-scale SAR analysis, which puts strong emphasis on data mining and SAR visualization. This contribution reviews recent concepts for large-scale SAR analysis including numerical functions to characterize global and local SAR information content of compound data sets, alternative activity landscape representations and data mining strategies.
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Csermely P, Korcsmáros T, Kiss HJM, London G, Nussinov R. Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review. Pharmacol Ther 2013; 138:333-408. [PMID: 23384594 PMCID: PMC3647006 DOI: 10.1016/j.pharmthera.2013.01.016] [Citation(s) in RCA: 512] [Impact Index Per Article: 46.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2013] [Accepted: 01/22/2013] [Indexed: 02/02/2023]
Abstract
Despite considerable progress in genome- and proteome-based high-throughput screening methods and in rational drug design, the increase in approved drugs in the past decade did not match the increase of drug development costs. Network description and analysis not only give a systems-level understanding of drug action and disease complexity, but can also help to improve the efficiency of drug design. We give a comprehensive assessment of the analytical tools of network topology and dynamics. The state-of-the-art use of chemical similarity, protein structure, protein-protein interaction, signaling, genetic interaction and metabolic networks in the discovery of drug targets is summarized. We propose that network targeting follows two basic strategies. The "central hit strategy" selectively targets central nodes/edges of the flexible networks of infectious agents or cancer cells to kill them. The "network influence strategy" works against other diseases, where an efficient reconfiguration of rigid networks needs to be achieved by targeting the neighbors of central nodes/edges. It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates. We review the recent boom in network methods helping hit identification, lead selection optimizing drug efficacy, as well as minimizing side-effects and drug toxicity. Successful network-based drug development strategies are shown through the examples of infections, cancer, metabolic diseases, neurodegenerative diseases and aging. Summarizing >1200 references we suggest an optimized protocol of network-aided drug development, and provide a list of systems-level hallmarks of drug quality. Finally, we highlight network-related drug development trends helping to achieve these hallmarks by a cohesive, global approach.
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Affiliation(s)
- Peter Csermely
- Department of Medical Chemistry, Semmelweis University, P.O. Box 260, H-1444 Budapest 8, Hungary.
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Biamonte MA, Wanner J, Le Roch KG. Recent advances in malaria drug discovery. Bioorg Med Chem Lett 2013; 23:2829-43. [PMID: 23587422 PMCID: PMC3762334 DOI: 10.1016/j.bmcl.2013.03.067] [Citation(s) in RCA: 129] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2013] [Revised: 03/11/2013] [Accepted: 03/20/2013] [Indexed: 01/18/2023]
Abstract
This digest covers some of the most relevant progress in malaria drug discovery published between 2010 and 2012. There is an urgent need to develop new antimalarial drugs. Such drugs can target the blood stage of the disease to alleviate the symptoms, the liver stage to prevent relapses, and the transmission stage to protect other humans. The pipeline for the blood stage is becoming robust, but this should not be a source of complacency, as the current therapies set a high standard. Drug discovery efforts directed towards the liver and transmission stages are in their infancy but are receiving increasing attention as targeting these stages could be instrumental in eradicating malaria.
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Affiliation(s)
- Marco A Biamonte
- Drug Discovery for Tropical Diseases, Suite 230, San Diego, CA 92121, USA.
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Hu Y, Bajorath J. Freely available compound data sets and software tools for chemoinformatics and computational medicinal chemistry applications. F1000Res 2012; 1:11. [PMID: 24358818 PMCID: PMC3782340 DOI: 10.12688/f1000research.1-11.v1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/07/2012] [Indexed: 01/22/2023] Open
Abstract
We have generated a number of compound data sets and programs for different types of applications in pharmaceutical research. These data sets and programs were originally designed for our research projects and are made publicly available. Without consulting original literature sources, it is difficult to understand specific features of data sets and software tools, basic ideas underlying their design, and applicability domains. Currently, 30 different entries are available for download from our website. In this data article, we provide an overview of the data and tools we make available and designate the areas of research for which they should be useful. For selected data sets and methods/programs, detailed descriptions are given. This article should help interested readers to select data and tools for specific computational investigations.
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Affiliation(s)
- Ye Hu
- Department of Life Science Informatics, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstr, Bonn, D-53113, Germany
| | - Jurgen Bajorath
- Department of Life Science Informatics, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstr, Bonn, D-53113, Germany
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Miyata Y, Fujii H, Uenohara Y, Kobayashi S, Takeuchi T, Nagase H. Investigation of 7-benzylidenenaltrexone derivatives as resistance reverser for chloroquine-resistant Plasmodium chabaudi. Bioorg Med Chem Lett 2012; 22:5174-6. [DOI: 10.1016/j.bmcl.2012.06.085] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2012] [Revised: 06/19/2012] [Accepted: 06/26/2012] [Indexed: 01/24/2023]
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Wassermann AM, Haebel P, Weskamp N, Bajorath J. SAR matrices: automated extraction of information-rich SAR tables from large compound data sets. J Chem Inf Model 2012; 52:1769-76. [PMID: 22657271 DOI: 10.1021/ci300206e] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
We introduce the SAR matrix data structure that is designed to elucidate SAR patterns produced by groups of structurally related active compounds, which are extracted from large data sets. SAR matrices are systematically generated and sorted on the basis of SAR information content. Matrix generation is computationally efficient and enables processing of large compound sets. The matrix format is reminiscent of SAR tables, and SAR patterns revealed by different categories of matrices are easily interpretable. The structural organization underlying matrix formation is more flexible than standard R-group decomposition schemes. Hence, the resulting matrices capture SAR information in a comprehensive manner.
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Affiliation(s)
- Anne Mai Wassermann
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstrasse 2, D-53113 Bonn, Germany
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13
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Calderón F, Barros D, Bueno JM, Coterón JM, Fernández E, Gamo FJ, Lavandera JL, León ML, Macdonald SJF, Mallo A, Manzano P, Porras E, Fiandor JM, Castro J. An Invitation to Open Innovation in Malaria Drug Discovery: 47 Quality Starting Points from the TCAMS. ACS Med Chem Lett 2011; 2:741-6. [PMID: 24900261 DOI: 10.1021/ml200135p] [Citation(s) in RCA: 63] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2011] [Accepted: 08/03/2011] [Indexed: 11/28/2022] Open
Abstract
In 2010, GlaxoSmithKline published the structures of 13533 chemical starting points for antimalarial lead identification. By using an agglomerative structural clustering technique followed by computational filters such as antimalarial activity, physicochemical properties, and dissimilarity to known antimalarial structures, we have identified 47 starting points for lead optimization. Their structures are provided. We invite potential collaborators to work with us to discover new clinical candidates.
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Affiliation(s)
- Félix Calderón
- Tres Cantos Medicines Development Campus, DDW, GlaxoSmithKline, Severo Ochoa 2, 28760 Tres Cantos, Spain
| | - David Barros
- Tres Cantos Medicines Development Campus, DDW, GlaxoSmithKline, Severo Ochoa 2, 28760 Tres Cantos, Spain
| | - José María Bueno
- Tres Cantos Medicines Development Campus, DDW, GlaxoSmithKline, Severo Ochoa 2, 28760 Tres Cantos, Spain
| | - José Miguel Coterón
- Tres Cantos Medicines Development Campus, DDW, GlaxoSmithKline, Severo Ochoa 2, 28760 Tres Cantos, Spain
| | - Esther Fernández
- Tres Cantos Medicines Development Campus, DDW, GlaxoSmithKline, Severo Ochoa 2, 28760 Tres Cantos, Spain
| | - Francisco Javier Gamo
- Tres Cantos Medicines Development Campus, DDW, GlaxoSmithKline, Severo Ochoa 2, 28760 Tres Cantos, Spain
| | - José Luís Lavandera
- Tres Cantos Medicines Development Campus, DDW, GlaxoSmithKline, Severo Ochoa 2, 28760 Tres Cantos, Spain
| | - María Luisa León
- Tres Cantos Medicines Development Campus, DDW, GlaxoSmithKline, Severo Ochoa 2, 28760 Tres Cantos, Spain
| | - Simon J. F. Macdonald
- Medicines for Malaria Venture (MMV), 20, route de Pré-Bois-PO Box 1826, 1215 Geneva 15, Switzerland
| | - Araceli Mallo
- Tres Cantos Medicines Development Campus, DDW, GlaxoSmithKline, Severo Ochoa 2, 28760 Tres Cantos, Spain
| | - Pilar Manzano
- Tres Cantos Medicines Development Campus, DDW, GlaxoSmithKline, Severo Ochoa 2, 28760 Tres Cantos, Spain
| | - Esther Porras
- Tres Cantos Medicines Development Campus, DDW, GlaxoSmithKline, Severo Ochoa 2, 28760 Tres Cantos, Spain
| | - José María Fiandor
- Tres Cantos Medicines Development Campus, DDW, GlaxoSmithKline, Severo Ochoa 2, 28760 Tres Cantos, Spain
| | - Julia Castro
- Tres Cantos Medicines Development Campus, DDW, GlaxoSmithKline, Severo Ochoa 2, 28760 Tres Cantos, Spain
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Ripphausen P, Nisius B, Wawer M, Bajorath J. Rationalizing the role of SAR tolerance for ligand-based virtual screening. J Chem Inf Model 2011; 51:837-42. [PMID: 21438544 DOI: 10.1021/ci200064c] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
It is well appreciated that the results of ligand-based virtual screening (LBVS) are much influenced by methodological details, given the generally strong compound class dependence of LBVS methods. It is less well understood to what extent structure-activity relationship (SAR) characteristics might influence the outcome of LBVS. We have assessed the hypothesis that the success of prospective LBVS depends on the SAR tolerance of screening targets, in addition to methodological aspects. In this context, SAR tolerance is rationalized as the ability of a target protein to specifically interact with series of structurally diverse active compounds. In compound data sets, SAR tolerance articulates itself as SAR continuity, i.e., the presence of structurally diverse compounds having similar potency. In order to analyze the role of SAR tolerance for LBVS, activity landscape representations of compounds active against 16 different target proteins were generated for which successful LBVS applications were reported. In all instances, the activity landscapes of known active compounds contained multiple regions of local SAR continuity. When analyzing the location of newly identified LBVS hits and their SAR environments, we found that these hits almost exclusively mapped to regions of distinct local SAR continuity. Taken together, these findings indicate the presence of a close link between SAR tolerance at the target level, SAR continuity at the ligand level, and the probability of LBVS success.
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Affiliation(s)
- Peter Ripphausen
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstrasse 2, D-53113 Bonn, Germany
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