Literature DB >> 25277948

Tools for in silico target fishing.

Adrià Cereto-Massagué1, María José Ojeda1, Cristina Valls1, Miquel Mulero1, Gerard Pujadas2, Santiago Garcia-Vallve3.   

Abstract

Computational target fishing methods are designed to identify the most probable target of a query molecule. This process may allow the prediction of the bioactivity of a compound, the identification of the mode of action of known drugs, the detection of drug polypharmacology, drug repositioning or the prediction of the adverse effects of a compound. The large amount of information regarding the bioactivity of thousands of small molecules now allows the development of these types of methods. In recent years, we have witnessed the emergence of many methods for in silico target fishing. Most of these methods are based on the similarity principle, i.e., that similar molecules might bind to the same targets and have similar bioactivities. However, the difficult validation of target fishing methods hinders comparisons of the performance of each method. In this review, we describe the different methods developed for target prediction, the bioactivity databases most frequently used by these methods, and the publicly available programs and servers that enable non-specialist users to obtain these types of predictions. It is expected that target prediction will have a large impact on drug development and on the functional food industry.
Copyright © 2014 Elsevier Inc. All rights reserved.

Keywords:  Computational target fishing; Drug repositioning; Drug research; Functional foods; Polypharmacology; Reverse screening

Mesh:

Year:  2014        PMID: 25277948     DOI: 10.1016/j.ymeth.2014.09.006

Source DB:  PubMed          Journal:  Methods        ISSN: 1046-2023            Impact factor:   3.608


  40 in total

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Authors:  Maykel Cruz-Monteagudo; Stephan Schürer; Eduardo Tejera; Yunierkis Pérez-Castillo; José L Medina-Franco; Aminael Sánchez-Rodríguez; Fernanda Borges
Journal:  Drug Discov Today       Date:  2017-03-06       Impact factor: 7.851

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4.  Large-scale chemical similarity networks for target profiling of compounds identified in cell-based chemical screens.

Authors:  Yu-Chen Lo; Silvia Senese; Chien-Ming Li; Qiyang Hu; Yong Huang; Robert Damoiseaux; Jorge Z Torres
Journal:  PLoS Comput Biol       Date:  2015-03-31       Impact factor: 4.475

5.  Characterization of anti-leukemia components from Indigo naturalis using comprehensive two-dimensional K562/cell membrane chromatography and in silico target identification.

Authors:  Xunxun Wu; Xiaofei Chen; Jia Dan; Yan Cao; Shouhong Gao; Zhiying Guo; Philipp Zerbe; Yifeng Chai; Yong Diao; Lei Zhang
Journal:  Sci Rep       Date:  2016-05-06       Impact factor: 4.379

6.  Discovery of a New Class of Cathepsin K Inhibitors in Rhizoma Drynariae as Potential Candidates for the Treatment of Osteoporosis.

Authors:  Zuo-Cheng Qiu; Xiao-Li Dong; Yi Dai; Gao-Keng Xiao; Xin-Luan Wang; Ka-Chun Wong; Man-Sau Wong; Xin-Sheng Yao
Journal:  Int J Mol Sci       Date:  2016-12-16       Impact factor: 5.923

Review 7.  Internet Databases of the Properties, Enzymatic Reactions, and Metabolism of Small Molecules-Search Options and Applications in Food Science.

Authors:  Piotr Minkiewicz; Małgorzata Darewicz; Anna Iwaniak; Justyna Bucholska; Piotr Starowicz; Emilia Czyrko
Journal:  Int J Mol Sci       Date:  2016-12-06       Impact factor: 5.923

8.  Identification of a Potential Target of Capsaicin by Computational Target Fishing.

Authors:  Xuan-Yi Ye; Qing-Zhi Ling; Shao-Jun Chen
Journal:  Evid Based Complement Alternat Med       Date:  2015-12-03       Impact factor: 2.629

9.  How Reliable Are Ligand-Centric Methods for Target Fishing?

Authors:  Antonio Peón; Cuong C Dang; Pedro J Ballester
Journal:  Front Chem       Date:  2016-04-14       Impact factor: 5.221

10.  Historeceptomic Fingerprints for Drug-Like Compounds.

Authors:  Evgeny Shmelkov; Arsen Grigoryan; James Swetnam; Junyang Xin; Doreen Tivon; Sergey V Shmelkov; Timothy Cardozo
Journal:  Front Physiol       Date:  2015-12-18       Impact factor: 4.566

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