Literature DB >> 19961207

Small-world phenomena in chemical library networks: application to fragment-based drug discovery.

Naoki Tanaka1, Kazuki Ohno, Tatsuya Niimi, Ayako Moritomo, Kenichi Mori, Masaya Orita.   

Abstract

A wide variety of networks in various fields have been characterized as small-world networks. In scale-free networks, a representative class of small-world networks, numbers of contacts (degree distributions) of nodes follow power laws. Although several examples of power-law distributions have been found in the field of chemoinformatics, the network structures of chemical libraries have not been analyzed. Here, we show that small-world phenomena are observed not only in existing chemical libraries but also in virtual libraries generated from structurally diverse fragments when represented as networks. On the basis of this observation, we propose that an efficient compound-prioritization method of fragment-based drug discovery (FBDD) would be to select those fragments as a starting point such that the linked compounds become hubs in the library and therefore allow identification of many similar compounds when all-to-all fragment linkings are performed. Moreover, our analyses indicated that the variety of linkers had a marked influence on the network structure and thus on the diversity of the compounds synthesized by linking fragment hits.

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Year:  2009        PMID: 19961207     DOI: 10.1021/ci900123v

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  9 in total

1.  Design of chemical space networks using a Tanimoto similarity variant based upon maximum common substructures.

Authors:  Bijun Zhang; Martin Vogt; Gerald M Maggiora; Jürgen Bajorath
Journal:  J Comput Aided Mol Des       Date:  2015-09-29       Impact factor: 3.686

2.  Design of chemical space networks on the basis of Tversky similarity.

Authors:  Mengjun Wu; Martin Vogt; Gerald M Maggiora; Jürgen Bajorath
Journal:  J Comput Aided Mol Des       Date:  2015-12-22       Impact factor: 3.686

3.  Design and characterization of chemical space networks for different compound data sets.

Authors:  Magdalena Zwierzyna; Martin Vogt; Gerald M Maggiora; Jürgen Bajorath
Journal:  J Comput Aided Mol Des       Date:  2014-12-03       Impact factor: 3.686

4.  Comparison of bioactive chemical space networks generated using substructure- and fingerprint-based measures of molecular similarity.

Authors:  Bijun Zhang; Martin Vogt; Gerald M Maggiora; Jürgen Bajorath
Journal:  J Comput Aided Mol Des       Date:  2015-06-07       Impact factor: 3.686

Review 5.  Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review.

Authors:  Peter Csermely; Tamás Korcsmáros; Huba J M Kiss; Gábor London; Ruth Nussinov
Journal:  Pharmacol Ther       Date:  2013-02-04       Impact factor: 12.310

6.  Chemical space networks: a powerful new paradigm for the description of chemical space.

Authors:  Gerald M Maggiora; Jürgen Bajorath
Journal:  J Comput Aided Mol Des       Date:  2014-06-13       Impact factor: 3.686

7.  Lessons learned from the design of chemical space networks and opportunities for new applications.

Authors:  Martin Vogt; Dagmar Stumpfe; Gerald M Maggiora; Jürgen Bajorath
Journal:  J Comput Aided Mol Des       Date:  2016-03-05       Impact factor: 3.686

8.  Impact of similarity threshold on the topology of molecular similarity networks and clustering outcomes.

Authors:  Gergely Zahoránszky-Kőhalmi; Cristian G Bologa; Tudor I Oprea
Journal:  J Cheminform       Date:  2016-03-30       Impact factor: 5.514

9.  Towards Predictive Synthesis of Inorganic Materials Using Network Science.

Authors:  Alex Aziz; Javier Carrasco
Journal:  Front Chem       Date:  2021-12-21       Impact factor: 5.221

  9 in total

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