Literature DB >> 26945865

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

Martin Vogt1, Dagmar Stumpfe1, Gerald M Maggiora2, Jürgen Bajorath3.   

Abstract

The concept of chemical space is of fundamental relevance in chemical informatics and computer-aided drug discovery. In a series of articles published in the Journal of Computer-Aided Molecular Design, principles of chemical space design were evaluated, molecular networks proposed as an alternative to conventional coordinate-based chemical reference spaces, and different types of chemical space networks (CSNs) constructed and analyzed. Central to the generation of CSNs was the way in which molecular similarity relationships were assessed and a primary focal point was the network-based representation of biologically relevant chemical space. The design and comparison of CSNs based upon alternative similarity measures can be viewed as an evolutionary path with interesting lessons learned along the way. CSN design has matured to the point that such chemical space representations can be used in practice. In this contribution, highlights from the sequence of CSN design efforts are discussed in context, providing a perspective for future practical applications.

Keywords:  Biologically relevant chemical space; Chemical space networks; Chemical space representation; Coordinate-free chemical space; Molecular similarity measures; Network science; Network topology; Structure–activity relationships; Substructure relationship

Mesh:

Year:  2016        PMID: 26945865     DOI: 10.1007/s10822-016-9906-3

Source DB:  PubMed          Journal:  J Comput Aided Mol Des        ISSN: 0920-654X            Impact factor:   3.686


  21 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.  Chemical space and biology.

Authors:  Christopher M Dobson
Journal:  Nature       Date:  2004-12-16       Impact factor: 49.962

3.  On outliers and activity cliffs--why QSAR often disappoints.

Authors:  Gerald M Maggiora
Journal:  J Chem Inf Model       Date:  2006 Jul-Aug       Impact factor: 4.956

4.  Recent progress in understanding activity cliffs and their utility in medicinal chemistry.

Authors:  Dagmar Stumpfe; Ye Hu; Dilyana Dimova; Jürgen Bajorath
Journal:  J Med Chem       Date:  2013-09-13       Impact factor: 7.446

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

Authors:  Naoki Tanaka; Kazuki Ohno; Tatsuya Niimi; Ayako Moritomo; Kenichi Mori; Masaya Orita
Journal:  J Chem Inf Model       Date:  2009-12       Impact factor: 4.956

6.  Drug-target network.

Authors:  Muhammed A Yildirim; Kwang-Il Goh; Michael E Cusick; Albert-László Barabási; Marc Vidal
Journal:  Nat Biotechnol       Date:  2007-10       Impact factor: 54.908

7.  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

8.  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

9.  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

10.  Using Graph Indices for the Analysis and Comparison of Chemical Datasets.

Authors:  Denis Fourches; Alexander Tropsha
Journal:  Mol Inform       Date:  2013-09-09       Impact factor: 3.353

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  11 in total

1.  Knowledge discovery through chemical space networks: the case of organic electronics.

Authors:  Christian Kunkel; Christoph Schober; Harald Oberhofer; Karsten Reuter
Journal:  J Mol Model       Date:  2019-03-07       Impact factor: 1.810

2.  Tracing compound pathways using chemical space networks.

Authors:  Ryo Kunimoto; Martin Vogt; Jürgen Bajorath
Journal:  Medchemcomm       Date:  2016-12-23       Impact factor: 3.597

3.  From bird's eye views to molecular communities: two-layered visualization of structure-activity relationships in large compound data sets.

Authors:  Shilva Kayastha; Ryo Kunimoto; Dragos Horvath; Alexandre Varnek; Jürgen Bajorath
Journal:  J Comput Aided Mol Des       Date:  2017-10-06       Impact factor: 3.686

4.  Exploring sets of molecules from patents and relationships to other active compounds in chemical space networks.

Authors:  Ryo Kunimoto; Jürgen Bajorath
Journal:  J Comput Aided Mol Des       Date:  2017-09-04       Impact factor: 3.686

5.  ADME-Space: a new tool for medicinal chemists to explore ADME properties.

Authors:  Giovanni Bocci; Emanuele Carosati; Philippe Vayer; Alban Arrault; Sylvain Lozano; Gabriele Cruciani
Journal:  Sci Rep       Date:  2017-07-25       Impact factor: 4.379

6.  Design of chemical space networks incorporating compound distance relationships.

Authors:  Antonio de la Vega de León; Jürgen Bajorath
Journal:  F1000Res       Date:  2016-11-04

7.  Progress on open chemoinformatic tools for expanding and exploring the chemical space.

Authors:  José L Medina-Franco; Norberto Sánchez-Cruz; Edgar López-López; Bárbara I Díaz-Eufracio
Journal:  J Comput Aided Mol Des       Date:  2021-06-18       Impact factor: 4.179

8.  Structure based classification for bile salt export pump (BSEP) inhibitors using comparative structural modeling of human BSEP.

Authors:  Sankalp Jain; Melanie Grandits; Lars Richter; Gerhard F Ecker
Journal:  J Comput Aided Mol Des       Date:  2017-05-19       Impact factor: 4.179

9.  Network-based piecewise linear regression for QSAR modelling.

Authors:  Jonathan Cardoso-Silva; Lazaros G Papageorgiou; Sophia Tsoka
Journal:  J Comput Aided Mol Des       Date:  2019-10-18       Impact factor: 3.686

10.  Automatic construction of molecular similarity networks for visual graph mining in chemical space of bioactive peptides: an unsupervised learning approach.

Authors:  Longendri Aguilera-Mendoza; Yovani Marrero-Ponce; César R García-Jacas; Edgar Chavez; Jesus A Beltran; Hugo A Guillen-Ramirez; Carlos A Brizuela
Journal:  Sci Rep       Date:  2020-10-22       Impact factor: 4.379

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