Literature DB >> 16426054

Molecular transformations as a way of finding and exploiting consistent local QSAR.

Robert P Sheridan1, Peter Hunt, J Chris Culberson.   

Abstract

The idea of a "transformation", making a small change to a chemical structure, for instance removing or replacing a substituent, is familiar to chemists. We suggest two ways of representing a transformation in silico, as a substructure descriptor difference vector, and as the set of atoms remaining once a maximum common substructure is eliminated. Such transformations can be filtered sensibly, and it is easy to compare one transformation to another. These representations have two applications. First, we can use these methods to automatically organize and display sets of closely related compounds such that any consistent local QSAR in a data set can be easily seen, the T-ANALYZE application. Second, we can suggest to a chemist how to change a molecule "on hand" to a more active one based on local QSAR for that activity, the T-MORPH application.

Year:  2006        PMID: 16426054     DOI: 10.1021/ci0503208

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


  12 in total

1.  Successful identification of key chemical structure modifications that lead to improved ADME profiles.

Authors:  Lourdes Cucurull-Sanchez
Journal:  J Comput Aided Mol Des       Date:  2010-05-09       Impact factor: 3.686

2.  Exploring Modifications of an HIV-1 Capsid Inhibitor: Design, Synthesis, and Mechanism of Action.

Authors:  Jimmy P Xu; Ashwanth C Francis; Megan E Meuser; Marie Mankowski; Roger G Ptak; Adel A Rashad; Gregory B Melikyan; Simon Cocklin
Journal:  J Drug Des Res       Date:  2018-08-13

3.  QSAR model based on weighted MCS trees approach for the representation of molecule data sets.

Authors:  Bernardo Palacios-Bejarano; Gonzalo Cerruela García; Irene Luque Ruiz; Miguel Ángel Gómez-Nieto
Journal:  J Comput Aided Mol Des       Date:  2013-02-06       Impact factor: 3.686

Review 4.  The ups and downs of structure-activity landscapes.

Authors:  Rajarshi Guha
Journal:  Methods Mol Biol       Date:  2011

5.  Exploring Structure-Activity Data Using the Landscape Paradigm.

Authors:  Rajarshi Guha
Journal:  Wiley Interdiscip Rev Comput Mol Sci       Date:  2012-11

6.  The CARLSBAD database: a confederated database of chemical bioactivities.

Authors:  Stephen L Mathias; Jarrett Hines-Kay; Jeremy J Yang; Gergely Zahoransky-Kohalmi; Cristian G Bologa; Oleg Ursu; Tudor I Oprea
Journal:  Database (Oxford)       Date:  2013-06-21       Impact factor: 3.451

Review 7.  Matched Molecular Pair Analysis in Short: Algorithms, Applications and Limitations.

Authors:  Christian Tyrchan; Emma Evertsson
Journal:  Comput Struct Biotechnol J       Date:  2016-12-13       Impact factor: 7.271

8.  Chemical reaction vector embeddings: towards predicting drug metabolism in the human gut microbiome.

Authors:  Emily K Mallory; Ambika Acharya; Stefano E Rensi; Peter J Turnbaugh; Roselie A Bright; Russ B Altman
Journal:  Pac Symp Biocomput       Date:  2018

9.  Strong nonadditivity as a key structure-activity relationship feature: distinguishing structural changes from assay artifacts.

Authors:  Christian Kramer; Julian E Fuchs; Klaus R Liedl
Journal:  J Chem Inf Model       Date:  2015-03-11       Impact factor: 4.956

10.  A fingerprint pair analysis of hERG inhibition data.

Authors:  Clayton Springer; Katherine L Sokolnicki
Journal:  Chem Cent J       Date:  2013-10-21       Impact factor: 4.215

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.