Literature DB >> 27481663

Activity Landscapes, Information Theory, and Structure - Activity Relationships.

Preeti Iyer1, Dagmar Stumpfe1, Martin Vogt1, J Bajorath2, G M Maggiora3.   

Abstract

Activity landscapes provide a comprehensive description of structure-activity relationships (SARs). An information theoretic assessment of their features, namely, activity cliffs, similarity cliffs, smooth-SAR, and featureless regions, is presented based on the probability of occurrence of these features. It is shown that activity cliffs provide highly informative SARs compared to smooth-SAR regions, although the latter are the basis for most QSAR studies. This follows since small structural changes in the former are coupled with relatively large changes in activity, thus pinpointing specific structural features associated with the changes in activity. In contrast, Smooth-SAR regions are typically associated with relatively small changes in both structure and activity. Surprisingly, similarity cliffs, which occur when both compounds in a compound-pair have approximately equal activities but significantly different structures, are the most prevalent feature of activity landscapes. Hence, from an information theoretic point of view, they are the least informative landscape feature. Nevertheless, similarity cliffs do provide SAR information on potentially new active compound classes, and in that sense they are quite useful in drug discovery programs since they provide alternative possibilities should ADMET or other issues arise during the discovery and earlier preclinical development phases of drug research.
Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Entities:  

Keywords:  Activity cliffs; Activity landscapes; Information theory; Molecular similarity; Similarity cliffs; Structure-activity relationships

Year:  2013        PMID: 27481663     DOI: 10.1002/minf.201200120

Source DB:  PubMed          Journal:  Mol Inform        ISSN: 1868-1743            Impact factor:   3.353


  5 in total

1.  Activity landscape analysis of novel 5α-reductase inhibitors.

Authors:  J Jesús Naveja; Francisco Cortés-Benítez; Eugene Bratoeff; José L Medina-Franco
Journal:  Mol Divers       Date:  2016-02-01       Impact factor: 2.943

2.  Design of an activity landscape view taking compound-based feature probabilities into account.

Authors:  Bijun Zhang; Martin Vogt; Jürgen Bajorath
Journal:  J Comput Aided Mol Des       Date:  2014-07-08       Impact factor: 3.686

3.  Impact of distance-based metric learning on classification and visualization model performance and structure-activity landscapes.

Authors:  Natalia V Kireeva; Svetlana I Ovchinnikova; Sergey L Kuznetsov; Andrey M Kazennov; Aslan Yu Tsivadze
Journal:  J Comput Aided Mol Des       Date:  2014-02-04       Impact factor: 3.686

4.  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

5.  A simple mathematical approach to the analysis of polypharmacology and polyspecificity data.

Authors:  Gerry Maggiora; Vijay Gokhale
Journal:  F1000Res       Date:  2017-06-06
  5 in total

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