Literature DB >> 22475223

Modeling of activity landscapes for drug discovery.

Jürgen Bajorath1.   

Abstract

INTRODUCTION: Activity landscapes (ALs) are graphical representations that integrate compound structure and potency relationships. These computer-generated models enable the interactive large-scale analysis of structure-activity relationships (SARs) and complement traditional approaches to study SARs of individual compound series in a qualitative or quantitative manner. A variety of AL designs have been reported. AREAS COVERED: The concept of activity landscapes is introduced and different methodologies to represent 2D or 3D AL representations of large compound data sets are described on the basis of original literature references. Several AL variants and extensions have been generated for special applications in medicinal chemistry. These include, for example, AL views of evolving data sets with constant topology, selectivity landscapes and multi-target ALs, or molecular mechanism and multi-property maps. Furthermore, the applicability domain of the AL concept is discussed including specific requirements for practical utility in medicinal chemistry opportunities for further developments. EXPERT OPINION: AL modeling has substantially extended conventional ways to study SARs. The AL concept is inseparable from the notion of activity cliffs that are of high interest in SAR analysis. AL design is an area of active research at the interface between chemoinformatics and medicinal chemistry with potential for further growth. Special emphasis must be put on increasing the usability of AL models for practicing medicinal chemists.

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Year:  2012        PMID: 22475223     DOI: 10.1517/17460441.2012.679616

Source DB:  PubMed          Journal:  Expert Opin Drug Discov        ISSN: 1746-0441            Impact factor:   6.098


  11 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.  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

3.  Structure-based predictions of activity cliffs.

Authors:  Jarmila Husby; Giovanni Bottegoni; Irina Kufareva; Ruben Abagyan; Andrea Cavalli
Journal:  J Chem Inf Model       Date:  2015-05-11       Impact factor: 4.956

4.  Rapid scanning structure-activity relationships in combinatorial data sets: identification of activity switches.

Authors:  José L Medina-Franco; Bruce S Edwards; Clemencia Pinilla; Jon R Appel; Marc A Giulianotti; Radleigh G Santos; Austin B Yongye; Larry A Sklar; Richard A Houghten
Journal:  J Chem Inf Model       Date:  2013-06-07       Impact factor: 4.956

Review 5.  Discovery of potent positive allosteric modulators of the α3β2 nicotinic acetylcholine receptor by a chemical space walk in ChEMBL.

Authors:  Justus J Bürgi; Mahendra Awale; Silvan D Boss; Tifany Schaer; Fabrice Marger; Juan M Viveros-Paredes; Sonia Bertrand; Jürg Gertsch; Daniel Bertrand; Jean-Louis Reymond
Journal:  ACS Chem Neurosci       Date:  2014-03-04       Impact factor: 4.418

6.  Conditional probabilistic analysis for prediction of the activity landscape and relative compound activities.

Authors:  Radleigh G Santos; Marc A Giulianotti; Richard A Houghten; José L Medina-Franco
Journal:  J Chem Inf Model       Date:  2013-09-17       Impact factor: 4.956

7.  Combinatorial Libraries As a Tool for the Discovery of Novel, Broad-Spectrum Antibacterial Agents Targeting the ESKAPE Pathogens.

Authors:  Renee Fleeman; Travis M LaVoi; Radleigh G Santos; Angela Morales; Adel Nefzi; Gregory S Welmaker; José L Medina-Franco; Marc A Giulianotti; Richard A Houghten; Lindsey N Shaw
Journal:  J Med Chem       Date:  2015-04-01       Impact factor: 7.446

8.  On the validity versus utility of activity landscapes: are all activity cliffs statistically significant?

Authors:  Rajarshi Guha; José L Medina-Franco
Journal:  J Cheminform       Date:  2014-04-02       Impact factor: 5.514

9.  Expanding the fragrance chemical space for virtual screening.

Authors:  Lars Ruddigkeit; Mahendra Awale; Jean-Louis Reymond
Journal:  J Cheminform       Date:  2014-05-22       Impact factor: 5.514

Review 10.  Computer-aided drug discovery.

Authors:  Jürgen Bajorath
Journal:  F1000Res       Date:  2015-08-26
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