Bajorath, Jürgen

Dimova, Dilyana

Journal of Cheminformatics

English

PubMed

Dilyana Dimova

Jürgen Bajorath

Springer - Publisher Connector

POSTER PRESENTATION Open Access
Design of multi-target activity landscapes that
capture hierarchical activity cliff distributions
Dilyana Dimova*, Jürgen Bajorath
From 7th German Conference on Chemoinformatics: 25 CIC-Workshop
Goslar, Germany. 6-8 November 2011
For compounds with activity against multiple targets the
ensuing multi-target structure-activity relationships
(mtSARs) are generally difficult to analyze. However,
understanding mtSARs is often of critical importance
for compound design and optimization [1]. In addition,
the detection and analysis of activity cliffs also plays a
crucial role in comprehensive SAR exploration [2,3].
Activity cliffs represent the most prominent feature of
activity landscapes, which can be graphically represented
by models that integrate molecular similarity and
potency relationships [2,4]. Different activity landscape
representations have been introduced. These activity
landscape designs have in common that they all focus
on activity against a single or at most two biological tar-
gets (the latter case giving rise to selectivity landscapes
[3]). For compounds active against multiple targets,
landscape representations cannot be obtained directly
on the basis of currently available models and new
design concepts are required. Here we introduce a
methodology to derive and visualize multi-target activity
landscapes and systematically analyze activity cliff distri-
butions [5]. The framework is based on a general activ-
ity cliff classification scheme. Multi-target activity
landscapes are visualized as graphs where nodes repre-
sent individual compounds and edges activity cliffs. In
addition, node proximity indicates molecular similarity.
The methodology has been applied to derive landscape
models for various compound data sets with activity
against multiple targets belonging to different families.
The resulting representations identify single and multi-
target activity cliffs and reveal hierarchical cliff distribu-
tions. From these landscape models, compounds form-
ing complex activity cliffs can be readily selected.
Published: 1 May 2012
References
1. Kubinyi H: Similarity and Dissimilarity. A Medicinal Chemist’s View.
Perspect Drug Discovery Des 1998, 9-11:225-252.
2. Maggiora GM: On Outliers and Activity Cliffs - Why QSAR Often
Dissapoints. J Chem Inf Model 2006, 46:1535.
3. Bajorath J, Peltason L, Wawer M, Guha R, Lajiness MS, Van Drie JH:
Navigating Structure-Activity Landscapes. Drug Discovery Today 2009,
14:698-705.
4. Wassermann AM, Wawer M, Bajorath J: Activity Landscape
Representations for Structure-Activity Relationship Analysis. J Med Chem
2010, 53:8209-8223.
5. Dimova D, Wawer M, Wasserman AM, Bajorath J: A Design of Multitarget
Activity Landscapes That Capture Hierarchical Activity Cliff Distributions.
J Chem Inf Model 2011, 51:258-266.
doi:10.1186/1758-2946-4-S1-P4
Cite this article as: Dimova and Bajorath: Design of multi-target activity
landscapes that capture hierarchical activity cliff distributions. Journal of
Cheminformatics 2012 4(Suppl 1):P4.
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Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical
Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität,
Dahlmannstr. 2, D-53113 Bonn, Germany
Dimova and Bajorath Journal of Cheminformatics 2012, 4(Suppl 1):P4
http://www.jcheminf.com/content/4/S1/P4
© 2012 Dimova and Bajorath; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative
Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work is properly cited.


Design of multi-target activity landscapes that capture hierarchical activity cliff distributions

Activity Landscape Representations for Structure-Activity Relationship Analysis.

Drie JH: Navigating Structure-Activity Landscapes. Drug Discovery Today

GM: On Outliers and Activity Cliffs - Why QSAR Often Dissapoints.

H: Similarity and Dissimilarity. A Medicinal Chemist’s View. Perspect Drug Discovery Des

Inf Model 2011, 51:258-266. doi:10.1186/1758-2946-4-S1-P4 Cite this article as: Dimova and Bajorath: Design of multi-target activity landscapes that capture hierarchical activity cliff distributions.

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Design of multi-target activity landscapes that capture hierarchical activity cliff distributions

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