Recent developments in SAR visualization

Graphical approaches help to explore structure–activity relationships (SARs) in large and heterogeneous compound data sets. Herein, we review recent methodological developments in SAR visualization for medicinal chemistry. Different concepts are discussed for projecting high-dimensional feature spaces or generating SAR networks and other graphical SAR views. In addition, a perspective on new opportunities for activity landscape design and SAR visualization is provided.

[1]  Eugen Lounkine,et al.  Chemotography for multi-target SAR analysis in the context of biological pathways. , 2012, Bioorganic & medicinal chemistry.

[2]  Jürgen Bajorath,et al.  AnalogExplorer2 – Stereochemistry sensitive graphical analysis of large analog series , 2015, F1000Research.

[3]  Jürgen Bajorath,et al.  Navigating High-Dimensional Activity Landscapes: Design and Application of the Ligand-Target Differentiation Map , 2012, J. Chem. Inf. Model..

[4]  Jürgen Bajorath,et al.  Visualization of multi-property landscapes for compound selection and optimization , 2015, Journal of Computer-Aided Molecular Design.

[5]  Jürgen Bajorath,et al.  Comparison of bioactive chemical space networks generated using substructure- and fingerprint-based measures of molecular similarity , 2015, Journal of Computer-Aided Molecular Design.

[6]  Rolph E. Anderson,et al.  Multivariate Data Analysis (7th ed. , 2009 .

[7]  J. Bajorath,et al.  SAR index: quantifying the nature of structure-activity relationships. , 2007, Journal of medicinal chemistry.

[8]  J. Bajorath,et al.  Data structures and computational tools for the extraction of SAR information from large compound sets. , 2010, Drug discovery today.

[9]  Jürgen Bajorath,et al.  Design of chemical space networks on the basis of Tversky similarity , 2015, Journal of Computer-Aided Molecular Design.

[10]  Anne Mai Wassermann,et al.  SAR Matrices: Automated Extraction of Information-Rich SAR Tables from Large Compound Data Sets , 2012, J. Chem. Inf. Model..

[11]  D. Bojanic,et al.  Impact of high-throughput screening in biomedical research , 2011, Nature Reviews Drug Discovery.

[12]  Jürgen Bajorath,et al.  Introducing the LASSO graph for compound data set representation and structure-activity relationship analysis. , 2012, Journal of medicinal chemistry.

[13]  Wolfgang Guba,et al.  Neighborhood-preserving visualization of adaptive structure-activity landscapes: application to drug discovery. , 2011, Angewandte Chemie.

[14]  Jean-Louis Reymond,et al.  Similarity Mapplet: Interactive Visualization of the Directory of Useful Decoys and ChEMBL in High Dimensional Chemical Spaces , 2015, J. Chem. Inf. Model..

[15]  Jürgen Bajorath,et al.  Methods for SAR visualization , 2012 .

[16]  J. Bajorath,et al.  Structure-activity relationship anatomy by network-like similarity graphs and local structure-activity relationship indices. , 2008, Journal of medicinal chemistry.

[17]  Robert Nadon,et al.  Statistical practice in high-throughput screening data analysis , 2006, Nature Biotechnology.

[18]  Jürgen Bajorath,et al.  Design and characterization of chemical space networks for different compound data sets , 2015, Journal of Computer-Aided Molecular Design.

[19]  Mark Newman,et al.  Networks: An Introduction , 2010 .

[20]  P Schneider,et al.  Self-organizing maps in drug discovery: compound library design, scaffold-hopping, repurposing. , 2009, Current medicinal chemistry.

[21]  Jürgen Bajorath,et al.  AnalogExplorer: a new method for graphical analysis of analog series and associated structure-activity relationship information. , 2014, Journal of medicinal chemistry.

[22]  José L Medina-Franco,et al.  Bioactivity landscape modeling: chemoinformatic characterization of structure-activity relationships of compounds tested across multiple targets. , 2012, Bioorganic & medicinal chemistry.

[23]  Dimitris K Agrafiotis,et al.  SAR maps: a new SAR visualization technique for medicinal chemists. , 2007, Journal of medicinal chemistry.

[24]  John P. Overington,et al.  ChEMBL: a large-scale bioactivity database for drug discovery , 2011, Nucleic Acids Res..

[25]  G. Bemis,et al.  The properties of known drugs. 1. Molecular frameworks. , 1996, Journal of medicinal chemistry.

[26]  Héléna A. Gaspar,et al.  Generative Topographic Mapping (GTM): Universal Tool for Data Visualization, Structure‐Activity Modeling and Dataset Comparison , 2012, Molecular informatics.

[27]  J. Bajorath,et al.  Activity landscape representations for structure-activity relationship analysis. , 2010, Journal of medicinal chemistry.

[28]  Jürgen Bajorath,et al.  Neighborhood-Based Prediction of Novel Active Compounds from SAR Matrices , 2014, J. Chem. Inf. Model..

[29]  Igor I. Baskin,et al.  Stargate GTM: Bridging Descriptor and Activity Spaces , 2015, J. Chem. Inf. Model..

[30]  Jürgen Bajorath,et al.  Hit Expansion from Screening Data Based upon Conditional Probabilities of Activity Derived from SAR Matrices , 2015, Molecular informatics.

[31]  Kimito Funatsu,et al.  Generative topographic mapping of binding pocket of β2 receptor and three‐way partial least squares modeling of inhibitory activities , 2014 .

[32]  Jürgen Bajorath,et al.  Rationalizing Three-Dimensional Activity Landscapes and the Influence of Molecular Representations on Landscape Topology and the Formation of Activity Cliffs , 2010, J. Chem. Inf. Model..

[33]  Knut Baumann,et al.  inSARa: Intuitive and Interactive SAR Interpretation by Reduced Graphs and Hierarchical MCS-Based Network Navigation , 2014, J. Chem. Inf. Model..

[34]  Jürgen Bajorath,et al.  Exploring activity cliffs in medicinal chemistry. , 2012, Journal of medicinal chemistry.

[35]  Jameed Hussain,et al.  Computationally Efficient Algorithm to Identify Matched Molecular Pairs (MMPs) in Large Data Sets , 2010, J. Chem. Inf. Model..

[36]  J. Bajorath,et al.  Local structural changes, global data views: graphical substructure-activity relationship trailing. , 2011, Journal of medicinal chemistry.

[37]  Stefan Wetzel,et al.  The Scaffold Tree - Visualization of the Scaffold Universe by Hierarchical Scaffold Classification , 2007, J. Chem. Inf. Model..