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From bird’s eye views to molecular communities: two-layered visualization of structure–activity relationships in large compound data sets

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Abstract

The analysis of structure–activity relationships (SARs) becomes rather challenging when large and heterogeneous compound data sets are studied. In such cases, many different compounds and their activities need to be compared, which quickly goes beyond the capacity of subjective assessments. For a comprehensive large-scale exploration of SARs, computational analysis and visualization methods are required. Herein, we introduce a two-layered SAR visualization scheme specifically designed for increasingly large compound data sets. The approach combines a new compound pair-based variant of generative topographic mapping (GTM), a machine learning approach for nonlinear mapping, with chemical space networks (CSNs). The GTM component provides a global view of the activity landscapes of large compound data sets, in which informative local SAR environments are identified, augmented by a numerical SAR scoring scheme. Prioritized local SAR regions are then projected into CSNs that resolve these regions at the level of individual compounds and their relationships. Analysis of CSNs makes it possible to distinguish between regions having different SAR characteristics and select compound subsets that are rich in SAR information.

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Acknowledgements

S.K. is supported by a Ph.D. fellowship from Region Alsace. We thank Martin Vogt for helpful discussions. The authors are grateful to OpenEye Scientific Software, Inc., for the free academic license of the OpenEye Toolkits.

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Authors and Affiliations

  1. Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstr. 2, 53113, Bonn, Germany

    Shilva Kayastha, Ryo Kunimoto & Jürgen Bajorath

  2. Laboratoire de Chemoinformatique, UMR 7140, Université de Strasbourg, 1 rue Blaise Pascal, 67000, Strasbourg, France

    Shilva Kayastha, Dragos Horvath & Alexandre Varnek

Authors
  1. Shilva Kayastha
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  2. Ryo Kunimoto
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  3. Dragos Horvath
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  4. Alexandre Varnek
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  5. Jürgen Bajorath
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Corresponding authors

Correspondence to Alexandre Varnek or Jürgen Bajorath.

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Kayastha, S., Kunimoto, R., Horvath, D. et al. From bird’s eye views to molecular communities: two-layered visualization of structure–activity relationships in large compound data sets. J Comput Aided Mol Des 31, 961–977 (2017). https://doi.org/10.1007/s10822-017-0070-1

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  • DOI: https://doi.org/10.1007/s10822-017-0070-1

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