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Journal of Computer-Aided Molecular Design

, Volume 31, Issue 8, pp 701–714 | Cite as

Predictive cartography of metal binders using generative topographic mapping

  • Igor I. Baskin
  • Vitaly P. Solov’ev
  • Alexander A. Bagatur’yants
  • Alexandre Varnek
Article

Abstract

Generative topographic mapping (GTM) approach is used to visualize the chemical space of organic molecules (L) with respect to binding a wide range of 41 different metal cations (M) and also to build predictive models for stability constants (logK) of 1:1 (M:L) complexes using “density maps,” “activity landscapes,” and “selectivity landscapes” techniques. A two-dimensional map describing the entire set of 2962 metal binders reveals the selectivity and promiscuity zones with respect to individual metals or groups of metals with similar chemical properties (lanthanides, transition metals, etc). The GTM-based global (for entire set) and local (for selected subsets) models demonstrate a good predictive performance in the cross-validation procedure. It is also shown that the data likelihood could be used as a definition of the applicability domain of GTM-based models. Thus, the GTM approach represents an efficient tool for the predictive cartography of metal binders, which can both visualize their chemical space and predict the affinity profile of metals for new ligands.

Keywords

Generative topographic mapping Metal binding Cartography of chemical space Activity landscapes 

Notes

Acknowledgements

This work was supported by the Russian Science Foundation Grant No. 14-43-00052, base organization Photochemistry Center, Russian Academy of Sciences. AAB thanks for partial support from the improving of the competitiveness program of National Research Nuclear University “MEPhI”.

Supplementary material

10822_2017_33_MOESM1_ESM.docx (4.4 mb)
Supplementary material includes: (1) activity landscapes for 41 metal ions, (2) density maps for the distribution of ligands for 41 metal ions, (3) optimal GTM parameters for global and local models, and (4) statistical characteristics of GTM-based regression models. (DOCX 4520 KB)

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Faculty of PhysicsM.V. Lomonosov Moscow State UniversityMoscowRussian Federation
  2. 2.Laboratory of Chemoinformatics, Butlerov Institute of ChemistryKazan Federal UniversityKazanRussian Federation
  3. 3.Institute of Physical Chemistry and ElectrochemistryRussian Academy of SciencesMoscowRussian Federation
  4. 4.Federal Research Center Crystallography and Photonics RASMoscowRussian Federation
  5. 5.National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)MoscowRussian Federation
  6. 6.Laboratoire de Chemoinformatique, UMR 7140Université de StrasbourgStrasbourgFrance

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