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Predictive cartography of metal binders using generative topographic mapping

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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.

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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”.

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Authors and Affiliations

  1. Faculty of Physics, M.V. Lomonosov Moscow State University, Leninskie Gory, Moscow, Russian Federation, 119991

    Igor I. Baskin

  2. Laboratory of Chemoinformatics, Butlerov Institute of Chemistry, Kazan Federal University, Kazan, Russian Federation

    Igor I. Baskin

  3. Institute of Physical Chemistry and Electrochemistry, Russian Academy of Sciences, Leninskiy Prospect, 31, Moscow, Russian Federation, 119071

    Vitaly P. Solov’ev

  4. Federal Research Center Crystallography and Photonics RAS, ul. Novatorov 7a, Moscow, Russian Federation, 119421

    Alexander A. Bagatur’yants

  5. National Research Nuclear University MEPhI (Moscow Engineering Physics Institute), Kashirskoye Shosse 31, Moscow, Russian Federation, 115409

    Alexander A. Bagatur’yants

  6. Laboratoire de Chemoinformatique, UMR 7140, Université de Strasbourg, 1 Rue Blaise Pascal, 67000, Strasbourg, France

    Alexandre Varnek

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  1. Igor I. Baskin
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  2. Vitaly P. Solov’ev
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Correspondence to Alexandre Varnek.

Electronic supplementary material

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10822_2017_33_MOESM1_ESM.docx

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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Baskin, I.I., Solov’ev, V.P., Bagatur’yants, A.A. et al. Predictive cartography of metal binders using generative topographic mapping. J Comput Aided Mol Des 31, 701–714 (2017). https://doi.org/10.1007/s10822-017-0033-6

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  • DOI: https://doi.org/10.1007/s10822-017-0033-6

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