The Monte Carlo method based on eclectic data as an efficient tool for predictions of endpoints for nanomaterials-two examples of application

Andrey A. Toropov, Alla P. Toropova, Aleksandar M. Veselinović, Jovana B. Veselinović, Karel Nesmerak, Ivan Raska, Pablo R. Duchowicz, Eduardo A. Castro, Valentin O. Kudyshkin, Danuta Leszczynska, Jerzy Leszczynski

Research output: Contribution to journalArticle

Abstract

The theoretical predictions of endpoints related to nanomaterials are attractive and more efficient alternatives for their experimental determinations. Such type of calculations for the "usual" substances (i.e. non nanomaterials) can be carried out with molecular graphs. However, in the case of nanomaterials, descriptors traditionally used for the quantitative structure-property/activity relationships (QSPRs/QSARs) do not provide reliable results since the molecular structure of nanomaterials, as a rule, cannot be expressed by the molecular graph. Innovative principles of computational prediction of endpoints related to nanomaterials extracted from available eclectic data (technological attributes, conditions of the synthesis, etc.) are suggested, applied to two different sets of data, and discussed in this work.

Original languageEnglish
Pages (from-to)376-386
Number of pages11
JournalCombinatorial Chemistry and High Throughput Screening
Volume18
Issue number4
Publication statusPublished - 2015

Keywords

  • CORAL software
  • Optimal descriptor
  • Quasi-QSPR/QSAR

ASJC Scopus subject areas

  • Organic Chemistry
  • Drug Discovery
  • Computer Science Applications
  • Medicine(all)

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  • Cite this

    Toropov, A. A., Toropova, A. P., Veselinović, A. M., Veselinović, J. B., Nesmerak, K., Raska, I., Duchowicz, P. R., Castro, E. A., Kudyshkin, V. O., Leszczynska, D., & Leszczynski, J. (2015). The Monte Carlo method based on eclectic data as an efficient tool for predictions of endpoints for nanomaterials-two examples of application. Combinatorial Chemistry and High Throughput Screening, 18(4), 376-386.