The Monte Carlo Method Based on Eclectic Data as an Efficient Tool for Predictions of Endpoints for Nanomaterials - Two Examples of Application

Title:The Monte Carlo Method Based on Eclectic Data as an Efficient Tool for Predictions of Endpoints for Nanomaterials - Two Examples of Application

Volume: 18 Issue: 4

Author(s): Andrey A. Toropov, Alla P. Toropova, Aleksandar M. Veselinovic, Jovana B. Veselinovic, Karel Nesmerak, Ivan Raska, Pablo R. Duchowicz, Eduardo A. Castro, Valentin O. Kudyshkin, Danuta Leszczynska and Jerzy Leszczynski

Affiliation:

Keywords: CORAL software, optimal descriptor, Quasi-QSPR/QSAR.

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.

Cite this article as:

A. Toropov Andrey, P. Toropova Alla, M. Veselinovic Aleksandar, B. Veselinovic Jovana, Nesmerak Karel, Ivan Raska , R. Duchowicz Pablo, A. Castro Eduardo, O. Kudyshkin Valentin, Leszczynska Danuta and Leszczynski Jerzy, The Monte Carlo Method Based on Eclectic Data as an Efficient Tool for Predictions of Endpoints for Nanomaterials - Two Examples of Application, Combinatorial Chemistry & High Throughput Screening 2015; 18 (4) . https://dx.doi.org/10.2174/1386207318666150305125044