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

The Monte Carlo method based on eclectic data as an efficient tool for predictions of endpoints for nanomaterials - two examples of application.
Comb Chem High Throughput Screen. 2015 Mar 5;
Authors: Toropov AA, Toropova AP, Veselinović AM, Veselinović JB, Nesmerak K, Raska I, Duchowicz PR, Castro EA, Kudyshkin VO, Leszczynska D, Leszczynski J
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.
PMID: 25747446 [PubMed - as supplied by publisher]

Source: Combinatorial Chemistry and High Throughput Screening - March 5, 2015 Category: Chemistry Authors: Toropov AA, Toropova AP, Veselinović AM, Veselinović JB, Nesmerak K, Raska I, Duchowicz PR, Castro EA, Kudyshkin VO, Leszczynska D, Leszczynski J Tags: Comb Chem High Throughput Screen Source Type: research