The Monte Carlo method based on eclectic data as an efficient tool for predictions of endpoints for nanomaterials - two examples of application.
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.
IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa 19, 20156 Milano, Italy. andrey.toropov@marionegri.it.
Andrey A
AA
Toropov
Alla P
AP
Toropova
Aleksandar M
AM
Veselinović
Jovana B
JB
Veselinović
Karel
K
Nesmerak
Ivan
I
Raska
Pablo R
PR
Duchowicz
Eduardo A
EA
Castro
Valentin O
VO
Kudyshkin
Danuta
D
Leszczynska
Jerzy
J
Leszczynski
D015394:Molecular Structure; D009010:Monte Carlo Method; D049329:Nanostructures / Q000737:chemistry*; D021281:Quantitative Structure-Activity Relationship