Development of the Latest Tools for Building up “Nano-QSAR”: Quantitative Features—Property/Activity Relationships (QFPRs/QFARs)
Computational studies of common compounds are already standard ways of their investigations. However, modeling properties of nanomaterials has been always a challenging task. This chapter reveals important differences between approaches applied to these two groups of species. The development of an optimal descriptor provides one of the efficient ways for the computational techniques to estimate endpoints related to nanospecies. Notably, the optimal descriptor can represent a translator of eclectic information into the endpoint prediction. Development of the optimal descriptor could start with consideration of a hybrid of topological indices calculated with the adjacency matrix of the molecular graph and application of additive scheme where a physicochemical parameter is modeled as the summation of contributions of molecular fragments. Further, the optimal descriptor might be advanced by taking into account contributions of various physicochemical conditions. Such contributions include presence/absence of defined chemical elements and/or defined kinds of covalent bonds, as well as different kinds of rings in the molecular system—factors which are able to modify the physicochemical (biochemical) behavior of a substance. Finally, the latest version of optimal descriptor involves the applications of eclectic data into building up model for endpoints related to nanomaterials. A recently acquired collection of models developed to predict various endpoints of nanomaterials is presented and discussed in this chapter.
KeywordsQSPR/QSAR Monte Carlo method CORAL software QFPR/QFAR
The authors are grateful to the EU project PROSIL funded under the LIFE program (Project LIFE12 ENV/IT/000154), the EC FP7 project NanoPUZZLES (Project Reference: 309837) and EU FP7 project PreNanoTox (contract 309666). D.L. and J.L. acknowledge support from the National Science Foundation (NSF/CREST HRD-0833178), and EPSCoR (Award #: 362492-190200-01/NSFEPS-090378).
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