Optimal descriptor as a translator of eclectic information into the prediction of membrane damage by means of various TiO(2) nanoparticles.

The increasing use of nanomaterials incorporated into consumer products leads to the need for developing approaches to establish "quantitative structure-activity relationships" (QSARs) for various nanomaterials. However, the molecular structure as rule is not available for nanomaterials at least in its classic meaning. An possible alternative of classic QSAR (based on the molecular structure) is the using of data on physicochemical features of TiO(2) nanoparticles. The damage to cellular membranes (units L(-1)) by means of various TiO(2) nanoparticles is examined as the endpoint.

[1]  Eduardo A. Castro,et al.  QSAR treatment on a new class of triphenylmethyl-containing compounds as potent anticancer agents , 2011 .

[2]  Andrey A. Toropov,et al.  QSPR modeling of alkanes properties based on graph of atomic orbitals , 2003 .

[3]  Kunal Roy,et al.  Docking and 3D-QSAR studies of acetohydroxy acid synthase inhibitor sulfonylurea derivatives , 2010, Journal of molecular modeling.

[4]  Giuseppina C. Gini,et al.  CORAL: Quantitative structure–activity relationship models for estimating toxicity of organic compounds in rats , 2011, J. Comput. Chem..

[5]  Andrey A. Toropov,et al.  QSAR modeling of toxicity on optimization of correlation weights of Morgan extended connectivity , 2002 .

[6]  I. Ivanov,et al.  Comparative Study of Predictive Computational Models for Nanoparticle‐Induced Cytotoxicity , 2010, Risk analysis : an official publication of the Society for Risk Analysis.

[7]  George Kollias,et al.  Ligand-based virtual screening procedure for the prediction and the identification of novel β-amyloid aggregation inhibitors using Kohonen maps and Counterpropagation Artificial Neural Networks. , 2011, European journal of medicinal chemistry.

[8]  Jerzy Leszczynski,et al.  Improved model for fullerene C60 solubility in organic solvents based on quantum-chemical and topological descriptors , 2011 .

[9]  Jerzy Leszczynski,et al.  CORAL: QSPR model of water solubility based on local and global SMILES attributes. , 2013, Chemosphere.

[10]  I. Gutman,et al.  Relation between second and third geometric–arithmetic indices of trees , 2011 .

[11]  Eduardo A. Castro,et al.  QSAR Study and Molecular Design of Open-Chain Enaminones as Anticonvulsant Agents , 2011, International journal of molecular sciences.

[12]  Jerzy Leszczynski,et al.  SMILES-based QSAR approaches for carcinogenicity and anticancer activity: comparison of correlation weights for identical SMILES attributes. , 2011, Anti-cancer agents in medicinal chemistry.

[13]  Eduardo A. Castro,et al.  QSAR on aryl-piperazine derivatives with activity on malaria , 2012 .

[14]  Jerzy Leszczynski,et al.  Author ' s personal copy QSAR as a random event : Modeling of nanoparticles uptake in PaCa 2 cancer cells , 2013 .

[15]  Jerzy Leszczynski,et al.  Novel application of the CORAL software to model cytotoxicity of metal oxide nanoparticles to bacteria Escherichia coli. , 2012, Chemosphere.

[16]  T. Puzyn,et al.  Toward the development of "nano-QSARs": advances and challenges. , 2009, Small.

[17]  Jerzy Leszczynski,et al.  Optimal descriptor as a translator of eclectic information into the prediction of thermal conductivity of micro-electro-mechanical systems , 2013, Journal of Mathematical Chemistry.

[18]  Jerzy Leszczynski,et al.  QSAR modeling of acute toxicity by balance of correlations. , 2008, Bioorganic & medicinal chemistry.

[19]  Humayun Kabir,et al.  Comparative Studies on Some Metrics for External Validation of QSPR Models , 2012, J. Chem. Inf. Model..

[20]  Pablo R Duchowicz,et al.  A comparative QSAR on 1,2,5-thiadiazolidin-3-one 1,1-dioxide compounds as selective inhibitors of human serine proteinases. , 2011, Journal of molecular graphics & modelling.

[21]  K. Roy,et al.  Further exploring rm2 metrics for validation of QSPR models , 2011 .

[22]  Emilio Benfenati,et al.  Co-evolutions of correlations for QSAR of toxicity of organometallic and inorganic substances: An unexpected good prediction based on a model that seems untrustworthy , 2011 .

[23]  A. Tropsha,et al.  Quantitative nanostructure-activity relationship modeling. , 2010, ACS nano.

[24]  Jerzy Leszczynski,et al.  Predicting thermal conductivity of nanomaterials by correlation weighting technological attributes codes , 2007 .