Literature DB >> 25465947

Quasi-QSAR for mutagenic potential of multi-walled carbon-nanotubes.

Andrey A Toropov1, Alla P Toropova2.   

Abstract

Available on the Internet, the CORAL software (http://www.insilico.eu/coral) has been used to build up quasi-quantitative structure-activity relationships (quasi-QSAR) for prediction of mutagenic potential of multi-walled carbon-nanotubes (MWCNTs). In contrast with the previous models built up by CORAL which were based on representation of the molecular structure by simplified molecular input-line entry system (SMILES) the quasi-QSARs based on the representation of conditions (not on the molecular structure) such as concentration, presence (absence) S9 mix, the using (or without the using) of preincubation were encoded by so-called quasi-SMILES. The statistical characteristics of these models (quasi-QSARs) for three random splits into the visible training set and test set and invisible validation set are the following: (i) split 1: n=13, r(2)=0.8037, q(2)=0.7260, s=0.033, F=45 (training set); n=5, r(2)=0.9102, s=0.071 (test set); n=6, r(2)=0.7627, s=0.044 (validation set); (ii) split 2: n=13, r(2)=0.6446, q(2)=0.4733, s=0.045, F=20 (training set); n=5, r(2)=0.6785, s=0.054 (test set); n=6, r(2)=0.9593, s=0.032 (validation set); and (iii) n=14, r(2)=0.8087, q(2)=0.6975, s=0.026, F=51 (training set); n=5, r(2)=0.9453, s=0.074 (test set); n=5, r(2)=0.8951, s=0.052 (validation set).
Copyright © 2014 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  MWCNT; Monte Carlo method; Mutagenicity TA100; Quasi-QSAR

Mesh:

Substances:

Year:  2014        PMID: 25465947     DOI: 10.1016/j.chemosphere.2014.10.067

Source DB:  PubMed          Journal:  Chemosphere        ISSN: 0045-6535            Impact factor:   7.086


  7 in total

1.  Advantages and limitations of classic and 3D QSAR approaches in nano-QSAR studies based on biological activity of fullerene derivatives.

Authors:  Karolina Jagiello; Monika Grzonkowska; Marta Swirog; Lucky Ahmed; Bakhtiyor Rasulev; Aggelos Avramopoulos; Manthos G Papadopoulos; Jerzy Leszczynski; Tomasz Puzyn
Journal:  J Nanopart Res       Date:  2016-08-29       Impact factor: 2.253

2.  Environmental Risk Assessment Strategy for Nanomaterials.

Authors:  Janeck J Scott-Fordsmand; Willie J G M Peijnenburg; Elena Semenzin; Bernd Nowack; Neil Hunt; Danail Hristozov; Antonio Marcomini; Muhammad-Adeel Irfan; Araceli Sánchez Jiménez; Robert Landsiedel; Lang Tran; Agnes G Oomen; Peter M J Bos; Kerstin Hund-Rinke
Journal:  Int J Environ Res Public Health       Date:  2017-10-19       Impact factor: 3.390

Review 3.  Metal Oxide Nanomaterial QNAR Models: Available Structural Descriptors and Understanding of Toxicity Mechanisms.

Authors:  Jiali Ying; Ting Zhang; Meng Tang
Journal:  Nanomaterials (Basel)       Date:  2015-10-12       Impact factor: 5.076

4.  Towards the Development of Global Nano-Quantitative Structure-Property Relationship Models: Zeta Potentials of Metal Oxide Nanoparticles.

Authors:  Andrey A Toropov; Natalia Sizochenko; Alla P Toropova; Jerzy Leszczynski
Journal:  Nanomaterials (Basel)       Date:  2018-04-15       Impact factor: 5.076

5.  An ISA-TAB-Nano based data collection framework to support data-driven modelling of nanotoxicology.

Authors:  Richard L Marchese Robinson; Mark T D Cronin; Andrea-Nicole Richarz; Robert Rallo
Journal:  Beilstein J Nanotechnol       Date:  2015-10-05       Impact factor: 3.649

6.  Identfication of Potent LXRβ-Selective Agonists without LXRα Activation by In Silico Approaches.

Authors:  Meimei Chen; Fafu Yang; Jie Kang; Huijuan Gan; Xuemei Yang; Xinmei Lai; Yuxing Gao
Journal:  Molecules       Date:  2018-06-04       Impact factor: 4.411

Review 7.  Nano-(Q)SAR for Cytotoxicity Prediction of Engineered Nanomaterials.

Authors:  Andrey A Buglak; Anatoly V Zherdev; Boris B Dzantiev
Journal:  Molecules       Date:  2019-12-11       Impact factor: 4.411

  7 in total

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