Literature DB >> 32554036

Correlation intensity index: Building up models for mutagenicity of silver nanoparticles.

Andrey A Toropov1, Alla P Toropova2.   

Abstract

Nanomaterials become significant component of economics. Consequently, nanomaterials become object of environmental sciences. There is a traditional list of endpoints which are indicators of the ecological risk. Mutagenicity is one of important component in this list. The quasi-SMILES approach, that in contrast to majority of work dedicated to modelling behaviour of nanomaterials gives possibility to consider experimental conditions as well as other circumstances which can impact the behaviour of nanomaterials is suggested. This is carried out via so-called quasi-SMILES. The quasi-SMILES is a line on of codes that contains all the above available eclectic data. Modelling process aimed to build up a model involves Correlation Intensity Index (CII) that is a new criterion of predictive potential of models. The scheme of calculation of CII is described in this work in the first time. The applying of CII together with Index of Ideality Correlation (IIC) in modelling of mutagenicity of silver nanoparticles by the Monte Carlo method using the CORAL software (http://www.insilico.eu/coral) indicates that application of the CII improves the predictive potential of these models for three random splits into the training set (75%) and validation set (25%).
Copyright © 2020 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Correlation intensity index; Index of ideality of correlation; Monte Carlo method; Mutagenicity; Quasi-SMILES; Silver nanoparticles

Mesh:

Substances:

Year:  2020        PMID: 32554036     DOI: 10.1016/j.scitotenv.2020.139720

Source DB:  PubMed          Journal:  Sci Total Environ        ISSN: 0048-9697            Impact factor:   7.963


  1 in total

1.  Monte Carlo Models for Sub-Chronic Repeated-Dose Toxicity: Systemic and Organ-Specific Toxicity.

Authors:  Gianluca Selvestrel; Giovanna J Lavado; Alla P Toropova; Andrey A Toropov; Domenico Gadaleta; Marco Marzo; Diego Baderna; Emilio Benfenati
Journal:  Int J Mol Sci       Date:  2022-06-14       Impact factor: 6.208

  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.