Literature DB >> 26874948

Estimation of melting points of large set of persistent organic pollutants utilizing QSPR approach.

Marquita Watkins1, Natalia Sizochenko1, Bakhtiyor Rasulev1,2, Jerzy Leszczynski3.   

Abstract

The presence of polyhalogenated persistent organic pollutants (POPs), such as Cl/Br-substituted benzenes, biphenyls, diphenyl ethers, and naphthalenes has been identified in all environmental compartments. The exposure to these compounds can pose potential risk not only for ecological systems, but also for human health. Therefore, efficient tools for comprehensive environmental risk assessment for POPs are required. Among the factors vital for environmental transport and fate processes is melting point of a compound. In this study, we estimated the melting points of a large group (1419 compounds) of chloro- and bromo- derivatives of dibenzo-p-dioxins, dibenzofurans, biphenyls, naphthalenes, diphenylethers, and benzenes by utilizing quantitative structure-property relationship (QSPR) techniques. The compounds were classified by applying structure-based clustering methods followed by GA-PLS modeling. In addition, random forest method has been applied to develop more general models. Factors responsible for melting point behavior and predictive ability of each method were discussed.

Entities:  

Keywords:  Melting point; Organic pollutants; POPs; Partial least squares; QSPR; Random forest

Mesh:

Substances:

Year:  2016        PMID: 26874948     DOI: 10.1007/s00894-016-2917-0

Source DB:  PubMed          Journal:  J Mol Model        ISSN: 0948-5023            Impact factor:   1.810


  18 in total

1.  Structurally diverse quantitative structure--property relationship correlations of technologically relevant physical properties

Authors: 
Journal:  J Chem Inf Comput Sci       Date:  2000-01

2.  QSPR correlation of the melting point for pyridinium bromides, potential ionic liquids.

Authors:  Alan R Katritzky; Andre Lomaka; Ruslan Petrukhin; Ritu Jain; Mati Karelson; Ann E Visser; Robin D Rogers
Journal:  J Chem Inf Comput Sci       Date:  2002 Jan-Feb

3.  QSAR approach to POPs screening for atmospheric persistence.

Authors:  P Gramatica; F Consolaro; S Pozzi
Journal:  Chemosphere       Date:  2001 May-Jun       Impact factor: 7.086

4.  Partial least squares modeling and genetic algorithm optimization in quantitative structure-activity relationships.

Authors:  K Hasegawa; K Funatsu
Journal:  SAR QSAR Environ Res       Date:  2000       Impact factor: 3.000

5.  The QSAR prediction of melting point, a property of environmental relevance.

Authors:  J C Dearden
Journal:  Sci Total Environ       Date:  1991-12       Impact factor: 7.963

6.  Prediction of rate constants for radical degradation of aromatic pollutants in water matrix: a QSAR study.

Authors:  Hrvoje Kusić; Bakhtiyor Rasulev; Danuta Leszczynska; Jerzy Leszczynski; Natalija Koprivanac
Journal:  Chemosphere       Date:  2009-02-07       Impact factor: 7.086

7.  Quantitative relationships between molecular structures, environmental temperatures and solid vapor pressures of PCDD/Fs.

Authors:  Guanghui Ding; Jingwen Chen; Xianliang Qiao; Liping Huang; Jing Lin; Xiaoyang Chen
Journal:  Chemosphere       Date:  2005-07-01       Impact factor: 7.086

Review 8.  The impact of endocrine disrupters on the female reproductive system.

Authors:  P Nicolopoulou-Stamati; M A Pitsos
Journal:  Hum Reprod Update       Date:  2001 May-Jun       Impact factor: 15.610

9.  Predicting water solubility of congeners: chloronaphthalenes--a case study.

Authors:  Tomasz Puzyn; Aleksandra Mostrag; Jerzy Falandysz; Yana Kholod; Jerzy Leszczynski
Journal:  J Hazard Mater       Date:  2009-05-22       Impact factor: 10.588

10.  QSAR models for CXCR2 receptor antagonists based on the genetic algorithm for data preprocessing prior to application of the PLS linear regression method and design of the new compounds using in silico virtual screening.

Authors:  Tahereh Asadollahi; Shayessteh Dadfarnia; Ali Mohammad Haji Shabani; Jahan B Ghasemi; Maryam Sarkhosh
Journal:  Molecules       Date:  2011-02-25       Impact factor: 4.411

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  1 in total

1.  Modeling Physico-Chemical ADMET Endpoints with Multitask Graph Convolutional Networks.

Authors:  Floriane Montanari; Lara Kuhnke; Antonius Ter Laak; Djork-Arné Clevert
Journal:  Molecules       Date:  2019-12-21       Impact factor: 4.411

  1 in total

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