|Table of Contents|

Prediction of Biodegradability of Substituted Phenols and Benzoic Acids with Novel Molecular Connectivity Index and Artificial Neural Network

《南京理工大学学报》（自然科学版）[ISSN:1005-9830/CN:32-1397/N]

Issue:
2009年05期
Page:
700-706
Research Field:
Publishing date:

Info

Title:
Prediction of Biodegradability of Substituted Phenols and Benzoic Acids with Novel Molecular Connectivity Index and Artificial Neural Network
Author(s):
FENG Chang-jun1; DU Xi-hua1; MU Lai-long2
1.School of Chemistry and Chemical Engineering,Xuzhou Institute of Technology,Xuzhou 221008,China;2.School of Chemistry and Chemical Engineering,Xuzhou Normal University,Xuzhou 221116,China
Keywords:
molecular connectivity index;  substituted phenol;  substituted benzoic acid;  biochemical oxygen demand;  artificial neural network;  quantitative structure-biodegradability relationship
PACS:
X172;X592
DOI:
-
Abstract:
On the basis of the revision of Kier and Hall’s molecular connectivity index and conjugation matrix,a novel molecular connectivity indexmKvt is defined and calculated for 30 substituted phenol and benzoic acid molecules in this paper.The quantitative structure-biodegradability relationship(QSBR) model between biochemical oxygen demand(BOD) and 2Kvp,5Kvp for 25 organic pollutants among above molecules is developed by Leaps-and-Bounds regression(LBR),the traditional correlation coefficient R2,the cross-validation correlation coefficient Q2 of leave-one-out(LOO) and Kubinyi function(FIT) are 0.818,0.776 and 3.410,respectively.The result demonstrates that the model is highly reliable and has good predictive ability from the point of statistics.The model shows that the dominant influencing factors of BOD are the electronic effects of substituents and the space factors of molecule: the flexibility and the puckered degree of molecules.The two structural parameters are used as the input neurons of the artificial neural network,and a 2∶5∶1 network architecture is employed.A satisfying QSBR model can be constructed with the back-propagation algorithm,with the correlation coefficient R2 and the standard errors being 0.967 and 3.688,respectively,showing that the relationship between BOD and the two structural parameters has a good nonlinear correlation.The results show that the new molecular connectivity index have good rationality and efficiency for the biochemical oxygen demand of organic compounds.It can be expected that the mKvt will be used widely in quantitative structure-property/activity relationship research.

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Last Update: 2012-11-19