Literature DB >> 21174136

Development of a QSPR model for predicting thermal stabilities of nitroaromatic compounds taking into account their decomposition mechanisms.

Guillaume Fayet1, Patricia Rotureau, Laurent Joubert, Carlo Adamo.   

Abstract

The molecular structures of 77 nitroaromatic compounds have been correlated to their thermal stabilities by combining the quantitative structure-property relationship (QSPR) method with density functional theory (DFT). More than 300 descriptors (constitutional, topological, geometrical and quantum chemical) have been calculated, and multilinear regressions have been performed to find accurate quantitative relationships with experimental heats of decomposition (-ΔH). In particular, this work demonstrates the importance of accounting for chemical mechanisms during the selection of an adequate experimental data set. A reliable QSPR model that presents a strong correlation with experimental data for both the training and the validation molecular sets (R (2) = 0.90 and 0.84, respectively) was developed for non-ortho-substituted nitroaromatic compounds. Moreover, its applicability domain was determined, and the model's predictivity reached 0.86 within this applicability domain. To our knowledge, this work has produced the first QSPR model, developed according to the OECD principles of regulatory acceptability, for predicting the thermal stabilities of energetic compounds.

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Year:  2010        PMID: 21174136     DOI: 10.1007/s00894-010-0908-0

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


  20 in total

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2.  Quantitative structure-property relationships in pharmaceutical research - Part 2.

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Journal:  Pharm Sci Technolo Today       Date:  2000-02

3.  QSPR analysis of flash points.

Authors:  A R Katritzky; R Petrukhin; R Jain; M Karelson
Journal:  J Chem Inf Comput Sci       Date:  2001 Nov-Dec

4.  Comparative QSAR analysis of estrogen receptor ligands.

Authors:  H Gao; J A Katzenellenbogen; R Garg; C Hansch
Journal:  Chem Rev       Date:  1999-03-10       Impact factor: 60.622

5.  Prediction of reactive hazards based on molecular structure.

Authors:  S R Saraf; W J Rogers; M S Mannan
Journal:  J Hazard Mater       Date:  2003-03-17       Impact factor: 10.588

Review 6.  Comparative QSAR and the radical toxicity of various functional groups.

Authors:  Cynthia D Selassie; Rajni Garg; Sanjay Kapur; Alka Kurup; Rajeshwar P Verma; Suresh Babu Mekapati; Corwin Hansch
Journal:  Chem Rev       Date:  2002-07       Impact factor: 60.622

7.  Integrating process safety with molecular modeling-based risk assessment of chemicals within the REACH regulatory framework: benefits and future challenges.

Authors:  Amanda Lewis; Nikolaos Kazantzis; Ilie Fishtik; Jennifer Wilcox
Journal:  J Hazard Mater       Date:  2006-06-28       Impact factor: 10.588

8.  QSPR modeling of thermal stability of nitroaromatic compounds: DFT vs. AM1 calculated descriptors.

Authors:  Guillaume Fayet; Patricia Rotureau; Laurent Joubert; Carlo Adamo
Journal:  J Mol Model       Date:  2010-01-05       Impact factor: 1.810

9.  Simple method for prediction of activation energies of the thermal decomposition of nitramines.

Authors:  Mohammad Hossein Keshavarz
Journal:  J Hazard Mater       Date:  2008-06-21       Impact factor: 10.588

10.  Theoretical study of the decomposition reactions in substituted nitrobenzenes.

Authors:  Guillaume Fayet; Laurent Joubert; Patricia Rotureau; Carlo Adamo
Journal:  J Phys Chem A       Date:  2008-04-05       Impact factor: 2.781

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  1 in total

1.  Applying machine learning techniques to predict the properties of energetic materials.

Authors:  Daniel C Elton; Zois Boukouvalas; Mark S Butrico; Mark D Fuge; Peter W Chung
Journal:  Sci Rep       Date:  2018-06-13       Impact factor: 4.379

  1 in total

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