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

Fayet, Guillaume; Rotureau, Patricia; Joubert, Laurent; Adamo, Carlo

doi:10.1007/s00894-010-0908-0

Cited by 37 publications

(49 citation statements)

References 54 publications

(70 reference statements)

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“…Despite this, the general robustness of the models is testified by the fact that no loss of correlation was observed when passing from LOO to 2-fold cross-validations. Since in our previous work, [26] a MLR model was built by using only four descriptors, we have tested further variable reductions. In particular, we developed a new model by reducing the number of descriptors using the M5 method of WEKA software.…”

Section: Quantitative Approachesmentioning

confidence: 99%

“…Indeed, the number of nitro groups (n NO2 ) has been already observed to be directly correlated to the energy release during decomposition, [18] notably in our previous models. [26] Besides, the maximum net atomic charge, Q max , is also related to nitrogen atoms of nitro groups and the number of nitrogen atoms (n N ) also remains in the final set of seven descriptors. It has to be noticed that this last descriptor is related to the number of particular groups, including not only nitro groups but also amino and cyano groups.…”

Section: Descriptor Space Reductionmentioning

confidence: 99%

“…In that case, separating the reactional classes sensibly improved the robustness and predictivity of a multilinear regression (MLR) model. [26] Since MLR is the most common approach in QSAR/QSPR analyses, we have also developed a MLR model based on the reduced set of 7 descriptors presented in Section 3.2 that led to the following equation: …”

Section: Quantitative Approachesmentioning

confidence: 99%

“…A complete validation procedure yielded a high predictive power in its applicability domain (R in 2 = 0.86). [26] We also highlighted that substituents in ortho position to the nitro group can undergo specific decomposition mechanism by direct interaction with the nitro group [27,28] and this fact constituted the main difficulty behind the construction of a general reliable model describing the reactivity of nitroaromatic compounds.…”

Section: Introductionmentioning

confidence: 99%

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Predicting the Thermal Stability of Nitroaromatic Compounds Using Chemoinformatic Tools

Fayet

Río

Rotureau

et al. 2011

Molecular Informatics

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In the framework of the European REACH regulation major attention was recently devoted to toxicological and ecotoxicological problems while little attention has been dedicated to other important applications concerning chemical hazards, for instance, explosive properties. In this work different chemoinformatic tools such as partial least squares, multilinear regressions, and decision trees have been used for the development of a novel quantitative structure-property relationships to predict the heat of decomposition of a series of nitroaromatic compounds. Models were conceived in order to follow the regulatory requirements according to OECD principles for the validation of QSAR methods. Three models derived with MLR, PLS and decision tree techniques were developed, validated (internally and externally) and their applicability domains have been defined and analyzed. All models proved to be reliable with remarkable robustness in terms of full cross-validation scheme and showed good predictive power toward the external validation set. These models also present a large applicability domain within nitrobenzene derivatives and are easy to implement and interpret in terms of subjacent mechanisms.

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Section: Quantitative Approachesmentioning

confidence: 99%

Section: Descriptor Space Reductionmentioning

confidence: 99%

Section: Quantitative Approachesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Predicting the Thermal Stability of Nitroaromatic Compounds Using Chemoinformatic Tools

Fayet

Río

Rotureau

et al. 2011

Molecular Informatics

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“…Quantitative structure-property relationship (QSPR) studies have been successfully used for the prediction of physicochemical properties of chemical compounds based on their structures (19)(20)(21)(22)(23)(24). The biological counterpart of such studies, quantitative structure-activity relationships (QSAR), has also been extensively used with great success (25)(26)(27)(28).…”

Section: Introductionmentioning

confidence: 99%

Density functional theory based quantitative structure-property relationship studies on coumarin-based prodrugs

et al. 2012

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How to use QSPR‐type approaches to predict properties in the context of Green Chemistry

Fayet

Rotureau

2016

Biofuels Bioprod Bioref

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Green Chemistry is an active field of chemical research in which the ultimate targets are to promote high‐value, safe, clean products from renewable resources using inherently safer and clean processes. Until recently, the main efforts focused on the production of chemicals based on renewable resources by cleaner processes, notably by eliminating or substituting solvents. Quantitative Structure‐Property Relationships (QSPR) offer the opportunity to also take into account safety issues (in particular fire and explosion risks) in the early steps of development of chemicals and processes in the context of Green Chemistry. Based on robust methods for their development and validation, these predictive approaches allow access to the properties of substances based only on the knowledge of their molecular structures, even before their synthesis. QSPR models have been developed for various kinds of properties, including physico‐chemical hazards, for diverse families of compounds. They can be used as virtual screening tools to identify the best candidate among a series of possible compounds (within databases of products) for a target application and even in computer‐aided molecular design to propose alternative molecular structures, for instance for substitution purposes. So they represent very relevant tools to take into account the physico‐chemical hazards of substances together with targeted functional properties from the early stages of R&D projects toward safe‐by‐design “green” products and processes. © 2016 Society of Chemical Industry and John Wiley & Sons, Ltd

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

Cited by 37 publications

References 54 publications

Predicting the Thermal Stability of Nitroaromatic Compounds Using Chemoinformatic Tools

Predicting the Thermal Stability of Nitroaromatic Compounds Using Chemoinformatic Tools

Density functional theory based quantitative structure-property relationship studies on coumarin-based prodrugs

How to use QSPR‐type approaches to predict properties in the context of Green Chemistry

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