QSPR prediction of physico-chemical properties for REACH

Dearden, John C.; Rotureau, Patricia; Fayet, Guillaume

doi:10.1080/1062936x.2013.773372

Cited by 61 publications

(55 citation statements)

References 132 publications

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“…Typically, these QSARs assume an additive behavior of contributions that come from the various molecular fragments of the chemical. Other QSARs, such as for air/water partitioning, are even more limited because much smaller calibration data sets are available (roughly 1000 data points for air/water and considerably fewer for all other environmental partition systems [4,8]). However, even QSARs for octanol/water partition coefficients (K OW ), for which more than 10 000 experimental data are available, appear not to have enough of those correction factors to perform well as a predictive tool for new chemical structures.…”

Section: Introductionmentioning

confidence: 99%

Prediction of partition coefficients for complex environmental contaminants: Validation of COSMOtherm, ABSOLV, and SPARC

Stenzel

Goss

Endo

2014

Enviro Toxic and Chemistry

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Prediction of partition coefficients is essential for screening of environmentally relevant compounds. Prediction methods using only the molecular structure as input are especially useful for this purpose. In the present study, the authors validated 3 prediction method-COSMOtherm, ABSOLV, and SPARC-which are based on more mechanistic approaches than most other quantitative structure-activity relationships. Validation was based on a consistent experimental data set of up to 270 compounds, mostly pesticides and flame retardants. The validation systems included 3 gas chromatographic (GC) columns and 4 liquid/liquid systems that represent all relevant types of intermolecular interactions. Results revealed that the overall prediction accuracy of COSMOtherm and ABSOLV is comparable, whereas SPARC performance is substantially lower than the other methods. For instance, the root mean squared error for the 4 liquid/liquid partition coefficients was 0.65 log units to 0.93 log units for COSMOtherm, 0.64 log units to 0.95 log units for ABSOLV, and 1.43 to 2.85 log units for SPARC. In addition, version and parameterization influences of COSMOtherm on the prediction accuracy were determined.

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Section: Introductionmentioning

confidence: 99%

Prediction of partition coefficients for complex environmental contaminants: Validation of COSMOtherm, ABSOLV, and SPARC

Stenzel

Goss

Endo

2014

Enviro Toxic and Chemistry

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“…Some recent reviews [19][20][21] list the existing predictive models developed for the relevant properties of chemicals in the context of REACH.…”

Section: Full Papermentioning

confidence: 99%

Development of Simple QSPR Models for the Prediction of the Heat of Decomposition of Organic Peroxides

Prana

Rotureau

André

et al. 2017

Molecular Informatics

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Quantitative structure-property relationships represent alternative method to experiments to access the estimation of physico-chemical properties of chemicals for screening purpose at R&D level but also to gather missing data in regulatory context. In particular, such predictions were encouraged by the REACH regulation for the collection of data, provided that they are developed respecting the rigorous principles of validation proposed by OECD. In this context, a series of organic peroxides, unstable chemicals which can easily decompose and may lead to explosion, were investigated to develop simple QSPR models that can be used in a regulatory framework. Only constitutional and topological descriptors were employed to achieve QSPR models predicting the heat of decomposition, which could be used without any time consuming preliminary structure calculations at quantum chemical level. To validate the models, the original experimental dataset was divided into a training and a validation set according to two methods of partitioning, one based on the property value and the other based on the structure of the molecules by the mean of PCA. Four QSPR models were developed upon the type of descriptors and the methods of partitioning. The 2 models issuing from the PCA based method were highlighted as they presented good predictive power and they are easier to apply than our previous quantum chemical based model, since they do not need any preliminary calculations.

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“…QSPR models have been developed and reviewed for years for very diverse properties of chemicals 19 (notably related to the REACH regulation), 14,20 of materials, 38 and also for hazardous substances. 14,20,21 Table 1 summarizes the capabilities of QSPR models available to predict physicochemical properties used to classify substances according to the European regulation related to the Classifi cation, Labelling, and Packaging of Substances and Mixtures. 39 Th is table indicates that QSPR models nowadays concern only one part of the properties required to classify chemicals according to physical hazards.…”

Section: Existing Models For Hazardous Physico-chemical Propertiesmentioning

confidence: 99%

“…Today, among existing computational approaches to estimate the properties of chemical products, Quantitative Structure‐Activity/Property Relationships (QSAR/QSPR) represent a very active and productive field of research. Indeed, the number of research articles in this field increased from 164 to 914 per year between 1993 and 2012 and extended in application fields from drug design and toxicology to environmental sciences, physico‐chemical properties, and safety . In drug design, QSAR/QSPR models are considered as low‐cost tools to estimate physico‐chemical properties and biological activities for the selection and for the optimization of promising chemical structures.…”

Section: Introductionmentioning

confidence: 99%

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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QSPR prediction of physico-chemical properties for REACH

Cited by 61 publications

References 132 publications

Prediction of partition coefficients for complex environmental contaminants: Validation of COSMOtherm, ABSOLV, and SPARC

Prediction of partition coefficients for complex environmental contaminants: Validation of COSMOtherm, ABSOLV, and SPARC

Development of Simple QSPR Models for the Prediction of the Heat of Decomposition of Organic Peroxides

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

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