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

Guillaume Fayet; Patricia Rotureau

doi:10.1002/bbb.1723

journal article Sep 27, 2016

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

Guillaume Fayet Patricia Rotureau

Biofuels, Bioproducts and Biorefining Vol. 10 No. 6 pp. 738-752 · Wiley

View at Publisher Save 10.1002/bbb.1723

Abstract

AbstractGreen 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

Topics

No keywords indexed for this article. Browse by subject →

References

88

[1]

Anastas PT (1998)

[2]

10.1039/c4ta00565a

[3]

10.1039/c0gc00407c

[4]

10.1021/ar010065m

[5]

10.1016/s1066-7938(02)00068-4

[6]

10.1021/op060160g

[7]

10.1039/b617536h

[8]

Clinical development success rates for investigational drugs

Michael Hay, David W Thomas, John L Craighead et al.

Nature Biotechnology 10.1038/nbt.2786

[9]

10.1016/0009-9236(95)90066-7

[10]

10.1080/10473289.1991.10466899

[11]

10.1039/b811909k

[12]

Cramer CJ (2004)

[13]

10.1002/0471220655

[14]

A General Guidebook for the Theoretical Prediction of Physicochemical Properties of Chemicals for Regulatory Purposes

Carlos Nieto-Draghi, Guillaume Fayet, Benoit Creton et al.

Chemical Reviews 10.1021/acs.chemrev.5b00215

[15]

10.1002/minf.201300180

[16]

Bajot F (2010)

[17]

10.1016/s0166-1280(02)00614-0

[18]

10.1002/qsar.200710117

[19]

10.1021/cr900238d

[20]

10.1080/1062936x.2013.773372

[21]

10.1021/ie301079r

[22]

Karelson M (2000)

[23]

10.1002/9783527613106

[24]

10.1002/anie.199305031

[25]

10.1002/9780470116449.ch6

[26]

10.1002/qsar.200810066

[27]

10.1021/cr3004339

[28]

Valadi J (2014) 10.1007/978-3-319-06508-3

[29]

10.1080/10629360902949567

[30]

Best Practices for QSAR Model Development, Validation, and Exploitation

Alexander Tropsha

Molecular Informatics 10.1002/minf.201000061

[31]

10.1002/qsar.200610151

[32]

10.1021/ci700157b

[33]

10.1021/ci200211n

[34]

10.1021/ci300084j

[35]

10.1002/cem.1290

[36]

10.1177/026119290503300508

[37]

Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARs.

Lennart Eriksson, Joanna Jaworska, Andrew P Worth et al.

Environmental Health Perspectives 10.1289/ehp.5758

[38]

10.1021/cr200066h

[39]

Regulation (EC) N°1272/2008 of the European Parliament and of the Council of 16 December 2008 on classification labelling and packaging of substances and mixtures amending and repealing Directives 67/548/EEC and 1999/45/EC and amending Regulation (EC) N°1907/2006.

[40]

10.1007/s00894-010-0908-0

[41]

10.1021/jp905979w

[42]

10.1016/j.jlp.2014.04.005

[43]

10.18356/3821b22f-en

[44]

10.1002/prs.11499

[45]

10.1002/minf.201000077

[46]

10.1016/j.jhazmat.2014.05.009

[47]

10.1002/jcc.540140402

[48]

10.1016/s0040-4020(01)85017-1

[49]

10.1021/ie2023788

[50]

10.1021/ef4005362

Showing 50 of 88 references

Metrics

15

Citations

88

References

Details

Published: Sep 27, 2016
Vol/Issue: 10(6)
Pages: 738-752
License: View

Authors

G

Guillaume Fayet

INERIS Verneuil‐en‐Halatte France

P

Patricia Rotureau

INERIS Verneuil‐en‐Halatte France

Cite This Article

Guillaume Fayet, Patricia Rotureau (2016). How to use QSPR‐type approaches to predict properties in the context of Green Chemistry. Biofuels, Bioproducts and Biorefining, 10(6), 738-752. https://doi.org/10.1002/bbb.1723

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

You May Also Like