Regression model (regression)
Open in:QDB ExplorerQDB Predictor
Name | Type | n |
R2 |
σ |
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Training set | training | 180 | 0.884 | 0.518 |
Test set | testing | 70 | N/A | N/A |
When using this QDB archive, please cite (see details) it together with the original article:
Kahn, I. Data for: QSPR as a support for the EU REACH regulation and rational design of environmentally safer chemicals: PBT identification from molecular structure. QsarDB repository, QDB.180. 2016. https://doi.org/10.15152/QDB.180
Papa, E.; Gramatica, P. QSPR as a support for the EU REACH regulation and rational design of environmentally safer chemicals: PBT identification from molecular structure. Green Chem. 2010, 12, 836-843. https://doi.org/10.1039/b923843c
Title: | Papa, E.; Gramatica, P. QSPR as a support for the EU REACH regulation and rational design of environmentally safer chemicals: PBT identification from molecular structure. Green Chem. 2010, 12, 836-843. |
Abstract: | The chemicals that are jointly Persistent, Bioaccumulative and Toxic (PBT) are substances of very high concern (SVHC) and subject to an authorization step in the new European REACH regulation, which includes plans for safer substitutions of recognized hazardous compounds. The limited availability of experimental data necessary for the hazard/risk assessment of chemicals and the expected high costs have increased the interest, also in REACH, for alternative predictive in silico methods, such as Quantitative Structure–Activity (Property) Relationships (QSA(P)Rs). A structurally-based approach is proposed here for a holistic screening of potential PBTs in the environment. Persistence, bioconcentration and toxicity data available for a set of 180 organic chemicals, some of which are known PBTs, have been combined in a multivariate approach by Principal Component Analysis. This method is applied to rank the studied compounds according to their cumulative PBT behaviour; this ranking can be defined as a PBT Index. A simple, robust and externally predictive QSPR multiple linear regression model (MLR), which is based on four molecular descriptors, has been developed for the PBT Index. This QSPR model is proposed as a hazard screening tool, applicable also by regulators, for the early identification and prioritization of not yet known PBTs, only on the basis of the knowledge of their molecular structure. New, safer chemicals can be designed as alternatives to hazardous PBT chemicals by applying the proposed QSPR model, according to the green chemistry philosophy of “benign by design”. A consensus approach is also proposed from the comparison of the results obtained by different screening methods. |
URI: | http://hdl.handle.net/10967/180
http://dx.doi.org/10.15152/QDB.180 |
Date: | 2016-08-18 |
Name | Description | Format | Size | View |
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2010GC836.qdb.zip | QSAR for PBT Index | application/zip | 125.2Kb | View/ |