Uni. Insubria (Italy), QSAR Research Unit in Environmental Chemistry and Ecotoxicology http://hdl.handle.net/10967/156 2026-04-23T16:39:24Z 2026-04-23T16:39:24Z http://hdl.handle.net/10967/201 2017-05-24T21:12:23Z 2017-05-24T10:08:30Z

The rate constant for the nighttime degradation of 114 heterogeneous organic compounds, through reaction with nitrate radicals in the troposphere, is predicted here by quantitative structure–activity relationships modelling. The multiple linear regression approach is based on a variety of theoretical molecular descriptors, selected by the genetic algorithms-variable subset selection procedure. The proposed model, calculated on a limited subset of compounds selected by a D-optimal experimental design and checked for reliability and robustness, has good predictivity, verified by internal (Q_LMO^2=89.6%) and “external” validation (Q_EXT^2=95.7%). The model applicability domain was always verified by the leverage approach in order to propose reliable predicted data. The average root-mean square error for the prediction of logKNO3 was 0.57, similar to (and even smaller than) the typical experimental error range.

2017-05-24T10:08:30Z http://hdl.handle.net/10967/186 2017-03-02T22:27:16Z 2016-09-20T09:37:44Z

Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous pollutants of high environmental concern. The experimental data of a mutagenicity test on human B-lymphoblastoid cells (alternative to the Ames bacterial test) for a set of 70 oxo-, nitro- and unsubstituted PAHs, detected in particulate matter (PM), were modelled by Quantitative Structure-Activity Relationships (QSAR) classification methods (k-NN, k-Nearest Neighbour, and CART, Classification and Regression Tree) based on different theoretical molecular descriptors selected by Genetic Algorithms. The best models were validated for predictivity both externally and internally. For external validation, Self Organizing Maps (SOM) were applied to split the original data set. The best models, developed on the training set alone, show good predictive performance also on the prediction set chemicals (sensitivity 69.2–87.1%, specificity 62.5–87.5%). The classification of PAHs according to their mutagenicity, based only on a few theoretical molecular descriptors, allows a preliminary assessment of the human health risk, and the prioritisation of these compounds.

2016-09-20T09:37:44Z http://hdl.handle.net/10967/183 2020-02-07T20:52:06Z 2016-09-05T11:49:37Z

The crucial importance of the three central OECD principles for quantitative structure-activity relationship (QSAR) model validation is highlighted in a case study of tropospheric degradation of volatile organic compounds (VOCs) by OH, applied to two CADASTER chemical classes (PBDEs and (benzo-)triazoles). The application of any QSAR model to chemicals without experimental data largely depends on model reproducibility by the user. The reproducibility of an unambiguous algorithm (OECD Principle 2) is guaranteed by redeveloping MLR models based on both updated version of DRAGON software for molecular descriptors calculation and some freely available online descriptors. The Genetic Algorithm has confirmed its ability to always select the most informative descriptors independently on the input pool of variables. The ability of the GA-selected descriptors to model chemicals not used in model development is verified by three different splittings (random by response, K-ANN and K-means clustering), thus ensuring the external predictivity of the new models, independently of the training/prediction set composition (OECD Principle 5). The relevance of checking the structural applicability domain becomes very evident on comparing the predictions for CADASTER chemicals, using the new models proposed herein, with those obtained by EPI Suite.

2016-09-05T11:49:37Z http://hdl.handle.net/10967/182 2017-03-02T22:29:26Z 2016-08-29T13:00:14Z

A case study of toxicity of (benzo)triazoles ((B)TAZs) to the algae Pseudokirchneriella subcapitata is used to discuss some problems and solutions in QSAR modeling, particularly in the environmental context. The relevance of data curation (not only of experimental data, but also of chemical structures and input formats for the calculation of molecular descriptors), the crucial points of QSAR model validation and the potential application for new chemicals (internal robustness, exclusion of chance correlation, external predictivity, applicability domain) are described, while developing MLR-OLS models based on molecular descriptors, calculated by various QSAR software tools (commercial DRAGON, free PaDEL-Descriptor and QSPR-THESAURUS). Additionally, the utility of consensus models is highlighted. This work summarizes a methodology for a rigorous statistical approach to obtain reliable QSAR predictions, also for a large number of (B)TAZs in the ECHA preregistration list of REACH (even if starting from limited experimental data availability), and has evidenced some ambiguities and discrepancies related to SMILES notations from different databases; furthermore it highlighted some general problems related to QSAR model generation and was useful in the implementation of the PaDEL-Descriptor software.

2016-08-29T13:00:14Z