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Multimedia environmental chemical partitioning from molecular information

The prospect of assessing the environmental distribution of chemicals directly from their molecular information was analyzed. Multimedia chemical partitioning of 455 chemicals, expressed in dimensionless compartmental mass ratios, was predicted by SimpleBox 3, a Level III Fugacity model, together with the propagation of reported uncertainty for key physicochemical and transport properties, and degradation rates. Chemicals, some registered in priority lists, were selected according to the availability of experimental property data to minimize the influence of predicted information in model development. Chemicals were emitted in air or water in a fixed geographical scenario representing the Netherlands and characterized by five compartments (air, water, sediments, soil and vegetation). Quantitative structure–fate relationship (QSFR) models to predict mass ratios in different compartments were developed with support vector regression algorithms. A set of molecular descriptors, including the molecular weight and 38 counts of molecular constituents were adopted to characterize the chemical space. Out of the 455 chemicals, 375 were used for training and testing the QSFR models, while 80 were excluded from model development and were used as an external validation set. Training and test chemicals were selected and the domain of applicability (DOA) of the QSFRs established by means of self-organizing maps according to structural similarity. Best results were obtained with QSFR models developed for chemicals belonging to either the class [C] and [C; O], or the class with at least one heteroatom different than oxygen in the structure. These two class-specific models, with respectively 146 and 229 chemicals, showed a predictive squared coefficient of q2≥0.90 both for air and water, which respectively dropped to q2 ≈ 0.70 and 0.40 for outlying chemicals. Prediction errors were of the same order of magnitude as the deviations associated to the uncertainty of the physicochemical and transport properties, and degradation rates.

Martinez I, Grifoll J, Rallo R, Giralt F (2010) Multimedia environmental chemical partitioning from molecular information. Science of the Total Environment, 409(2):412-422

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Using an ensemble of neural based QSARs for the prediction of toxicological properties of chemical contaminants

An ensemble of neural predictors is used to develop a set of QSAR models for the prediction of the carcinogenicity TD50 index. The proposed approach makes use of the self organizing feature map algorithm to select diverse subsets of molecular descriptors that are used afterwards to train an ensemble of predictive fuzzy ARTMAP networks. The diversity introduced by the predictors trained using different subsets of descriptors produces better generalization results than single models. Comparison of the developed models with published models is to be used to assess the quality of the prediction system.

Rallo, R., Espinosa, G., and F. Giralt (2005) Using an ensemble of neural based QSARs for the prediction of toxicological properties of chemical contaminants, Trans IChemE Part B. Process Safety and Environmental Protection, 83(B4), 387-392

DOI: 10.1205/psep.04389