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From a training set of 7200 chemicals, a back-propagation neural network (BNN) model was developed for calculating the 1-octanol/water partition coefficient (log P) of molecules containing nitrogen, oxygen, halogen, phosphorus, and/or sulfur atoms. Chemicals were described by means of autocorrelation vectors encoding hydrophobicity, molar refractivity, H-bonding acceptor ability, and H-bonding donor ability. A 35/32/1 composite network composed of four configurations was selected as the final model (root-mean-square error (RMS) = 0.37, r = 0.97) because it provided the best simulation results (RMS = 0.39, r = 0.98) on an external testing set of 519 molecules. This final model compared favorably with a recently published BNN model using variables (atoms and bonds) derived from connection matrices.

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Numerous drugs have been identified as presenting adverse effects towards the driving of vehicles. A large set of these drugs was compiled and classified into ten categories. Nonlinear neural mapping (N2M) was used to derive a typology of these molecules and also to link their adverse effects to therapeutic categories and structural information.

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Mathematical simulation models of fate and transport of chemicals have been identified by researchers and regulators as potentially valuable tools for improving the understanding of the environmental behavior of chemicals which may be released to the environment as a consequence of routine (i.e., normal manufacturing, use, disposal) and non-routine (e.g., accidental spillage) events. In this context, CHEMFRANCE, a regional fugacity model level III, which calculates the environmental distribution of organic chemicals in 12 defined regions of France, or France as a whole, has been designed. The aim of this study is to show that CHEMFRANCE provides valuable simulation results for understanding the environmental fate behavior of atrazine.

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This article presents a validation exercise performed from eight practical case studies on EASE (version 2.0), a knowledge-based system allowing to estimate the workplace exposure to chemicals. Our results show that EASE represents a valuable simulation tool in occupational hygiene. However, it requires to be refined and extended to more realistic and precise situations to be easily used in practice.

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Abstract A large data set of Microtox® toxicity results was used to derive a general QSAR model. Chemicals were described by means of a modified autocorrelation method. The autocorrelation vectors were generated from atomic contributions encoding the hydrophobicity and molar refractivity of the molecules. A three-layer backpropagation neural network was used to design the model. The obtained results were compared with those obtained from a principal components regression analysis.

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Abstract The combined use of the nonlinear mapping method with correspondence factor analysis allowed to derive interesting structure-chemoreception relationships in Lepidoptera. A chemotaxonomy of insects based on their responses to pheromones was also proposed.

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A nonlinear mapping (NLM) analysis was performed on a set of 166 aromatic substituents described by six variables encoding hydrophobic (pi), steric (MR), and electronic effects (HBA, HBD, F, and R). NLM allowed to easily summarize the main information contained in the original data table. By means of collections of graphs, it was possible to relate the structure of the substituents to their pi, MR, HBA, HBD, F, and R values. The proposed approach provides a useful and easy tool for the selection of test series and for deriving structure-activity relationships.

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A nonlinear mapping (NLM) analysis was performed on a set of 103 aliphatic substituents described by five variables encoding hydrophobic (Fr), steric (MR), and electronic effects (HBA, HBD, and F). NLM allowed to easily summarize the main information contained in the original data table. By means of collections of graphs, it was possible to relate the structure of the aliphatic substituents to their Fr, MR, HBA, HBD, and F values. The proposed approach provides a useful and easy tool for the selection of test series and for deriving structure-activity relationships.

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Nonlinear mapping coupled to powerful graphical tools was used to compare the toxicological responses of 32 in vivo and in vitro test systems to the first 10 MEIC chemicals. The obtained results clearly underline the usefulness of our methodological approach for the comparison of the different endpoints and the selection of a battery of in vitro toxicity tests allowing to estimate the possible harmful effects of chemicals in vivo.

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The field half-lives of 110 pesticides were modelled using a backpropagation neural network (NN). The molecules were described by means of the frequency of 17 structural fragments. Before training the NN, different scaling transformations were assayed. Best results were obtained with correspondence factor analysis which also allowed a reduction of dimensionality. The training and testing sets of the NN analysis gave 95.5% and 84.6% of good classifications, respectively. Comparison with discriminant factor analysis showed that a backpropagation NN was more appropriate to model the field half-lives of pesticides.

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