The recent REACH Policy of the European Union has led to scientists and regulators to focus their attention on establishing general validation principles for QSAR models in the context of chemical regulation (previously known as the Setubal, nowadays, the OECD principles). This paper gives a brief analysis of some principles: unambiguous algorithm, Applicability Domain (AD), and statistical validation. Some concerns related to QSAR algorithm reproducibility and an example of a fast check of the applicability domain for MLR models are presented. Common myths and misconceptions related to popular techniques for verifying internal predictivity, particularly for MLR models (for instance crossvalidation, bootstrap), are commented on and compared with commonly used statistical techniques for external validation. The differences in the two validating approaches are highlighted, and evidence is presented that only models that have been validated externally, after their internal validation, can be considered reliable and applicable for both external prediction and regulatory purposes. (“Validation is one of those words...that is constantly used and seldom defined” as stated by A. R. Feinstein in the book Multivariate Analysis: An Introduction, Yale University Press, New Haven, 1996).

Principles of QSAR models validation: internal and external

http://www.sciencemadness.org/talk/files.php?pid=302583&aid=26587

Azole-based energetic salts.

Quantitative Correlation of Physical and Chemical Properties with Chemical Structure: Utility for Prediction

The control of luminous radiation has extremely important implications for modern and future technologies as well as in medicine. In this Review, we detail chemical structures and their relevant photophysical features for various groups of materials, including organic dyes such as metalloporphyrins and metallophthalocyanines (and derivatives), other common organic materials, mixed metal complexes and clusters, fullerenes, dendrimeric nanocomposites, polymeric materials (organic and/or inorganic), inorganic semiconductors, and other nanoscopic materials, utilized or potentially useful for the realization of devices able to filter in a smart way an external radiation. The concept of smart is referred to the characteristic of those materials that are capable to filter the radiation in a dynamic way without the need of an ancillary system for the activation of the required transmission change. In particular, this Review gives emphasis to the nonlinear optical properties of photoactive materials for the functi...

Nonlinear Optical Materials for the Smart Filtering of Optical Radiation.

Quantitative Structure Property Relationship Modeling of Diverse Materials Properties Tu Le, V. Chandana Epa, Frank R. Burden, and David A. Winkler* CSIRO Materials Science and Engineering, Bag 10, Clayton South MDC 3169, Australia CSIRO Materials Science and Engineering, 343 Royal Parade, Parkville 3052, Australia Monash Institute of Pharmaceutical Sciences, 381 Royal Parade, Parkville 3052, Australia

Quantitative Structure–Property Relationship Modeling of Diverse Materials Properties

In this study, general and class-specific QSPR models for soil sorption, logKOC, of 344 organic pollutants (0 < logKOC < 4.94) were developed using a large variety of theoretical molecular descriptors based only on molecular structure. Two general models were obtained. The first model was derived for a structurally representative set of 68 chemicals (R2=0.76, s=0.44), whereas the second involved a total of 344 compounds (R2=0.76, s=0.41). The first was validated using the data for the remaining 276 pollutants (R2=0.70, s=0.45). An additional validation of both models was performed using an independent set of 48 pollutants. Both models predict the logKOC at the level of experimental precision, while the theoretical molecular descriptors appearing in the QSPR models give further insight into the mechanisms of soil sorption. The analysis of the distribution of the residuals of the logKOC values calculated by both general models indicated the need and possible advantages of modeling soil sorption for smaller ...

QSPR treatment of the soil sorption coefficients of organic pollutants.

An approach for predicting acute aquatic toxicity, in the form of a quantitative structure-activity-activity relationship (QSAAR), is described. This study assessed relative toxic effects to a fish, Pimephales promelas, and a ciliate, Tetrahymena pyriformis, and attempted to form relationships between them. A good agreement between toxic potencies (R 2 = 0.754) was found for a chemically diverse dataset of 364 compounds, when using toxicity to the ciliate as a surrogate to that for fish. This relationship was extended by adding three theoretical structural descriptors of the molecules. The inclusion of these descriptors improved the relationship further (R 2 = 0.824). The structural features that were found to improve the extrapolation between the toxicity to the two different species were related to the electron distribution of the carbon skeleton of the toxicant, its hydrogen-bonding ability, and its relative nitrogen content. Such a QSAAR approach provides a potential tool for predicting the toxicities of chemicals for environmental risk assessment and thus for reducing animal tests.

Comparative quantitative structure-activity-activity relationships for toxicity to Tetrahymena pyriformis and Pimephales promelas.

During the last years, considerable effort has been devoted to model the toxicity of chemicals to Tetrahymena pyriformis for medium and large sized data sets using various artificial neural network (ANN) techniques. Motivation behind this has been to model highly complex relationships with nonlinear character making it possible to describe wide structural diversity within one model. The current work compares the performance of two heuristic methods in developing quantitative structure-activity relationship (QSAR) models: the best multilinear regression (BMLR) approach and the heuristic back-propagation neural networks (hBNN). The modeling is based on a diverse data set of 1371 organic chemicals with toxicity data (log(1/IGC50)) collected from the literature. The toxicity values correspond to the static 40-h Tetrahymena pyriformis population growth impairment assay. The comparison of the two methods showed that the BMLR approach produces acceptable QSAR models (R2 = 0.726), whereas the hBNN method produced a statistically more significant model (R2 = 0.826) for the given endpoint. The hBNN method was able to relate different descriptors to the toxicity than the BMLR method. Both models were validated with an external prediction set. The descriptors in the models were analyzed and discussed.

Iiris Kahn

Papers

Quantitative Correlation of Physical and Chemical Properties with Chemical Structure: Utility for Prediction

Quantitative Correlation of Physical and Chemical Properties with Chemical Structure: Utility for Prediction

QSPR treatment of the soil sorption coefficients of organic pollutants.

Comparative quantitative structure-activity-activity relationships for toxicity to Tetrahymena pyriformis and Pimephales promelas.

Modeling the toxicity of chemicals to Tetrahymena pyriformis using heuristic multilinear regression and heuristic back-propagation neural networks.