Modeling of non-additive mixture properties using the Online CHEmical database and Modeling environment (OCHEM).

The Online Chemical Modeling Environment (OCHEM, http://ochem.eu) is a web-based platform that provides tools for automation of typical steps necessary to create a predictive QSAR/QSPR model. The platform consists of two major subsystems: a database of experimental measurements and a modeling framework. So far, OCHEM has been limited to the processing of individual compounds. In this work, we extended OCHEM with a new ability to store and model properties of binary non-additive mixtures. The developed system is publicly accessible, meaning that any user on the Web can store new data for binary mixtures and develop models to predict their non-additive properties.The database already contains almost 10,000 data points for the density, bubble point, and azeotropic behavior of binary mixtures. For these data, we developed models for both qualitative (azeotrope/zeotrope) and quantitative endpoints (density and bubble points) using different learning methods and specially developed descriptors for mixtures. The prediction performance of the models was similar to or more accurate than results reported in previous studies. Thus, we have developed and made publicly available a powerful system for modeling mixtures of chemical compounds on the Web

New QSPR Models to Predict the Flammability of Binary Liquid Mixtures

New Quantitative Structure‐Property Relationships (QSPR) are presented to predict the flash point of binary liquid mixtures, based on more than 600 experimental flash points for 60 binary mixtures. Two models are proposed based on a GA‐MLR approach that uses a genetic algorithm (GA) variable selection in multilinear regressions (MLR). In these models, mixtures were characterized by a series of mixture descriptors calculated from various mixture formula combining the molecular descriptors of the single compounds constituting the mixtures and their respective molar fractions in the mixture. The best model demonstrated good predictive capabilities with a mean absolute error of only 7.3 °C estimated for an external validation set. Moreover, this model is focused on mixture descriptors applicable to more complex mixtures, i. e. constituted of more than 2 components, and already demonstrated interesting predictions for a series of ternary mixtures