COSMO‐RS as an effective tool for predicting the physicochemical properties of fragrance raw materials

QSPR predicting the vapor pressure of pesticides into high/low volatility classes

In this work, the vapor pressure of pesticides is employed as an indicator of their volatility potential. Quantitative Structure-Property Relationship models are established to predict the classification of compounds according to their volatility, into the high and low binary classes separated by the 1-mPa limit. A large dataset of 1005 structurally diverse pesticides with known experimental vapor pressure data at 20 °C is compiled from the publicly available Pesticide Properties DataBase (PPDB) and used for model development. The freely available PaDEL-Descriptor and ISIDA/Fragmentor molecular descriptor programs provide a large number of 19,947 non-conformational molecular descriptors that are analyzed through multivariable linear regressions and the Replacement Method technique. Through the selection of appropriate molecular descriptors of the substructure fragment type and the use of different standard classification metrics of model's quality, the classification of the structure-property relationship achieves acceptable results for discerning between the high and low volatility classes. Finally, an application of the obtained QSPR model is performed to predict the classes for 504 pesticides not having experimentally measured vapor pressures.

Finding the Right Solvent: A Novel Screening Protocol for Identifying Environmentally Friendly and Cost-Effective Options for Benzenesulfonamide

This study investigated the solubility of benzenesulfonamide (BSA) as a model compound using experimental and computational methods. New experimental solubility data were collected in the solvents DMSO, DMF, 4FM, and their binary mixtures with water. The predictive model was constructed based on the best-performing regression models trained on available experimental data, and their hyperparameters were optimized using a newly developed Python code. To evaluate the models, a novel scoring function was formulated, considering not only the accuracy but also the bias-variance tradeoff through a learning curve analysis. An ensemble approach was adopted by selecting the top-performing regression models for test and validation subsets. The obtained model accurately back-calculated the experimental data and was used to predict the solubility of BSA in 2067 potential solvents. The analysis of the entire solvent space focused on the identification of solvents with high solubility, a low environmental impact, and affordability, leading to a refined list of potential candidates that meet all three requirements. The proposed procedure has general applicability and can significantly improve the quality and speed of experimental solvent screening.

Quantum chemical calculation of the vapor pressure of volatile and semi volatile organic compounds

The vapor pressure is a specific and temperature-dependent parameter that describes the volatility of a substance and thus its driving force for evaporation or sublimation into the gas phase. Depending on the magnitude of the vapor pressure, there are different methods for experimental determination. However, these are usually associated with a corresponding amount of effort and become less accurate as the vapor pressure decreases. For purposes of vapor pressure prediction, algorithms were developed that are usually based on quantitative structure-activity relationships (QSAR). The quantum mechanical (QM) approach followed here applies an alternative, much less empirical strategy, where the change in Gibbs free energy for the transition from the condensed to the gas phase is obtained from conformer ensembles computed for each phase separately. The results of this automatic, so-called CRENSO workflow are compared with experimentally determined vapor pressures for a large set of environmentally relevant compounds. In addition, comparisons are made with the single structure-based COSMO-RS QM approach, linear-free-energy relationships (LFER) as well as results from the SPARC program. We show that our CRENSO workflow is superior to conventional prediction models and provides reliable vapor pressures for liquids and sub-cooled liquids over a wide pressure range.

Fast Prediction of the Equivalent Alkane Carbon Number Using Graph Machines and Neural Networks

The hydrophobicity of oils is a key parameter to design surfactant/oil/water (SOW) macro-, micro-, or nano-dispersed systems with the desired features. This essential physicochemical characteristic is quantitatively expressed by the equivalent alkane carbon number (EACN) whose experimental determination is tedious since it requires knowledge of the phase behavior of the SOW systems at different temperatures and for different surfactant concentrations. In this work, two mathematical models are proposed for the rapid prediction of the EACN of oils. They have been designed using artificial intelligence (machine-learning) methods, namely, neural networks (NN) and graph machines (GM). While the GM model is implemented from the SMILES codes of a 111-molecule training set of known EACN values, the NN model is fed with some σ-moment descriptors computed with the COSMOtherm software for the 111-molecule set. In a preliminary step, the leave-one-out algorithm is used to select, given the available data, the appropriate complexity of the two models. A comparison of the EACNs of liquids of a fresh set of 10 complex cosmetic and perfumery molecules shows that the two approaches provide comparable results in terms of accuracy and reliability. Finally, the NN and GM models are applied to nine series of homologous compounds, for which the GM model results are in better agreement with the experimental EACN trends than the NN model predictions. The results obtained by the GMs and by the NN based on σ-moments can be duplicated with the demonstration tool available for download as detailed in the Supporting Information.

Bio-based alternatives to volatile silicones: Relationships between chemical structure, physicochemical properties and functional performances

Emollient oils are ubiquitous ingredients of personal care products, especially skin care and hair care formulations. They offer excellent spreading properties and give end-use products a soft, pleasant and non-sticky after-feel. Emollients belong to various petro- or bio-based chemical families among which silicone oils, hydrocarbons and esters are the most prominent. Silicones have exceptional physicochemical and sensory properties but their high chemical stability results in very low biodegradability and a high bioaccumulation potential. Nowadays, consumers are increasingly responsive to environmental issues and demand more environmentally friendly products. This awareness strongly encourages cosmetics industries to develop bio-based alternatives to silicone oils. Finding effective silicon-free emollients requires understanding the molecular origin of emollience. This review details the relationships between the molecular structures of emollients and their physicochemical properties as well as the resulting functional performances in order to facilitate the design of alternative oils with suitable physicochemical and sensory properties. The molecular profile of an ideal emollient in terms of chemical function (alkane, ether, ester, carbonate, alcohol), optimal number of carbons and branching is established to obtain an odourless oil with good spreading on the skin. Since none of the carbon-based emollients alone can imitate the non-sticky and dry feel of silicone oils, it is judicious to blend alkanes and esters to significantly improve both the sensory properties and the solubilizing properties of the synergistic mixture towards polar ingredients (sun filters, antioxidants, fragrances). Finally, it is shown how modelling tools (QSPR, COSMO-RS and neural networks) can predict in silico the key properties of hundreds of virtual candidate molecules in order to synthesize only the most promising whose predicted properties are close to the specifications.