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

Abstract

Each year, fragrance companies develop and patent two or three new molecules called captives. 1 These molecules are created not only to entice consumers with unique notes for perfumery but also to decrease cost, allergic reactions, feedstock shortages, or non-biodegradability/non-renewability of previous raw materials.To launch two to three captives per year on the market, screening of hundreds or thousands of molecules is necessary. As such, fragrance design requires considerable experimental work of synthesis and characterization. Hence, predicting in silico the physicochemical properties of virtual candidates can significantly contribute to a faster development of new captives with targeted features.According to the REACH regulation, 2 17 properties must be determined to bring a new molecule to the market (Table S1). However, measuring experimentally all these properties is not always realistic in terms of time, cost, feasibility at R&D level, and safety (notably for hazardous compounds). Therefore, this implementation has motivated the development of alternative approaches to experimental testing with fast and reliable methods. 2 As a consequence, thanks to improvements in computer hardware and software in the past decades, numerous tools to predict property values for large molecule sets have emerged.Perfumery is typically a field of application in which prediction of physicochemical properties can be very useful. Indeed, vapor pressure (VP) and boiling point (BP) as well as Henry's law constant (HLC), which represents the mass transfer of molecules from liquid to air, are essential data to describe and assess the volatility of scent molecules. [3][4][5][6][7] Likewise, water solubility (WS) and octanol-water partition coefficient (log P) can bring valuable insights for the elaboration of aqueous formulations. 4 Among the available predictive approaches, mainly five stand out for estimating chemical properties with their pros and cons. [5][6][7][8][9][10][11] The group contribution model (GC) or additive group method is an empirical method based on the division of molecule in blocks (functional groups). [12][13][14][15] In most cases, the property is computed as a sum

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

“…In the window of values presented in Figure 4 , only half of the tested solvents are visible, and the rest are cut off. Although the COSMO-RS method is undoubtedly a powerful and universal tool for predicting various physicochemical properties, including solubility [ 52 , 53 , 54 ], some limitations of this approach were observed in the case of BSA. Sorting the ensemble A predictions further emphasizes that screening solvents using only COSMO-RS estimation may be highly misleading.…”

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

“…COSMO-RS is a first-principles theoretical model based on a combination of quantum chemistry and statistical thermodynamics that serves to estimate, without any prior experience, a large number of chemical properties. , Due to the presence of polar covalent bonds, molecules carry a surface charge density σ on its so-called “σ-surface”, which corresponds to the slightly inflated van der Waals surface. The “σ-profile” p X (σ) of a molecule X is the curve obtained by smoothing the histogram of surface portions grouped by charge density in the interval [σ – d σ/2, σ + d σ/2] .…”

In-depth study of binary ethanol–triacetin mixtures in relation to their excellent solubilization power

“…By calculating the chemical potential m S (i) of a compound i according to eqn (1), several macroscopic thermodynamic properties become accessible, such as vapor pressure, solubilities, and partition coefficients. [20][21][22][23][24][25][26][27] m ðiÞ…”