The quantitative structure-property relationship (QSPR) method is used to develop the correlation between structures of refrigerants (198 compounds) and their critical temperature. Molecular descriptors calculated from structure alone were used to represent molecular structures. A subset of the calculated descriptors selected using a genetic algorithm (GA) was used in the QSPR model development...

The results of the quantitative structure-property relationship (QSPR) analysis of 45 different solvent scales and 350 solvents using the CODESSA program are presented. The QSPR models for each of the scales are constructed using only theoretical descriptors. The high quality of the models (32 of the 45 give R2 > 0.90, only two have R2 < 0.82) enables direct calculation of predicted values for ...

In the field of ionic liquids (ILs), theory-driven modeling approaches aimed at the best fit for all available data by using a unique, and often nonlinear, model have been widely adopted to develop quantitative structure-property relationship (QSPR) models. In this context, we propose chemoinformatic and chemometric data-driven procedures that lead to QSPR soft models with local validity that a...

High accuracy is paramount when predicting biochemical characteristics using Quantitative Structural-Property Relationships (QSPRs). Although existing graph-theoretic kernel methods combined with machine learning techniques are efficient for QSPR model construction, they cannot distinguish topologically identical chiral compounds which often exhibit different biological characteristics. In this...

The vapor pressure is a key property in determining the distribution and fate of environmentally relevant compounds, but experimental determinations are only available for a limited number of the chemicals in current commercial use. Despite experimental efforts there is a need for estimation methods. The liquid or subcooled liquid vapor pressures at 298.15 K were collected from the literature f...

Quantitative structure–property relationship (QSPR) modeling is performed to investigate the role of cycloalkyl-fused rings on catalytic performance 46 aryliminopyridyl nickel precatalysts. The activities for complexes in ethylene polymerization are well-predicted by obtained 2D-QSPR model, exploring main contribution from charge distribution negatively charged atoms. Comparatively, 3D-QSPR mod...

Nowadays, a large amount of experimental and predicted data about the 3D structure of organic molecules and biomolecules is available. Advanced computational methods and high performance computers allow us to obtain large sets of descriptors that can be used to estimate physicochemical properties. It is often of interest to study the correlations between descriptors and properties using multili...

Hierarchical BEA zeolite was prepared through desilication or followed by acid treatment. The catalytic performance of samples evaluated using Friedel-Crafts acylations with two substrates different molecular sizes, furan (5.7 Å) and benzofuran (6.9 Å), in the presence acetic anhydride as acylating agent. application simplified Langmuir-Hinshelwood kinetic model showed that size substrate leads...

We demonstrate applications of quantitative structure–property relationship (QSPR) modeling to supplement first-principles computations in materials design. We have here focused on the design of polymers with specific electronic properties. We first show that common materials properties such as the glass transition temperature (Tg) can be effectively modeled by QSPR to generate highly predictiv...

Our aim is to develop an effective computational procedure for predicting the aqueous acid equilibrium constants of protonated benzimidazoles at 298.15 K. The experimental determination of these values, apart from been laborious, is a challenge because of the low water solubility of these compounds. Using a variety of descriptors, quantitative structure-property relationships (QSPR) are explore...