QSPR-Modeling for the Second Virial Cross-Coefficients of Binary Organic Mixtures

Abstract

The second virial cross-coefficient is an important characteristic of the pair intermolecular interactions that describes solely the heterogeneous interactions. In the current study, the authors made the first attempt to develop rigorous QSPR models for analysis and prediction of the second virial cross-coefficient. Novel descriptors to describe pair intermolecular interactions were implemented. Statistical characteristics of the obtained models showed high performance. Prediction errors are comparable to the errors of data. Theoretically predicted values of the second virial cross-coefficient may be used to derive PVT-properties of mixtures at the different temperatures as well as to calculate intermolecular pair potential.

Interpretation of Quantitative Structure–Activity Relationship Models: Past, Present, and Future

“…The authors called this “similarity maps”. It was implemented using Python open-source toolkits RDKit, scikit-learn, and matplotlib. The same authors demonstrated its applicability on a set of dopamine D3 ligands using RF and Naïve Bayes (NB) models built on different fingerprints .…”

Simplex representation of molecular structure as universal QSAR/QSPR tool

“…This intrinsic property opens up the opportunity to predict PVT for multicomponent mixtures as well. To our knowledge, [ 78 ] is the first attempt at a QSPR model for this coefficient. Dymond et al [ 102 ] compilation was the source of the data for the 126 mixtures and 1211 values (each mixture selected had at least 4 values) of B 12 at different temperatures ranging from 200-600 K. The test set comprised of compounds with less than 4 data values for a total of 102 mixtures and 188 data points at different temperatures.…”

“…Lipophilicity and water solubility [44], [66][67][68][69][70][71][72] Luminescent properties [73] Thermodynamic properties [74][75][76][77][78][79][80] Properties of ionic compounds and materials [81], [82] Properties of nanosystems [63], [83], [84] The concept of chiral simplexes helped us to understand these problems. As a mathematical object, a simplex is a ndimensional polyhedron, which is a convex shell (n+1) of points (vertexes of simplex) that do not lie in the (n-1)-dimensional plane [85].…”

“…A series of publications where SiRMS descriptors were used to build QSPR models were devoted to the thermodynamic properties of substances: the boiling temperatures, critical parameters, second virial coefficients, and adsorption parameters [74][75][76][77][78][79][80]. What distinguished these publications from other similar works was the demonstration of applications to mixtures of compounds (SiRMS for mixtures is described in the "Simplex descriptors for solving various QSAR/QSPR tasks" section.).…”

“…This intrinsic property opens up the opportunity to predict PVT for multicomponent mixtures as well. To our knowledge, [78] is the first attempt at a QSPR model for this coefficient. Dymond et al [102] compilation was the source of the data for the 126 Contrary to other states, the QSPR models used 2D descriptors but do not require additional experimental data.…”