Predictive QSPR Modelling for the Second Virial Coefficient of the Pure Organic Compounds

Abstract

In this article we developed a system of the predictive models for the second virial coefficients of the pure compounds. Second virial coefficient is the property derived from the virial equation of state, and is of particular interest as it describes pair intermolecular interactions. The two-layer QSPR models were developed, which exploited the well-known physical equations and allowed us to include this information into traditional QSPR methodology. This shows some new perspectives for work with temperature-dependent properties. It was shown that 2D descriptors can be successfully used for modeling of complex thermodynamic properties like virial coefficients.

Simplex representation of molecular structure as universal QSAR/QSPR tool

“…One issue arises with the inconsistency in the temperature values and range of temperatures seen in the virial coefficients data. To solve this problem, like in [ 77 ], we used physical based methodologies that derive the following two simple but rigorous equations from the Van-der-Waals equation of state for real gases: …”

“…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.).…”

Predicting second virial coefficients of organic and inorganic compounds using Gaussian process regression

“…As the authors also suggest, this method should only be used when "high accuracy is required", 20 due to its complexity. Furthermore, the prediction of second virial coefficients has been addressed before by Mokshyna et al 21 , by assuming a functional form of the dependency of B 2 on temperature. The methodology involved modelling molecular structures using the Simplex Representation of Molecular Structure (SiRMS) 22 , based on which a Random Forest model was trained to predict second virial coefficients.…”

QSPR modeling of critical properties of organic binary mixtures

“…Furthermore, previously obtained QSPR models of organic compounds based on 2D representation of molecular structure can be employed under thermodynamic conditions analogous to critical point of a pure substance. As we showed in [2][3][4], the set of descriptors for pure substances includes both parameters characterizing intermolecular interactions and steric factors ensuing, in particular, from the van der Waals model.…”