‘Quasi‐Mixture’ Descriptors for QSPR Analysis of Molecular Macroscopic Properties. The Critical Properties of Organic Compounds

Abstract

Rational approach towards the QSAR/QSPR modeling requires the descriptors to be computationally efficient, yet physically and chemically meaningful. On the basis of existing simplex representation of molecular structure (SiRMS) the novel ‘quasi‐mixture’ descriptors were developed in order to accomplish the goal of characterization molecules on 2D level (i.e. without explicit generation of 3D structure and exhaustive conformational search) with account for potential intermolecular interactions. The critical properties of organic compounds were chosen as target properties for the estimation of descriptors’ efficacy because of their well‐known physical nature, rigorously estimated experimental errors and large quantity of experimental data. Among described properties are critical temperature, pressure and volume. Obtained models have high statistical characteristics, therefore showing the efficacy of suggested ‘quasi‐mixture’ approach. Moreover, ‘quasi‐mixture’ approach, as a branch of the SiRMS, allows to interpret results in terms of simple basic molecular properties. The obtained picture of influences corresponds to the accepted theoretical views.

QSPR modeling of critical properties of organic binary mixtures

“…An insignificant contribution of descriptors weighted by hydrogen bond may be accounted for by the small number of compounds capable of forming hydrogen bonds, 11 to 22% for different properties. Comparison with the variable importance in models for pure substances [2,4] showed similar trends: the contributions of descriptors related to electrostatic and van der Waals interactions predominate for both pure substances and their 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.…”

Simplex representation of molecular structure as universal QSAR/QSPR tool

“…SiRMS-based 2D-QSPR models attempting to predict the critical temperatures (Tc), volumes (Vc), and pressures (Pc) and Pitzer’s acentric factors (ω) of organic compounds used 407, 382, 309, and 331 compounds, respectively, all from NIST WebBook [ 75 , 76 , 101 ]. Structurally diverse organic compounds were used and this resulted in high statistics for the QSPR model after 5-fold external cross validation ( R 2 = 0.97–0.99, R 5f 2 = 0.86–0.95, predicted error T c and V c <3%, predicted error Pc and ω 3–10%).…”

“…By combining the SiRMS methodologies for single compounds and mixtures, the “quasi-mixture” approach designates a pure compound as a mixture of two molecules and hence presents new unique mixture simplexes [ 76 ]. The QSPR models of the “quasi-mixture” simplexes display higher performance statistics and statistically significant differences in RMSE (Fig.…”

“…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].…”