QSPR Models for Predicting of the Melting Points and Refractive Indices for Inorganic Substances

Abstract

The QSPR methodology is very promising for the creation of new materials, including materials based on inorganic compounds. However, the majority of QSPR descriptor systems are applicable only for organic molecules. In this work the 1D - QSPR descriptor system is proposed for analysis of the properties of various inorganic compounds. These descriptors are easily accessible, as they describe the most fundamental atom properties. The combinatorial schemes for computing these descriptors provide for their wide variety. The effectiveness of the proposed approach has been demonstrated to study the refractive indices and melting points of various inorganic compounds - components of potential optical film-forming materials. The developed QSPR models are suitable for the evaluative virtual screening of inorganic compounds; the mean relative error of prediction is 6 - 15%. The interpretation of the developed models reflects the nature of interatomic interactions in compounds with ionic structure.

Хемоінформатика Як Ефективний Інструмент Прогнозування І Конструювання Нових Речовин З Комплексом Необхідних Властивостей

“…Показано, що такої інформації в QSPR-моделях цілком достатньо для прогнозування температур плавлення та показників заломлення різноманітних неорганічних сполук (оксидів, галогенідів, халькогенідів, карбонатів, сульфатів тощо). Середня відносна помилка прогнозу в обох випадках становить 10 % [7].…”

Simplex representation of molecular structure as universal QSAR/QSPR tool

“…QSPR models were developed to predict the melting points (MP) and refractive indices (RI) of various inorganic compounds in [ 81 ]. These data points are essential for the development of new optical materials.…”

“…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].…”