Quality testing of distance-based molecular descriptors for benzenoid hydrocarbons (original) (raw)
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Molecular descriptors of benzenoid systems
Química Nova, 2016
Molecular descriptors are being widely used in QSAR/QSPR studies in chemistry and drug designing as well as modeling of compounds. Different topological descriptors have been formulated to investigate the physio chemical properties and chemical reactivity of compounds. In this article we gave exact relations for first and second Zagreb index, hyper Zagreb index, multiplicative Zagreb indices as well as first and second Zagreb polynomials for some benzenoid systems.
Estimating Some General Molecular Descriptors of Saturated Hydrocarbons
Molecular Informatics, 2019
Three general molecular descriptors, namely the general sum-connectivity index, general Platt index and ordinary generalized geometric-arithmetic index, are studied here. Best possible bounds for the aforementioned descriptors of arbitrary saturated hydrocarbons are derived. These bounds are expressed in terms of number of carbon atoms and number of carbon-carbon bonds of the considered hydrocarbons.
Degree- and irregularity-based molecular descriptors for benzenoid systems
The European Physical Journal Plus, 2021
The study of benzenoid systems has been steadily gaining momentum due to their extensive applications in many emerging fields including nanosciences. Topological descriptors provide a mathematical expression of the molecular structure of chemical compounds and their properties. They serve as efficient and cost-effective tools to theoretically predict the properties of compounds using quantitative structure-activity (QSAR) and structure-property relationship (QSPR) studies. This paper demonstrates the computation of degree-based and irregularity-based topological descriptors using edge-partition techniques for two benzenoid structures. This analysis of degree-based descriptors for these structures can lay the basis for further exploration into benzenoids and their properties.
2017
Despite the number of available chemicals growing exponentially, testing of their toxicological and environmental behavior is often a critical issue and alternative strategies are required. Additionally, there is the need to predict properties of not yet synthesized compounds to reduce the costs of synthesis, selecting only those that have the maximal potential to be active and nontoxic compounds. In order to evaluate chemical properties avoiding chemical synthesis and reducing expensive and time-demanding laboratory testing, it is necessary to build in silico models establishing a mathematical relationship between the structures of molecules and the considered properties (quantitative structure-activity relationships, QSARs). Molecular descriptors play a fundamental role in QSAR and other in silico models since they formally are the numerical representation of a molecular structure. Molecular descriptors can be classified using different criteria. Among them, there are two main categories, experimental and theoretical descriptors. The basis to understand and perform molecular descriptor calculation, the different theoretical descriptor categories together with their perspectives are described in this chapter.
Chemical Biology & Drug Design, 2009
We developed a new QSAR model, based on the optimal descriptors, calculated with simplified molecular input line entry system. These descriptors are correlated with mutagenic potential for a training set and correlated with this end-point for a test set. Statistical characteristics of the model are n = 28, r 2 = 0.902, q 2 = 0.892, s = 0.554, F = 240 (training set) and n = 20, r 2 = 0.853, q 2 = 0.823, s = 0.702, F = 105 (test set).
The Use of the Density Threshold Value as a Shape Descriptor on the Toxicity of Benzene Derivatives
The Use of the Density Threshold Value as a Shape Descriptor on the Toxicity of Benzene Derivatives, 2016
The Quantitative Structure Activity Relationship (QSAR) method, based on the three-dimensional (3D) shapes of formal molecular bodies and computed molecular descriptors, was used to calculate the octanol-water partition coefficient (logKow) of benzene derivatives to indicate their toxicity. The aim of this study is to use electron density threshold values as descriptors in predicting toxicology of benzene derivatives. Through Density Domain Analysis (DDA), a shape fragment database of benzene derivatives was constructed. Electron density threshold values were generated from molecular isodensity counter surfaces (MIDCO) of benzene molecules that were calculated at the ab initio HF/6-31G* level. Multiple linear regression analyses were performed, and two successful QSAR models were obtained. The analysis of variance (ANOVA) ratio for regression (F) and the significance of F (Fs) values was also used to evaluate the predictive power of the established QSAR models. The results indicate that the electron density threshold value, "a", gives a specific description of the 3D shape of electron density clouds. These models were further analyzed by three 3D shape features as one local and two global descriptors based on the electron density threshold "a" value. The global and local properties of benzene derivatives were found to exhibit similar toxicity behaviors.