The entire world is struggling to control the spread of coronavirus (COVID‐19) as there are no proper drugs for treating the disease. Under clinical trials, some of the repurposed antiviral drugs have been applied to COVID‐19 patients and reported the efficacy of the drugs with the diverse inferences. Molecular topology has been developed in recent years as an influential approach for drug design and discovery in which molecules that are structurally related show similar pharmacological properties. It permits a purely mathematical description of the molecular structure so that in the development of identification of new drugs can be found through adequate topological indices. In this paper, we study the structural properties of the several antiviral drugs such as chloroquine, hydroxychloroquine, lopinavir, ritonavir, remdesivir, theaflavin, nafamostat, camostat, umifenovir and bevacizumab by considering the distance and bond measures of chemical compounds. Our quantitative values of the topological indices are extremely useful in the recent development of designing new drugs for COVID‐19.

Zeolites are aluminosilicates with extensive application both commercially and in materials science. Current applications include dehydrating natural gas and in humidity sensors. Synthesis of new frameworks is an important area of research in chemistry and materials science. The Zeolite LTA framework in particular is getting much attention in this area due to its potential for application. Topological indices are graph invariants which provide information on the structure of graphs and have proven very useful in quantitative structure activity relationships (QSAR) and quantitative structure property relationships (QSPR) at predicting important chemico-phyiscal aspects of chemical compounds. In this paper we compute nine of the most significant distance based topological indices of the Zeolite LTA framework and thirteen valency based molecular descriptors.

Topological descriptors are non-empirical numerical quantities that describe molecular structures. These descriptors are fundamental to the QSAR/QSPR techniques that lay the groundwork for theoretical chemists to study and synthesize chemical structures. Entropic network measures are a class of topological descriptors with applications that range from quantitative characterization of a molecular structure to the study of certain chemical properties of molecular graphs. This paper focusses on computing analytical expressions of degree-based entropy measures for three prominent classes of polycyclic aromatic hydrocarbons with a coronene molecule at their center. The intriguing properties of these classes of compounds and their widespread availability warrant a study of their nature and properties from a structural standpoint. The numerical values of the entropy measures computed in this paper also provide an insight into the relationship between the entropy measures and the molecular structure.