Topological indices are used to research the chemical, biological, clinical, and medicinal properties of drugs in the field of chemical and medical sciences. The COVID-19 pandemic is widely regarded as the most life-threatening problem that modern medicine has ever faced. Scientists have tested usable antiviral agents and found that they have a significant impact on recovery from viral diseases such as COVID-19. Arbidol, Chloroquine, Hydroxychloroquine, Lopinavir, Remdesivir, Ritonavir, Thalidomide, and Theaflavin are some of the antiviral agents used in the treatment of COVID-19. In this paper we propose reduced reverse degree-based topological descriptors such as reduced reverse Zagreb indices, reduced reverse forgotten index, reduced reverse atom bond connectivity index, reverse geometric arithmetic index and reduced reverse general Randić index for the antiviral drugs of COVID-19. We also analyze the Quantitative Structure-Property Relationship (QSPR) of the proposed topological indices. Curvilinear regression models are obtained and analyzed for the physico-chemical properties of the COVID-19 drugs, in terms of the proposed indices. Our models and findings could aid in the development of new drugs for the treatment of COVID-19.

In chemistry and medical sciences, it is essential to study the chemical, biological, clinical, and therapeutic aspects of pharmaceuticals. To save time and money, mathematical chemistry focuses on topological indices used in quantitative structure-property relationship (QSPR) models to predict the properties of chemical structures. The COVID-19 pandemic is widely recognized as the greatest life-threatening crisis facing modern medicine. Scientists have tested various antiviral drugs available to treat COVID-19 disease, and some have found that they help get rid of this viral infection. Antiviral drugs such as Arbidol, chloroquine, hydroxychloroquine, lopinavir, remdesivir, ritonavir, thalidomide, and theaflavin are used to treat COVID-19. In this paper, reformulated leap Zagreb indices are introduced. Then, the reformulated leap Zagreb indices, leap eccentric connectivity indices, and reformulated Zagreb connectivity indices of these antiviral drugs are calculated. Curvilinear and multilinear regression models predicting the physicochemical properties of these antiviral drugs in terms of proposed indices are obtained and analyzed. The findings and models of this study will shed light on new drug discoveries for the treatment of COVID-19.

Topological indices refer to numerical values that are structure-invariant and are used to quantify the bonding topology of a molecular graph. The primary objective of studying topological indices is to acquire and modify chemical structure data, thereby establishing a mathematical correlation between structures and physico-chemical properties, bio-activities, and other experimental attributes. Several studies show a high intrinsic correlation between the molecular architectures of pharmaceuticals and their boiling and melting temperatures, as well as other chemical properties. Researchers can discover more about the physical characteristics, chemical stability, and bioactivities of these chemical molecular structures by using topological indices. To compensate for the lack of chemical experiments and to give a theoretical foundation for the production of pharmaceuticals and chemical materials, topological indices on the molecular structure of chemicals/drugs are studied. This study evaluates the chemical structures of medications used to treat hepatitis (A, B, C, D, E and G) based on reduced reverse degree-based topological indices. The success of drug design is influenced by factors such as solubility, metabolic stability, toxicity, permeability, and transporter effects, which are contingent upon the physical and chemical characteristics of the medication. In recent times, computational techniques have gained prominence in the field of hepatitis medication discovery and development. Machine learning is employed by certain systems to assess the effectiveness and adverse effects of medications. The primary focus of this article is to examine the chemical applicability of ten reduced reverse degree-based descriptors in predicting the ten physico-chemical properties for the 16 drugs employed in the treatment of hepatitis.

Prostate cancer is one of the most alarming diseases among men and leads to various illnesses. The main flaw in this disease is that there is not a single test that can find it. Prostate-specific antigens and prostate biopsies are used to identify this type of cancer. Many pharmaceutical industries do active research to identify non-steroidal drugs that can be used to treat prostate cancer effectively. QSPR analysis is one of the best practices that aid scientists in getting a deep insight into the physico-chemical properties of drugs without actually performing experiments in a wet lab, thereby cutting down on the cost of manufacturing such drugs. In this article, we perform a QSPR analysis of nineteen drugs primarily used to treat prostate cancer by using a set of ten degree-based indices and another set of ten novel neighbourhood-degree-based indices. In addition, we have done a comparative study of the findings obtained through these two different sets of indices over thirteen physico-chemical properties of prostate cancer drugs.