The exploration of non-cancer medications with potential anti-cancer activity offers a promising avenue for drug repurposing, accelerating the development of new oncological therapies. This study employs Quantitative Structure-Property Relationship (QSPR) modeling to identify and predict the anti-cancer efficacy of various non-cancer drugs, utilizing topological indices as key descriptors. Topological indices, which capture the molecular structure’s geometric and topological characteristics, provide critical insights into the pharmacological interactions relevant to anti-cancer activity. By analyzing a comprehensive dataset of non-cancer medications, this research establishes robust QSPR models that correlate topological indices with anti-cancer activity. The models demonstrate significant predictive power, highlighting several non-cancer drugs with potential anti-cancer properties. Further, we will use linear, quadratic and logarithmic regression to understand the structures of anti-cancer drugs and strengthen our ability to manipulate the molecular structures. The findings underscore the utility of topological indices in drug repurposing strategies and pave the way for further experimental validation and clinical trials. This integrative approach enhances our understanding of drug action mechanisms and offers a cost-effective strategy for expanding the repertoire of anti-cancer agents.

In molecular science, one of the key problem is to model a chemical compound and predict its chemical characteristic. Numerous hypothetical methods have been created by various analysts in this respect and one of them is concerned with the topological indices. A topological index is a numerical value attached with the structural graph of a molecule and is expected to predict certain chemical/physical properties of molecule [6]. Among the classes of topological indices, degree based topological indices play a vital role in chemical graph theory. In the current study, we compute the neighborhood second Zagreb index and the first extended first–order connectivity index of Planar zigzag nanotube ${\mathit{PTUZC}}_6$, polyhex zigzag nanotube $H_2={\mathit{TUZC}}_6$, polyhex zigzag nanotori ${\mathit{TTUZC}}_6,C_4{C}_8(S)[m,n]$ nanosheets, $C_4{C}_8(S)[m,n]$ nanotubes, $C_4{C}_8(S)[m,n]$ nanotori and armchair polyhex nanotubes ${\mathit{TUAC}}_6[p,q]$.

Cyclooctane is classified as a cycloalkane, characterized by the chemical formula C₈H₁₆. It consists of a closed ring structure composed of eight carbon atoms and sixteen hydrogen atoms. A cyclooctane chain typically refers to a series of cyclooctane molecules linked together. Cyclooctane and its derivatives find various applications in chemistry, materials science, and industry. Topological indices are numerical values associated with the molecular graph of a chemical compound, predicting certain physical or chemical properties. In this study, we calculated the expected values of degree-based and neighborhood degree-based topological descriptors for random cyclooctane chains. A comparison of these topological indices’ expected values is presented at the end.

Cyclodecanes, belonging to the class of aliphatic cycloalkanes, comprise non-aromatic carbon-atom rings. Within the 10-membered ring, cyclodecanes exhibit various isomers, characterized by different spatial arrangements of carbon atoms. Serving as model compounds in organic studies, cyclodecanes offer insights into the properties and reactivity of cyclic hydrocarbons. The chemistry of cyclodecane serves as a valuable foundation for understanding the behavior of more complex hydrocarbons. In this paper, we explore the potential probabilities of random cyclodecane chains D t ρ of length t ≥ 3. We derive exact expressions for the expected values of the modified second Zagreb index, inverse index, and second NDe index. A theoretical comparative analysis of these descriptors is provided, accompanied by graphical and numerical representations of the obtained results.