Molecular structural modeling and physical characteristics of anti-breast cancer drugs via some novel topological descriptors and regression models.

Research is continuously being pursued to treat cancer patients and prevent the disease by developing new medicines. However, experimental drug design and development is a costly, time-consuming, and challenging process. Alternatively, computational and mathematical techniques play an important role in optimally achieving this goal. Among these mathematical techniques, topological indices (TIs) have many applications in the drugs used for the treatment of breast cancer. TIs can be utilized to forecast the effectiveness of drugs by providing molecular structure information and related properties of the drugs. In addition, these can assist in the design and discovery of new drugs by providing insights into the structure-property/structure-activity relationships. In this article, a Quantitative Structure Property Relationship (QSPR) analysis is carried out using some novel degree-based molecular descriptors and regression models to predict various properties (such as boiling point, melting point, enthalpy, flashpoint, molar refraction, molar volume, and polarizability) of 14 drugs used for the breast cancer treatment. The molecular structures of these drugs are topologically modeled through vertex and edge partitioning techniques of graph theory, and then linear regression models are developed to correlate the computed values with the experimental properties of the drugs to investigate the performance of TIs in predicting these properties. The results confirmed the potential of the considered topological indices as a tool for drug discovery and design in the field of breast cancer treatment.

On the construction of some bioconjugate networks and their structural modeling via irregularity topological indices.

Bioconjugate networks refer to networks that are formed by connecting different molecules or particles (such as proteins, enzymes, or nanoparticles) through covalent or non-covalent interactions. These networks are often used in various biological and biomedical applications, such as drug delivery, biosensors, and tissue engineering. The specific properties and behavior of these networks depend on the types of molecules used and the nature of their interactions, which can be studied using various computational and experimental techniques. Farnesyl and geranyl groups are types of isoprenoid chains that are commonly found in various biological molecules such as proteins, lipids, and pigments. The addition of these groups to penicillin molecules may alter their physical and chemical properties, such as solubility, stability, and bioavailability. To gain a better understanding of the structure-property relationships of these antibiotics, this study computes various irregularity indices such as the Albertson index, irregularity index, total irregularity index, Randic irregularity index, and other degree-based indices for two types of sensitive bonds of bioconjugate networks. Numerical results and graphical representations are used to illustrate these findings. The obtained results provide valuable insights into the structure-property relationships of penicillins, which will aid in a better understanding of their behavior and developing more effective antibiotics.