Molecular insights into anticancer drugs through predictive mathematical modelling: A QSPR perspective

The purpose of this paper is to discuss the usage of topological indices and Entropy for Quantitative structure–property relationship (QSPR) to anticipate the physical and biological aspects of innovative drugs used in the treatment of anticancer disease. By using topological indices that represent the structural features of the molecular graphs, a series of incorporation entropic indices can be developed for the quantitative characterisation of molecular complexity and variation. Degree-based topological indices and Entropy were generated using edge partitioning to assess the drugs such as Dacomitinib, Afatinib, Neratinib, Nazartinib, Avitinib and Osimertinib by using python algorithm. Then, using linear and Logarithmic regression, a QSPR model is developed to predict characteristics such as Polarisability, Molar Refractivity, Surface Tension, Molar Volume, Complexity and Molecular Weight. Therefore, by applying these models, the study will extend the capabilities of QSPR analysis and provide a solid ground for assessing the eligibility of the potential drug candidates. The findings show that topological indices and Entropy have the potential to be used as a tool for drugs discovery and design in the field of anticancer disease treatment.