Empowerments of blood cancer therapeutics via molecular descriptors

A disease caused by cellular alterations that is unrestrained cell growth and division is cancer. Many anticancer medications, including those used to treat blood, breast, and skin cancer, may have their physical, chemical, and biological features predicted. This paper presents novel distance-based topological indices (TIs) computed using the suggested KP-polynomial with blood cancer drugs. The objective of the QSPR investigation is to determine the mathematical correlation between the analyzed properties (such as Molar Volume, Refractive Index, etc.) and different descriptors associated with the molecular structure of the medications. A polynomial regression model is employed to assess the predictive capability of TIs. The results are represented using a correlation coefficient to establish the connection between the predicted and observed values of blood cancer drugs. This theoretical method could potentially enable chemists and health care professionals to anticipate the characteristics of blood cancer drugs without the need for actual experimental tests. This leads towards new opportunities to paved the way for drug discovery and the formation of efficient multicriteria decision making technique TOPSIS for ranking of said disease treatment drugs and physicochemical characteristics.