Comparative Analysis of Reverse Degree and Entropy Topological Indices for Drug Molecules in Blood Cancer Treatment through QSPR Regression Models

The topological indices provide quantitative structural characteristics of drug molecules that can be utilized to predict or establish correlations with the biological activity, physicochemical properties, and toxicity of the molecules. Such studies play a crucial role in the initial stages of drug development by aiding in the identification and optimization of potential drug candidates and providing cost-effective techniques for experimental studies. Cancer, a multifaceted disease, can originate in any part of the body due to various factors, including genetic mutations, environmental toxins, and lifestyle choices. Blood cancer, encompassing malignancies affecting the blood, bone marrow, and lymphatic systems, is the focus of this research paper. The current study investigates a comprehensive set of drugs employed in blood cancer treatment, including clofarabine, mercaptopurine, olutasidenib, glasdegib, gliteritinib, zanubrutinib, chlorambucil, ibrutinib, bosutinib, hydroxyurea, cyclophosphamide, doxorubicin, daunorubicin, ivosidenib, prednisone, busulfan, omacetaxine mepesuccinate, and asciminib. By conducting a thorough analysis of these drugs, we acquire valuable insights into their molecular properties, which are crucial for predicting their behavior and efficacy in blood cancer treatment. We have devised QSPR models by leveraging the reverse degree and entropy topological indices. Our proposed QSPR models are compared with existing degree-based models, emphasizing the superior effectiveness of our approach.