Multi-criteria decision making: Revealing Afinitor as the leading brain tumor drug Using CRITIC, CoCoSo, and MABAC methods combined with QSPR analysis via Banhatti indices.

Brain tumors pose a significant health challenge due to their aggressive nature, complex structure, and often poor prognosis. They can be categorized as benign or malignant, with gliomas being the most prevalent and deadly form. Conventional treatments like surgery, radiation, and chemotherapy often fall short in effectiveness, prompting the need for innovative therapeutic approaches. Quantitative Structure-Property Relationship (QSPR) analysis has emerged as a cutting-edge computational tool for predicting molecular properties and aiding in the discovery of potential anti-tumor agents. This study leverages QSPR analysis to evaluate and forecast the bioactivity and pharmacokinetics of compounds designed to target brain tumors. The Banhatti indices consistently demonstrate high correlation values ranging from 0.8 to 0.9 with the specified properties. To enhance the decision-making process, the CRITIC method assigns weights to each criterion (totaling 1) and employs two Multi-Criteria Decision-Making (MCDM) techniques, Combined Compromise Solution (CoCoSo) and Multi-Attributive Border Approximation area Comparison (MABAC). CoCoSo integrates various criteria in a compromise-based approach, while MABAC offers a precise comparative framework for ranking therapeutic options. Notably, the Afinitor anti-brain tumor medications analyzed in this study were ranked No. 1 in both the CoCoSo and MABAC methods, underscoring the reliability of these approaches for decision-making purposes. In contrast to earlier research that mostly relies on single-criterion evaluation or various degree-based topological indices for drug discovery, this study fills the gap by integrating topological indices such as Banhatti indices with drug physical characteristics to offer an extensive perspective. The findings demonstrate the effectiveness of the methodology, with consistent rankings aligned with known therapeutic outcomes. This work establishes a foundation for integrating QSPR and MCDM techniques, contributing to advancements in drug discovery for complex diseases such as brain tumors.