Topological indices (Decision matrix).

Based on topological descriptors, QSPR analysis is an incredibly helpful statistical method for examining many physical and chemical properties of compounds without demanding costly and time-consuming laboratory tests. Firstly, we discuss and provide research on kidney cancer drugs using topological indices and done partition of the edges of kidney cancer drugs which are based on the degree. Secondly, we examine the attributes of nineteen drugs casodex, eligard, mitoxanrone, rubraca, and zoladex, etc and among others, using linear QSPR model. The study in the article not only demonstrates a good correlation between TIs and physical characteristics with the QSPR model being the most suitable for predicting complexity, enthalpy, molar refractivity, and other factors and a best-fit model is attained in this study. This theoretical approach might benefit chemists and professionals in the pharmaceutical industry to forecast the characteristics of kidney cancer therapies. This leads towards new opportunities to paved the way for drug discovery and the formation of efficient and suitable treatment options in therapeutic targeting. We also employed multicriteria decision making techniques like COPRAS and PROMETHEE-II for ranking of said disease treatment drugs and physicochemical characteristics.基于拓扑描述符（topological descriptors）的定量结构-性质关系（Quantitative Structure-Property Relationship, QSPR）分析是一种极具实用价值的统计方法，无需开展耗时耗力且成本高昂的实验室检测，即可对化合物的诸多物理化学性质进行分析表征。首先，本研究围绕肾癌药物展开相关研究，采用拓扑指数并基于顶点度数对肾癌药物分子的边进行划分。其次，本研究采用线性QSPR模型，对包括比卡鲁胺（Casodex）、艾去适（Eligard）、米托蒽醌（Mitoxanrone）、芦卡帕利（Rubraca）以及诺雷得（Zoladex）在内的19种药物的属性进行了分析。本研究不仅证实了拓扑指数与物理性质之间存在良好的相关性，且所构建的QSPR模型最适用于预测复杂度、焓、摩尔折射度等参数，本研究最终得到了最优拟合模型。该理论方法可为化学领域研究者与制药行业从业人员预测肾癌治疗药物的性质提供助力，为药物研发开辟新机遇，同时为治疗靶向领域开发高效且适宜的治疗方案提供支撑。本研究还采用了复杂比例评估法（COPRAS）、偏好顺序结构评估法II（PROMETHEE-II）等多准则决策技术，对上述疾病治疗药物及其理化性质进行排序分析。