Regression model of SDD (G).

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，可在无需开展昂贵且耗时的实验室测试的前提下，研究化合物的诸多物理与化学性质。其一，本研究针对肾癌治疗药物展开相关探讨与研究，采用拓扑指数，并基于顶点度对肾癌治疗药物分子的边进行划分。其二，本研究采用线性QSPR模型，对19种药物（如casodex、eligard、mitoxanrone、rubraca及zoladex等）的属性进行分析。本文研究不仅证实了拓扑指数（Topological Indices，TIs）与物理性质之间存在良好相关性，且QSPR模型最适用于预测复杂度、焓、摩尔折射度等参数，本研究亦获得了最优拟合模型。该理论方法可助力化学家与制药行业从业者预测肾癌治疗药物的相关性质，为药物研发以及治疗靶向领域中高效适配治疗方案的构建开辟新路径、带来新机遇。本研究还采用了COPRAS、PROMETHEE-II等多准则决策技术，对上述疾病治疗药物及理化性质进行排序。