Asthma is a widespread disease that has affected more than 300 million people worldwide. There is no efficient or preventive treatment for this disease yet. Drug scientists are striving hard to discover a variety of medicines that can treat asthma effectively. Quantitative structure‐property relationship (QSPR) analysis of drugs used in the treatment of a specific disease through topological descriptors is one of the best techniques adopted by drug scientists to cut down unnecessary laboratory expenses. In this article, we have done a QSPR analysis to identify the best predictor for the six properties namely, boiling point, enthalpy, flash point, log P, molar volume, and molar refractivity of 19 different anti‐asthmatic drugs using a set of 16 distance‐based topological indices. Also, we compare our results with QSPR analysis done by using certain degree‐based indices for the chosen drugs and obtain new directions.

中文翻译：

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使用一些新的基于距离的拓扑指数进行抗哮喘药物的 QSPR 分析：比较研究

哮喘是一种广泛传播的疾病，影响了全世界超过 3 亿人。目前还没有针对这种疾病的有效或预防性治疗方法。药物科学家正在努力寻找多种可以有效治疗哮喘的药物。通过拓扑描述符对用于治疗特定疾病的药物进行定量结构-性质关系（QSPR）分析是药物科学家为减少不必要的实验室费用而采用的最佳技术之一。在本文中，我们使用一组 16 种药物进行了 QSPR 分析，以确定 19 种不同抗哮喘药物的 6 个属性（即沸点、焓、闪点、log P、摩尔体积和摩尔折光率）的最佳预测因子。基于距离的拓扑指数。此外，我们将我们的结果与使用所选药物的某些基于程度的指数进行的 QSPR 分析进行比较，并获得新的方向。