A quadratic regression model to quantify certain latest corona treatment drug molecules based on coindices of M-polynomial

Shahid Zaman, Sadaf Rasheed, Ahmed Alamer
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Abstract

Medical researches encounter time, cost, solubility, and data challenges in new drug development. Within the realm of theory, chemical graph theory plays a crucial role in drug design. The SARS-CoV-2 pandemic prompts urgent exploration of drugs like favipiravir, baricitinib, fluvoxamine, nirmatrelvir, molnupiravir, lopinavir, and remdesivir. Developing effective treatments of COVID-19 is a top priority for health authorities, as they strive to curb the pandemic’s impact on public health and prevent future outbreaks. This article characterized CoM-polynomial and their derivatives to determine the topological characteristics of several antiviral drugs. Using this approach, we examine physicochemical properties with quadratic regression method. The results indicate significant correlation between the investigated topological coindices and the physicochemical properties of potential antiviral medicines.

Abstract Image

基于M-多项式协系数的二次回归模型,量化某些最新的电晕治疗药物分子
医学研究在新药开发过程中会遇到时间、成本、溶解度和数据方面的挑战。在理论领域,化学图谱理论在药物设计中发挥着至关重要的作用。SARS-CoV-2 大流行促使人们对法非拉韦、巴利替尼、氟伏沙明、尼尔马特韦、莫鲁吡拉韦、洛匹那韦和雷米替韦等药物进行紧急探索。开发有效的 COVID-19 治疗方法是卫生当局的当务之急,因为他们正努力遏制该流行病对公共卫生的影响并防止未来的爆发。本文以 CoM-polynomial 及其衍生物为特征,确定了几种抗病毒药物的拓扑特征。利用这种方法,我们用二次回归法研究了理化特性。结果表明,所研究的拓扑共线性与潜在抗病毒药物的理化性质之间存在明显的相关性。
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