Empowerments of blood cancer therapeutics via molecular descriptors

IF 3.7 2区化学 Q2 AUTOMATION & CONTROL SYSTEMS Chemometrics and Intelligent Laboratory Systems Pub Date : 2024-07-19 DOI:10.1016/j.chemolab.2024.105180

K. Pattabiraman

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Abstract

A disease caused by cellular alterations that is unrestrained cell growth and division is cancer. Many anticancer medications, including those used to treat blood, breast, and skin cancer, may have their physical, chemical, and biological features predicted. This paper presents novel distance-based topological indices (TIs) computed using the suggested KP-polynomial with blood cancer drugs. The objective of the QSPR investigation is to determine the mathematical correlation between the analyzed properties (such as Molar Volume, Refractive Index, etc.) and different descriptors associated with the molecular structure of the medications. A polynomial regression model is employed to assess the predictive capability of TIs. The results are represented using a correlation coefficient to establish the connection between the predicted and observed values of blood cancer drugs. This theoretical method could potentially enable chemists and health care professionals to anticipate the characteristics of blood cancer drugs without the need for actual experimental tests. This leads towards new opportunities to paved the way for drug discovery and the formation of efficient multicriteria decision making technique TOPSIS for ranking of said disease treatment drugs and physicochemical characteristics.

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通过分子描述符增强血癌疗法的能力

癌症是一种由细胞变化引起的疾病，即细胞无节制地生长和分裂。许多抗癌药物，包括用于治疗血癌、乳腺癌和皮肤癌的药物，都可以预测其物理、化学和生物学特征。本文介绍了使用建议的 KP-多项式与血液抗癌药物计算的基于距离的新型拓扑指数（TI）。QSPR 研究的目的是确定分析属性（如摩尔体积、折射率等）与药物分子结构相关的不同描述符之间的数学相关性。采用多项式回归模型来评估 TI 的预测能力。结果用相关系数表示，以建立血癌药物预测值和观察值之间的联系。这种理论方法有可能使化学家和医疗保健专业人员在无需实际实验测试的情况下预测血癌药物的特性。这将带来新的机遇，为药物发现铺平道路，并形成高效的多标准决策技术 TOPSIS，用于对上述疾病治疗药物和理化特性进行排序。

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来源期刊

Chemometrics and Intelligent Laboratory Systems 工程技术-分析化学

CiteScore

7.50

自引率

7.70%

发文量

169

审稿时长

3.4 months

期刊介绍： Chemometrics and Intelligent Laboratory Systems publishes original research papers, short communications, reviews, tutorials and Original Software Publications reporting on development of novel statistical, mathematical, or computer techniques in Chemistry and related disciplines. Chemometrics is the chemical discipline that uses mathematical and statistical methods to design or select optimal procedures and experiments, and to provide maximum chemical information by analysing chemical data. The journal deals with the following topics: 1) Development of new statistical, mathematical and chemometrical methods for Chemistry and related fields (Environmental Chemistry, Biochemistry, Toxicology, System Biology, -Omics, etc.) 2) Novel applications of chemometrics to all branches of Chemistry and related fields (typical domains of interest are: process data analysis, experimental design, data mining, signal processing, supervised modelling, decision making, robust statistics, mixture analysis, multivariate calibration etc.) Routine applications of established chemometrical techniques will not be considered. 3) Development of new software that provides novel tools or truly advances the use of chemometrical methods. 4) Well characterized data sets to test performance for the new methods and software. The journal complies with International Committee of Medical Journal Editors'' Uniform requirements for manuscripts.