通过拓扑指数、多图建模和多标准决策建立抗癌药物药代动力学特征的结构-属性模型

IF 2.3 3区 化学 Q3 CHEMISTRY, PHYSICAL International Journal of Quantum Chemistry Pub Date : 2024-06-03 DOI:10.1002/qua.27428
Ugasini Preetha Pandi, Sakander Hayat, Suresh Marimuthu, Julietraja Konsalraj
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引用次数: 0

摘要

本研究通过ev-ve度拓扑指数和QSPR分析,深入探讨了如何估算有前景的抗癌药物(尤其是氨基酸类烷化剂)的ADME特性。研究的目的是比较多图建模与简单图建模在估计六种 ADME 特性方面的不同。结果表明,多图建模性能优越,具有显著的高相关性,如 r = 0 . 926 $$ r=0.926 $$ 与简单图形建模的 r = 0 . 68 $$ r=0.68 $$ 采用 M 2 $$ {M}_2 $$ -ev 指数。这说明在药物研发中需要复杂的建模技术。主要目的是比较使用拓扑结构描述符的多图建模和简单图建模,然后进行 QSPR 分析,以确定估计 ADME 特性的更好方法。MCDM 权重分配技术验证了相关值,增强了对估算器的理解,并确定了潜在药物。这强调了考虑各种 MCDM 方法和权重分配方法对于医疗保健领域可靠决策的重要性。
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Structure-property modeling of pharmacokinetic characteristics of anticancer drugs via topological indices, multigraph modeling and multi-criteria decision making

This study presents an in-depth inquiry into estimating ADME properties for promising anticancer drugs, particularly amino acid-based alkylating agents, through ev-ve degree topological indices and QSPR analysis. The aim of the study is to compare multigraph modeling to simple graph modeling in estimating six ADME properties. Results demonstrate that multigraph modeling's superior performance, with notable high correlations such as r = 0 . 926 $$ r=0.926 $$ for maximum passive absorption (MPA) using the M-ev index, compared to simple graph modeling's r = 0 . 68 $$ r=0.68 $$ with the M 2 $$ {M}_2 $$ -ev index. This emphasizes the need for sophisticated modeling techniques in drug development. The primary objective is to compare multigraph and simple graph modeling using topological structure descriptors, followed by QSPR analysis to determine the better approach in estimating ADME properties. MCDM weight allocation techniques validate correlation values, enhancing understanding of estimators and identifying potential drugs. This underscores the importance of considering various MCDM methods and weight allocation approaches for reliable decision-making in healthcare contexts.

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来源期刊
International Journal of Quantum Chemistry
International Journal of Quantum Chemistry 化学-数学跨学科应用
CiteScore
4.70
自引率
4.50%
发文量
185
审稿时长
2 months
期刊介绍: Since its first formulation quantum chemistry has provided the conceptual and terminological framework necessary to understand atoms, molecules and the condensed matter. Over the past decades synergistic advances in the methodological developments, software and hardware have transformed quantum chemistry in a truly interdisciplinary science that has expanded beyond its traditional core of molecular sciences to fields as diverse as chemistry and catalysis, biophysics, nanotechnology and material science.
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