利用拓扑指标建立一些潜在抗癌药物的 QSPR 模型

IF 2.4 3区 化学 Q2 CHEMISTRY, ORGANIC Polycyclic Aromatic Compounds Pub Date : 2024-02-07 DOI:10.1080/10406638.2023.2189270
K. Pattabiraman , S. Sudharsan , Murat Cancan
{"title":"利用拓扑指标建立一些潜在抗癌药物的 QSPR 模型","authors":"K. Pattabiraman ,&nbsp;S. Sudharsan ,&nbsp;Murat Cancan","doi":"10.1080/10406638.2023.2189270","DOIUrl":null,"url":null,"abstract":"<div><p>In Sri Lanka as well as the rest of the globe, cancer is the top cause of mortality. One of the key medicines in treating tumors is anticancer medications and delivery dendrimers. To prevent the formation of the rapid proliferation of cancer cells, several tests were carried out. Because of this, research on dendrimers and anti-cancer medications is crucial. Topological indices (TIs) are molecular descriptors numerical values corresponding to the physical characteristics of a molecule’s chemical structure. It costs money to determine a molecule’s physical characteristics in a lab since it takes a lot of materials, medications, and time. Therefore, the relevant information about molecules may be obtained by computing TIs. This study’s goals are to compute hitherto uncalculated eccentricity-based TIs for various anticancer structures and to use curvilinear regression models to forecast the physical characteristics of particular anticancer medications. These anticancer medications were given different TIs developed in this work, allowing the researchers to understand the physical, physicochemical, and chemical characteristics related to them. In addition comparative study of the novel indices with some well-known and mostly used indices in structure–property modeling and anticancer drugs in performed.</p></div>","PeriodicalId":20303,"journal":{"name":"Polycyclic Aromatic Compounds","volume":null,"pages":null},"PeriodicalIF":2.4000,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"QSPR Modeling with Topological Indices of Some Potential Drugs against Cancer\",\"authors\":\"K. Pattabiraman ,&nbsp;S. Sudharsan ,&nbsp;Murat Cancan\",\"doi\":\"10.1080/10406638.2023.2189270\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>In Sri Lanka as well as the rest of the globe, cancer is the top cause of mortality. One of the key medicines in treating tumors is anticancer medications and delivery dendrimers. To prevent the formation of the rapid proliferation of cancer cells, several tests were carried out. Because of this, research on dendrimers and anti-cancer medications is crucial. Topological indices (TIs) are molecular descriptors numerical values corresponding to the physical characteristics of a molecule’s chemical structure. It costs money to determine a molecule’s physical characteristics in a lab since it takes a lot of materials, medications, and time. Therefore, the relevant information about molecules may be obtained by computing TIs. This study’s goals are to compute hitherto uncalculated eccentricity-based TIs for various anticancer structures and to use curvilinear regression models to forecast the physical characteristics of particular anticancer medications. These anticancer medications were given different TIs developed in this work, allowing the researchers to understand the physical, physicochemical, and chemical characteristics related to them. In addition comparative study of the novel indices with some well-known and mostly used indices in structure–property modeling and anticancer drugs in performed.</p></div>\",\"PeriodicalId\":20303,\"journal\":{\"name\":\"Polycyclic Aromatic Compounds\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2024-02-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Polycyclic Aromatic Compounds\",\"FirstCategoryId\":\"92\",\"ListUrlMain\":\"https://www.sciencedirect.com/org/science/article/pii/S1040663823003275\",\"RegionNum\":3,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"CHEMISTRY, ORGANIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Polycyclic Aromatic Compounds","FirstCategoryId":"92","ListUrlMain":"https://www.sciencedirect.com/org/science/article/pii/S1040663823003275","RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CHEMISTRY, ORGANIC","Score":null,"Total":0}
引用次数: 0

摘要

在斯里兰卡和全球其他国家,癌症是导致死亡的首要原因。治疗肿瘤的关键药物之一是抗癌药物和递送树枝状聚合物。为了防止癌细胞快速增殖,进行了多项试验。因此,对树枝状聚合物和抗癌药物的研究至关重要。拓扑指数(TI)是与分子化学结构的物理特性相对应的分子描述符数值。在实验室测定分子的物理特性需要花费大量的材料、药物和时间,因此成本很高。因此,可以通过计算 TIs 来获取分子的相关信息。本研究的目标是为各种抗癌结构计算迄今为止尚未计算过的基于偏心率的总指数,并使用曲线回归模型预测特定抗癌药物的物理特性。这些抗癌药物在这项工作中被赋予了不同的 TI,使研究人员能够了解与之相关的物理、物理化学和化学特征。此外,研究人员还将新指数与结构-性质建模和抗癌药物中一些常用的知名指数进行了比较研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
QSPR Modeling with Topological Indices of Some Potential Drugs against Cancer

In Sri Lanka as well as the rest of the globe, cancer is the top cause of mortality. One of the key medicines in treating tumors is anticancer medications and delivery dendrimers. To prevent the formation of the rapid proliferation of cancer cells, several tests were carried out. Because of this, research on dendrimers and anti-cancer medications is crucial. Topological indices (TIs) are molecular descriptors numerical values corresponding to the physical characteristics of a molecule’s chemical structure. It costs money to determine a molecule’s physical characteristics in a lab since it takes a lot of materials, medications, and time. Therefore, the relevant information about molecules may be obtained by computing TIs. This study’s goals are to compute hitherto uncalculated eccentricity-based TIs for various anticancer structures and to use curvilinear regression models to forecast the physical characteristics of particular anticancer medications. These anticancer medications were given different TIs developed in this work, allowing the researchers to understand the physical, physicochemical, and chemical characteristics related to them. In addition comparative study of the novel indices with some well-known and mostly used indices in structure–property modeling and anticancer drugs in performed.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Polycyclic Aromatic Compounds
Polycyclic Aromatic Compounds 化学-有机化学
CiteScore
3.70
自引率
20.80%
发文量
412
审稿时长
3 months
期刊介绍: The purpose of Polycyclic Aromatic Compounds is to provide an international and interdisciplinary forum for all aspects of research related to polycyclic aromatic compounds (PAC). Topics range from fundamental research in chemistry (including synthetic and theoretical chemistry) and physics (including astrophysics), as well as thermodynamics, spectroscopy, analytical methods, and biology to applied studies in environmental science, biochemistry, toxicology, and industry. Polycyclic Aromatic Compounds has an outstanding Editorial Board and offers a rapid and efficient peer review process, as well as a flexible open access policy.
期刊最新文献
Drugs and Their Mode of Action: A Review on Sulfur-Containing Heterocyclic Compounds Synthesis, Spectroscopic Characterization, DFT, Molecular Docking, Catechol Oxidase Activity, and Anti-SARS-CoV-2 of Acylhydrazone Derivatives Eco-Friendly One-Pot Synthesis of Tetrahydrobenzo[b]Pyran and Dihydropyrano[3,2-c]Chromene Derivatives Using Functionalized Fe3O4@SiO2@SO3H Under MWI Structural Elucidation of Some Flavonoids Isolated from Litsea costalis and Their Biological Evaluation Synthesis of Pharmacologically Potent Benzimidazole Analogs
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1