利用二维和三维 QSAR、分子对接和 ADME 性能开发潜在的抗糖尿病药物 α 淀粉酶抑制剂的室内研究

IF 1.2 4区 医学 Q4 CHEMISTRY, MEDICINAL
Kalusing S. Padvi, Aniket P. Sarkate, Shashikant V. Bhandari, Mahadevi V. Kendre
{"title":"利用二维和三维 QSAR、分子对接和 ADME 性能开发潜在的抗糖尿病药物 α 淀粉酶抑制剂的室内研究","authors":"Kalusing S. Padvi, Aniket P. Sarkate, Shashikant V. Bhandari, Mahadevi V. Kendre","doi":"10.2174/0115701808279839240206123454","DOIUrl":null,"url":null,"abstract":"Background: A series of 2-arylbenzimidazole derivatives were designed and developed as antidiabetic drugs using 2D and 3D QSAR, molecular docking and ADME studies. background: A series of 2-arylbenzimidazole derivatives were designed and developed as antidiabetic drugs using 2D and 3D QSAR, molecular docking and ADME studies. Methods: All molecular modeling studies were performed using Molecular Design Suite V-Life MDS software. New chemical entities (NCEs) were designed based on the results of 2D and 3D QSAR studies. Docking studies were performed with the designed NCEs in PDB: 5E0F and the results were compared with the receptor ligand. According to the ADME results, all the proposed compounds have good oral absorption, correct molecular weight, QPlogPo/w. All units show oral absorption above 80%, it is considered well absorbed. All the proposed units show satisfactory results in the area. This indicated that these NCEs have little or no chance of failure in the final stages of the drug development process. Results: The 2D QSAR results showed that the descriptor k2alpha, T_T_N_5, IodinesCount and BrominesCount play the most important role in determining the inhibitory activity of α-amylase. Although 3D QSAR showed that, the q2 and Pred_r2 values of the model (SA kNN MFA model) were 0.7476 and 0.6932. The G score of the proposed compound numbers mol-1, mol-2, mol-3, mol- 4, mol-5, mol-6, mol-7 and mol-8 are better compared to the standards, indicating that the proposed compounds have good binding properties affinity to bind to α-amylase. Conclusion: These investigations have produced statistically significant and exceptionally reliable 2D and 3D Quantitative Structure-Activity Relationship (QSAR) models for antidiabetic medications, particularly α-amylase inhibitors. Furthermore, docking experiments involving the α-amylase enzyme have revealed that the binding energies of most Novel Chemical Entities (NCEs) are comparable to those of the established standards. Docking studies with α-amylase enzyme showed that most NCEs have binding energies comparable to the standard.","PeriodicalId":18059,"journal":{"name":"Letters in Drug Design & Discovery","volume":null,"pages":null},"PeriodicalIF":1.2000,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Development of Potential Antidiabetic Agents Using 2D and 3D QSAR, Molecular Docking and ADME Properties In-silico Studies of α-Amylase Inhibitors\",\"authors\":\"Kalusing S. Padvi, Aniket P. Sarkate, Shashikant V. Bhandari, Mahadevi V. Kendre\",\"doi\":\"10.2174/0115701808279839240206123454\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Background: A series of 2-arylbenzimidazole derivatives were designed and developed as antidiabetic drugs using 2D and 3D QSAR, molecular docking and ADME studies. background: A series of 2-arylbenzimidazole derivatives were designed and developed as antidiabetic drugs using 2D and 3D QSAR, molecular docking and ADME studies. Methods: All molecular modeling studies were performed using Molecular Design Suite V-Life MDS software. New chemical entities (NCEs) were designed based on the results of 2D and 3D QSAR studies. Docking studies were performed with the designed NCEs in PDB: 5E0F and the results were compared with the receptor ligand. According to the ADME results, all the proposed compounds have good oral absorption, correct molecular weight, QPlogPo/w. All units show oral absorption above 80%, it is considered well absorbed. All the proposed units show satisfactory results in the area. This indicated that these NCEs have little or no chance of failure in the final stages of the drug development process. Results: The 2D QSAR results showed that the descriptor k2alpha, T_T_N_5, IodinesCount and BrominesCount play the most important role in determining the inhibitory activity of α-amylase. Although 3D QSAR showed that, the q2 and Pred_r2 values of the model (SA kNN MFA model) were 0.7476 and 0.6932. The G score of the proposed compound numbers mol-1, mol-2, mol-3, mol- 4, mol-5, mol-6, mol-7 and mol-8 are better compared to the standards, indicating that the proposed compounds have good binding properties affinity to bind to α-amylase. Conclusion: These investigations have produced statistically significant and exceptionally reliable 2D and 3D Quantitative Structure-Activity Relationship (QSAR) models for antidiabetic medications, particularly α-amylase inhibitors. Furthermore, docking experiments involving the α-amylase enzyme have revealed that the binding energies of most Novel Chemical Entities (NCEs) are comparable to those of the established standards. Docking studies with α-amylase enzyme showed that most NCEs have binding energies comparable to the standard.\",\"PeriodicalId\":18059,\"journal\":{\"name\":\"Letters in Drug Design & Discovery\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.2000,\"publicationDate\":\"2024-02-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Letters in Drug Design & Discovery\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.2174/0115701808279839240206123454\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"CHEMISTRY, MEDICINAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Letters in Drug Design & Discovery","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.2174/0115701808279839240206123454","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"CHEMISTRY, MEDICINAL","Score":null,"Total":0}
引用次数: 0

摘要

背景:通过二维和三维QSAR、分子对接和ADME研究,设计并开发了一系列2-芳基苯并咪唑衍生物作为抗糖尿病药物:通过二维和三维 QSAR、分子对接和 ADME 研究,设计并开发了一系列 2-芳基苯并咪唑衍生物作为抗糖尿病药物。方法:所有分子建模研究均使用分子设计套件 V-Life MDS 软件进行。根据二维和三维 QSAR 研究结果设计了新化学实体 (NCE)。利用 PDB: 5E0F 中设计的 NCEs 进行了对接研究,并将结果与受体配体进行了比较。根据 ADME 结果,所有提议的化合物都具有良好的口服吸收性、正确的分子量、QPlogPo/w。所有单位的口服吸收率都在 80% 以上,可以认为吸收良好。所有提议的化合物在该领域都取得了令人满意的结果。这表明这些 NCE 在药物开发过程的最后阶段几乎没有失败的可能。结果:二维 QSAR 结果表明,描述因子 k2alpha、T_T_N_5、IodinesCount 和 BrominesCount 在决定α-淀粉酶的抑制活性中起着最重要的作用。三维 QSAR 结果表明,模型(SA kNN MFA 模型)的 q2 值和 Pred_r2 值分别为 0.7476 和 0.6932。与标准化合物相比,所提化合物的 mol-1、mol-2、mol-3、mol-4、mol-5、mol-6、mol-7 和 mol-8 的 G 值均较好,表明所提化合物与 α 淀粉酶具有良好的亲和性。结论这些研究为抗糖尿病药物,特别是α-淀粉酶抑制剂,建立了具有统计意义且非常可靠的二维和三维定量结构-活性关系(QSAR)模型。此外,涉及α-淀粉酶的对接实验表明,大多数新型化学实体(NCE)的结合能与既定标准相当。与α-淀粉酶的对接研究表明,大多数新化学实体的结合能与标准物质相当。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Development of Potential Antidiabetic Agents Using 2D and 3D QSAR, Molecular Docking and ADME Properties In-silico Studies of α-Amylase Inhibitors
Background: A series of 2-arylbenzimidazole derivatives were designed and developed as antidiabetic drugs using 2D and 3D QSAR, molecular docking and ADME studies. background: A series of 2-arylbenzimidazole derivatives were designed and developed as antidiabetic drugs using 2D and 3D QSAR, molecular docking and ADME studies. Methods: All molecular modeling studies were performed using Molecular Design Suite V-Life MDS software. New chemical entities (NCEs) were designed based on the results of 2D and 3D QSAR studies. Docking studies were performed with the designed NCEs in PDB: 5E0F and the results were compared with the receptor ligand. According to the ADME results, all the proposed compounds have good oral absorption, correct molecular weight, QPlogPo/w. All units show oral absorption above 80%, it is considered well absorbed. All the proposed units show satisfactory results in the area. This indicated that these NCEs have little or no chance of failure in the final stages of the drug development process. Results: The 2D QSAR results showed that the descriptor k2alpha, T_T_N_5, IodinesCount and BrominesCount play the most important role in determining the inhibitory activity of α-amylase. Although 3D QSAR showed that, the q2 and Pred_r2 values of the model (SA kNN MFA model) were 0.7476 and 0.6932. The G score of the proposed compound numbers mol-1, mol-2, mol-3, mol- 4, mol-5, mol-6, mol-7 and mol-8 are better compared to the standards, indicating that the proposed compounds have good binding properties affinity to bind to α-amylase. Conclusion: These investigations have produced statistically significant and exceptionally reliable 2D and 3D Quantitative Structure-Activity Relationship (QSAR) models for antidiabetic medications, particularly α-amylase inhibitors. Furthermore, docking experiments involving the α-amylase enzyme have revealed that the binding energies of most Novel Chemical Entities (NCEs) are comparable to those of the established standards. Docking studies with α-amylase enzyme showed that most NCEs have binding energies comparable to the standard.
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
CiteScore
1.80
自引率
10.00%
发文量
245
审稿时长
3 months
期刊介绍: Aims & Scope Letters in Drug Design & Discovery publishes letters, mini-reviews, highlights and guest edited thematic issues in all areas of rational drug design and discovery including medicinal chemistry, in-silico drug design, combinatorial chemistry, high-throughput screening, drug targets, and structure-activity relationships. The emphasis is on publishing quality papers very rapidly by taking full advantage of latest Internet technology for both submission and review of manuscripts. The online journal is an essential reading to all pharmaceutical scientists involved in research in drug design and discovery.
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信