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计算机辅助策略研究 5-(取代亚苄基)噻唑烷-2,4-二酮类化合物以开发新型强效 PTP1B 抑制剂:QSAR 建模、分子对接、分子动力学、PASS 预测和 DFT 研究。

Computer-Aided Strategy on 5-(Substituted benzylidene) Thiazolidine-2,4-Diones to Develop New and Potent PTP1B Inhibitors: QSAR Modeling, Molecular Docking, Molecular Dynamics, PASS Predictions, and DFT Investigations.

机构信息

VTRS Laboratory, Faculty of Sciences, University of El Oued, P.O. Box 789, El Oued 39000, Algeria.

Group of Computational and Medicinal Chemistry, Laboratory of Molecular Chemistry and Environment, University of Biskra, P.O. Box 145, Biskra 07000, Algeria.

出版信息

Molecules. 2024 Feb 10;29(4):822. doi: 10.3390/molecules29040822.

DOI:10.3390/molecules29040822
PMID:38398573
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10892620/
Abstract

A set of 5-(substituted benzylidene) thiazolidine-2,4-dione derivatives was explored to study the main structural requirement for the design of protein tyrosine phosphatase 1B (PTP1B) inhibitors. Utilizing multiple linear regression (MLR) analysis, we constructed a robust quantitative structure-activity relationship (QSAR) model to predict inhibitory activity, resulting in a noteworthy correlation coefficient (R) of 0.942. Rigorous cross-validation using the leave-one-out (LOO) technique and statistical parameter calculations affirmed the model's reliability, with the QSAR analysis revealing 10 distinct structural patterns influencing PTP1B inhibitory activity. Compound emerged as the optimal scaffold for drug design. Seven new PTP1B inhibitors were designed based on the QSAR model, followed by molecular docking studies to predict interactions and identify structural features. Pharmacokinetics properties were assessed through drug-likeness and ADMET studies. After that density functional theory (DFT) was conducted to assess the stability and reactivity of potential diabetes mellitus drug candidates. The subsequent dynamic simulation phase provided additional insights into stability and interactions dynamics of the top-ranked compound . This comprehensive approach enhances our understanding of potential drug candidates for treating diabetes mellitus.

摘要

我们研究了一组 5-(取代苄叉基)噻唑烷-2,4-二酮衍生物,以探讨设计蛋白酪氨酸磷酸酶 1B(PTP1B)抑制剂的主要结构要求。利用多元线性回归(MLR)分析,我们构建了一个稳健的定量构效关系(QSAR)模型来预测抑制活性,得到了显著的相关系数(R)为 0.942。严格的交叉验证使用留一法(LOO)技术和统计参数计算证实了模型的可靠性,QSAR 分析揭示了 10 个不同的结构模式影响 PTP1B 抑制活性。化合物 被认为是药物设计的最佳支架。根据 QSAR 模型设计了 7 种新型 PTP1B 抑制剂,随后进行分子对接研究以预测相互作用和识别结构特征。通过类药性和 ADMET 研究评估了药代动力学性质。之后进行了密度泛函理论(DFT)计算,以评估潜在糖尿病药物候选物的稳定性和反应性。随后的动态模拟阶段提供了对排名靠前的化合物 的稳定性和相互作用动力学的更多见解。这种综合方法增强了我们对治疗糖尿病的潜在药物候选物的理解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/90b7/10892620/21ffb421c44b/molecules-29-00822-g012.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/90b7/10892620/21ffb421c44b/molecules-29-00822-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/90b7/10892620/9191f13daf1c/molecules-29-00822-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/90b7/10892620/5c99df09c8ed/molecules-29-00822-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/90b7/10892620/6b4eaaa55572/molecules-29-00822-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/90b7/10892620/960386bf873c/molecules-29-00822-g004a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/90b7/10892620/5c2f2add96cd/molecules-29-00822-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/90b7/10892620/60e8796eb94d/molecules-29-00822-g006a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/90b7/10892620/9a45c19989db/molecules-29-00822-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/90b7/10892620/cdec12a6db0b/molecules-29-00822-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/90b7/10892620/2ad101751c71/molecules-29-00822-g009.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/90b7/10892620/a5452a955fe7/molecules-29-00822-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/90b7/10892620/21ffb421c44b/molecules-29-00822-g012.jpg

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