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潜在抑制剂数据评估：密度泛函理论（DFT）和分子建模方法

evaluation on potential inhibitors data: DFT and molecular modelling approaches.

作者信息

Oyebamiji Abel Kolawole, Akintelu Sunday Adewale, Adekunle David O, Oke David Gbenga, Olanrewaju Adesoji Alani, Akinola Omowumi Temitayo

机构信息

Industrial Chemistry Programme, Bowen University, PMB 284, Iwo, Osun State, Nigeria.

Department of pure and Applied Chemistry, Ladoke Akintola University of Technology, PMB 4000, Ogbomoso, Oyo State, Nigeria.

出版信息

Data Brief. 2024 May 29;55:110565. doi: 10.1016/j.dib.2024.110565. eCollection 2024 Aug.

DOI:10.1016/j.dib.2024.110565
PMID:38952955
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11214998/
Abstract

Nine heterocyclic compounds were investigated using density functional theory, molecular operating environment software, material studio, swissparam (Swiss drug design) software. In this work, the descriptors generated from the optimized compounds proved to be efficient and explain the level of reactivity of the investigated compound. The developed quantitative structure activity relationship (QSAR) model was predictive and reliable. Also, compound 9 proved to be capable of inhibiting Mt-Sp1/Matriptase (pdb id: 1eax) than other examined heterocyclic compounds. Target prediction analysis was carried out on the compound with highest binding affinity (Compound 9) and the results were reported.

摘要

使用密度泛函理论、分子操作环境软件、材料工作室、Swissparam（瑞士药物设计）软件对九种杂环化合物进行了研究。在这项工作中，从优化后的化合物生成的描述符被证明是有效的，并解释了所研究化合物的反应活性水平。所建立的定量构效关系（QSAR）模型具有预测性且可靠。此外，化合物9被证明比其他检测的杂环化合物更能抑制Mt-Sp1/Matriptase（蛋白质数据银行编号：1eax）。对具有最高结合亲和力的化合物（化合物9）进行了靶点预测分析，并报告了结果。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/13fd/11214998/fa3cab2de80f/gr19.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/13fd/11214998/8c21a71c8a2c/gr9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/13fd/11214998/c5c10b49cf8e/gr10.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/13fd/11214998/b3581405d394/gr15.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/13fd/11214998/1a50bea2394c/gr16.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/13fd/11214998/133ddb63dbc4/gr17.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/13fd/11214998/b49d4f336f62/gr18.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/13fd/11214998/fa3cab2de80f/gr19.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/13fd/11214998/883b321cb28c/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/13fd/11214998/072f1684dc03/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/13fd/11214998/d9c10569c25b/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/13fd/11214998/9f37ef18a99f/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/13fd/11214998/0bdbdc01161b/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/13fd/11214998/bad4c4a597fe/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/13fd/11214998/405a36eeedf3/gr7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/13fd/11214998/44f59d64d76c/gr8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/13fd/11214998/8c21a71c8a2c/gr9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/13fd/11214998/c5c10b49cf8e/gr10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/13fd/11214998/1c9a6e666836/gr11.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/13fd/11214998/0f4b7e7e260b/gr12.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/13fd/11214998/57f9b71455f7/gr13.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/13fd/11214998/688ad0cea438/gr14.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/13fd/11214998/b3581405d394/gr15.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/13fd/11214998/1a50bea2394c/gr16.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/13fd/11214998/133ddb63dbc4/gr17.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/13fd/11214998/fa3cab2de80f/gr19.jpg

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