新型冠状病毒肺炎(COVID-19)治疗药物选择的计算机ADMET与对接研究

Sagar A. Jadhav, Payal Chavan, Supriya Suresh Shete, Dipti Shantisagar Patil, Saroj Dyandev Kolekar, Godfrey Rudolph Mathews, Dipak Babaso Bhingardev, P. Pawar
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引用次数: 0

摘要

对接技术是基于结构的药物设计中应用最广泛的方法之一,因为它能够预测配体与合适靶点的结合构象。与靶点(即生物活性肽或特定受体)的结合/亲和力能力为进一步研究结合构象模式和亲和力提供了强有力的证据。目的:本研究旨在评估目前原料药在COVID-19中的潜在应用。方法:采用SWISS ADME、MOLSOFT、MOLINSPIRATION、PYMOL、AUTO-DOCK VINA、BIOVIA DS VISUALIZER等软件进行分子对接。结果:目前的研究了解了所选原料药的药物可能性特征及其与SWISS TARGET PREDICTION选择的各种靶点的结合亲和力。结论:本研究表明,除Remdesivir和Anakinra外,所有靶点均符合Lipinski五法则,并且对靶点具有增强的结合亲和力,蛋白质配体相互作用的结合能也证实了该配体符合炸药口袋,这为进一步的体内和体外评价提供了证据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
In Silico ADMET and Docking Study of Selected Drug Used in Therapy of COVID-19
Docking is one of the most widely utilized technique used method in structure -based drug design because of its capability to predict the binding conformation of ligands to appropriate target. Ability of binding/ affinity towards the target i.e., bioactive peptides or specific receptor provides strong evidence of binding conformation pattern and affinity for further investigation. Aim- The present study was conducted for evaluation of current API’s potential used in COVID-19. Methods: In-silico molecular docking was performed using softwares such as SWISS ADME, MOLSOFT, MOLINSPIRATION, PYMOL, AUTO-DOCK VINA AND BIOVIA DS VISUALIZER. Results: The current research comprehend the drug likeliness character of selected API’s and their binding affinity with various targets selected by SWISS TARGET PREDICTION. Conclusion: The present investigation suggests that all the targets follow Lipinski rule of five except Remdesivir and Anakinra besides which it possesses enhanced binding affinity toward targets, the binding energy of the protein ligand interaction additionally confirms that the ligand fits into the dynamite pockets which proves to be evident for further in- vivo and in-vitro evaluations.
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