基于药剂的虚拟查,分子对接和分子动力学研究,以发现新型神经aminidase 抑制剂
Bourougaa Lotfi1, Ouassaf Mebarka1, Shafi Ullah Khan2
1Group of Computational and Medicinal Chemistry, LMCE Laboratory, University of Biskra, Biskra, Algeria.
Journal of biomolecular structure & dynamics
|June 19, 2023
概括
这项研究使用计算方法确定了新型神经氨基酶抑制剂. 开发的药模型准确地预测了具有有利药物相似性和稳定的结合力的强效药物候选者.
科学领域:
- 计算化学计算化学
- 药用化学 医学化学
- 药物发现 药物发现 药物发现
背景情况:
- 神经氨基酶抑制剂对于抗病毒疗法至关重要.
- 开发新的抑制剂需要有效的选方法.
- p-aminosalicylic 酸衍生物显示出作为抗病毒剂的潜力.
研究的目的:
- 使用in silico方法识别新型神经氨基酶抑制剂.
- 开发和验证一个用于预测抑制剂活性的药理模型.
- 评估潜在候选药物的药物相似性和结合稳定性.
主要方法:
- 基于体的药模拟和3D QSAR.
- 分子对接和分子动力学模拟.
- 在ADMET物业预测和MM-PBSA计算.
主要成果:
- 一个具有统计学意义的3D-QSAR模型 (ADDPR_4) 被开发出来,具有很高的预测能力 (Q2=0.905,R2pred=0.905).
- 在基中ADMET分析表明,已识别的化合物具有有利的药物相似性.
- 分子动力学和MM-PBSA揭示了稳定的结合复合体,用于与神经aminidase.
结论:
- 该研究通过计算建模成功识别了潜在的神经氨基酶抑制剂.
- 经过验证的药模型是未来药物发现工作的宝贵工具.
- 排名最高的化合物需要进一步进行实验性研究,以开发抗病毒药物.
关键词:
3D-QSAR 是一个3D-QSAR.接收人 接收人在MM-PBSASA中使用.分子对接的分子对接.分子动力学分子动力学神经aminidase 抑制剂的使用在p-Aminosalicylic 酸衍生物中.作为一个药物学家,他做了一些药物学.更多相关视频
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