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通过in silico方法对MAO-B抑制剂的活性和选择性的新见解

Liliana Pacureanu¹, Alina Bora¹, Luminita Crisan¹

¹"Coriolan Dragulescu" Institute of Chemistry, 24 Mihai Viteazu Ave., 300223 Timisoara, Romania.

International journal of molecular sciences

|June 10, 2023

概括

我们开发了一种计算方法来发现新的MAO-B抑制剂,使用3D QSAR和分子对接. 这种方法有助于为MAO-B相关疾病设计强效和选择性的候选药物.

科学领域:

计算化学计算化学
药用化学医学化学
药理学药理学是指药理学的学科.

背景情况:

单胺氧化酶B (MAO-B) 是神经退行性疾病的关键标.
鉴定选择性MAO-B抑制剂对于治疗开发至关重要.
现有的方法需要广泛的实验选.

研究的目的:

开发和验证用于识别新型MAO-B抑制剂的综合计算方法.
为设计强效和选择性MAO-B抑制剂建立一个预测模型.
引导针对MAO-B的候选药物的合理设计.

主要方法:

开发了一种基于药理原子的3D定量结构-活性关系 (QSAR) 模型.
使用活动悬崖,分子指纹 (ECFP4) 和分子对接.
分析了126个MAO-B抑制分子的数据集.

主要成果:

取得了一个统计学上显著的3D QSAR模型 (R2=0.900,Q2=0.774).
确定了与抑制活性相关的疏水和吸收电子的电场.
在ECFP4分析中,突出了林-2-支架在MAO-B选择性中的作用 (AUC=0.962).
对接揭示了与残留物TYR:435,TYR:326,CYS:172,GLN:206的关键相互作用.

结论:

综合计算方法有效地预测MAO-B抑制剂的强度和选择性.
这一策略有助于快速设计和发现新的MAO-B抑制剂.
该方法适用于选其他复合库和目标.

关键词:

在ECFP4中,我们可以使用ECFP4.活动悬崖活动悬崖.单氨酸氧化酶B的一种.选择性的选择性

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