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机器学习引导的查和分子对接,用于提出针对MERS-CoV 3CL蛋白酶的自然衍生药物候选药物

Mebarka Ouassaf¹, Radhia Mazri¹, Shafi Ullah Khan^2,3

¹Group of Computational and Medicinal Chemistry, LMCE Laboratory, University of Biskra, Biskra 07000, Algeria.

International journal of molecular sciences

|April 17, 2025

概括

机器学习发现了潜在的MERS-CoV 3CL蛋白酶抑制剂. 像Perenniporide B这样的顶级候选者表现出强烈的结合,表明作为抗病毒剂的承诺.

科学领域:

计算化学和药物发现
病毒学和传染病学.
机器学习在药物化学中的应用.

背景情况:

中东呼吸综合征冠状病毒 (MERS-CoV) 构成了严重的公共卫生威胁.
MERS-CoV 3CL 蛋白酶是抗病毒药物开发的关键目标.
识别新型抑制剂对于对抗新出现的病毒感染至关重要.

研究的目的:

采用机器学习来识别MERS-CoV 3CL蛋白酶的新型抑制剂.
选大量自然化合物的图书馆,以检查潜在的抗病毒活性.
用分子建模技术验证有前途的候选人.

主要方法:

随机森林 (RF) 算法用于化合物抑制的预测建模.
从PubChem.的14194种天然化合物的虚拟选.
分子对接和分子动力学模拟以评估结合亲和力和稳定性.

主要成果:

射频模型实现了高预测性能 (精度=0.97,ROC-AUC=0.98,F1得分=0.98).
确定了Perenniporide B,Phellifuropyranone A和Terrestrol G作为具有强大的结合能量 (-9.17, -9.08, -8.71 kcal/mol) 的顶级候选物.
模拟证实了这些化合物在蛋白酶活性部位内的稳定相互作用.

结论:

机器学习与分子建模相结合,加速了抗病毒疗法的发现.
已识别的天然化合物显示出作为MERS-CoV 3CL蛋白酶抑制剂的显著潜力.
这种方法为开发针对新出现的病毒威胁的治疗方法提供了可行的策略.

关键词:

这就是MERS-CoV.机器学习是机器学习.分子对接的分子对接.分子动力学模拟,分子动力学模拟随机的森林随机的森林

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