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揭示SARS-CoV-2 Mpro突变冷点和热点:动态残留网络分析与机器学习相遇

Victor Barozi1, Shrestha Chakraborty2, Shaylyn Govender1

  • 1Research Unit in Bioinformatics (RUBi), Department of Biochemistry, Microbiology and Bioinformatics, Rhodes University, Makhanda 6139, South Africa.

Computational and structural biotechnology journal
|November 11, 2024
PubMed
概括

预测病毒突变是药物设计的关键. 动态残留网络分析与机器学习相结合,可以准确识别SARS-CoV-2的突变热点.

关键词:
3CLpro 3CLpro是什么 3CLpro是什么 3CLpro是什么人工神经网络的人工神经网络在 COVID-19 疫情中,基莫特里普辛类蛋白酶是一种类似于基莫特里普辛的蛋白酶.决策树 决策树是一个决策树.药物设计 药物设计药物耐药性 药物耐药性 药物耐药性在Mpro Mpro中使用.网络分析 网络分析在 Nsp5 中使用.病原体的进化过程这就是SARS-CoV.

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