整合分子动力学,分子对接和机器学习来预测SARS-CoV-2类似帕帕因的蛋白酶结合剂
Ann Varghese1, Jie Liu1, Tucker A Patterson1
1National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA.
Molecules (Basel, Switzerland)
|July 30, 2025
概括
研究人员通过使用机器学习和分子模拟来准SARS-CoV-2帕帕因类蛋白酶 (PLpro) 确定了五种现有的药物作为COVID-19的潜在治疗方法,加速了抗病毒药物发现.
科学领域:
- 计算生物学是一种计算生物学.
- 药物发现 药物发现
- 病毒学 病毒学
背景情况:
- 新型冠状病毒疾病2019 (COVID-19) 已造成全球健康和经济的重大破坏.
- 针对COVID-19的有效抗病毒疗法是有限的,特别是那些针对SARS-CoV-2类似帕帕因蛋白酶 (PLpro) 的疗法,这对病毒复制和免疫逃避至关重要.
研究的目的:
- 通过使用计算方法重定位药物来识别潜在的SARS-CoV-2 PLpro 抑制剂.
- 为了加速发现新的抗病毒治疗COVID-19的发现.
主要方法:
- 结合机器学习,分子动力学模拟和分子对接来选FDA批准的药物.
- 在PLpro-ligand复合体上进行了长时间分子动力学模拟.
- 利用随机森林模型训练对接分数进行预测,达到76.4%的准确性.
主要成果:
- 通过重新使用,确定了五种FDA批准的药物作为COVID-19治疗的有希望的候选药物.
- 机器学习模型在预测潜在的PLpro抑制剂方面表现出很高的准确性.
- 根据预测信心和适用性领域,成功过了候选药物.
结论:
- 整合计算建模和机器学习是有效的加速药物重定向对新兴的病毒目标,如SARS-CoV-2 PLpro.
- 这些已识别的药物代表了COVID-19治疗的潜在新疗法.
- 这种方法可以加快对未来流行病的抗病毒疗法的开发.
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