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开发SARS-CoV-2主要蛋白酶结合预测随机森林模型,用于用于COVID-19治疗的药物重用

Jie Liu¹, Liang Xu¹, Wenjing Guo¹

¹National Center for Toxicological Research, U.S. Food & Drug Administration, Jefferson, AR 72079, USA.

Experimental biology and medicine (Maywood, N.J.)

|November 24, 2023

概括

机器学习通过预测严重急性呼吸系统综合征冠状病毒2 (SARS-CoV-2) 主蛋白酶结合,确定了10种FDA批准的药物,用于重新定位为COVID-19治疗方法. 这种方法加速了针对病毒的有效治疗方法的开发.

科学领域:

计算化学和药理学计算化学和药理学
药物的发现和开发.
传染病研究传染病研究.

背景情况:

在全球范围内,COVID-19疫情造成了数百万人的死亡,需要有效的治疗方法.
准SARS-CoV-2主要蛋白酶对于开发COVID-19治疗药物至关重要.
药物重定向提供了一条更快的途径来确定潜在的治疗方法.

研究的目的:

为了预测FDA批准的药物与SARS-CoV-2主要蛋白酶的结合.
通过机器学习识别潜在的药物重定向候选人用于COVID-19治疗.
加速发现针对SARS-CoV-2的新型治疗策略.

主要方法:

来自蛋白质数据库和文献的已知SARS-CoV-2主要蛋白酶结合物的精选数据集.
开发了使用Mold2软件随机森林算法和分子描述符的预测模型.
使用五倍交叉验证验证模型性能,达到78.8%的平衡精度.

主要成果:

一个随机森林模型准确地预测了SARS-CoV-2的主要蛋白酶结合.
选了1188种FDA批准的药物,确定了10种潜在的候选人.
该模型在预测药物重定向药物向相互作用方面表现出有效性.

结论:

机器学习是加速药物重定向工作的有效工具.
确定了10种FDA批准的药物作为COVID-19治疗开发的有希望的候选药物.
这项研究强调了计算方法在打击病毒大流行中的潜力.

关键词:

这就是SARS-CoV-2病毒.具有约束力的活动活动.药物重用是为了改变药物的用途.主要蛋白质酶的主要蛋白质酶预测模型是一个预测模型.随机的森林随机的森林

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