基于机器学习的网络特征和化合物的化学结构的集成用于SARS-CoV-2药物效应分析
Julian Späth1,2, Rui-Sheng Wang1, Maeve Humphrey1
1Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
CPT: pharmacometrics & systems pharmacology
|November 11, 2023
概括
这项研究开发了一种机器学习框架,用于预测SARS-CoV-2 (严重急性呼吸系统综合征冠状病毒2) 感染的药物效应. 该模型整合了网络和物理化学特征,以确定有效的治疗方法并降低药物开发成本.
科学领域:
- 计算生物学是一种计算生物学.
- 药理学 药理学是指药理学的学科.
- 机器学习 机器学习
背景情况:
- 高昂的药物开发成本需要创新的预测方法.
- 严重急性呼吸道综合征冠状病毒2 (SARS-CoV-2) 感染缺乏有效的治疗方法,导致需要药物重新定位和效果预测.
- 现有的COVID-19预防措施是不够的,强调了需要改进的治疗策略.
研究的目的:
- 开发一种机器学习框架,用于预测针对SARS-CoV-2的药物疗效和细胞毒性.
- 分析目标网络特征和化合物物理化学特性对药物效应的影响.
- 为新药发现提供对药物疗效决定因素的见解.
主要方法:
- 集成目标网络特征和化合物的物理化学特性.
- 在已知对SARS-CoV-2有实验性影响的化合物上训练的随机森林模型的开发.
- 在特征重要性分析中应用沙普利值,以了解药物疗效和细胞毒性决定因素.
主要成果:
- 机器学习框架在一个看不见的验证集上实现了0.73的接收器操作特征曲线 (ROC-AUC) 下的平均面积.
- 特性重要性分析提供了对药物疗效和细胞毒性的关键决定因素的见解.
- 该模型成功地整合了各种功能,用于预测药物对SARS-CoV-2感染的影响.
结论:
- 开发的基于系统药理学的机器学习框架可以对SARS-CoV-2感染分类现有药物.
- 这种方法可以加速药物发现并降低成本.
- 该框架可适应用于预测未来病毒爆发中的药物效应.
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