通过机器学习方法从Unani配方中识别潜在的天然抗生素
Ahmad Kamal Nasution1, Muhammad Alqaaf1, Rumman Mahfujul Islam1
1Computational Systems Biology Lab, Graduate School of Science and Technology, Nara Institute of Science and Technology, Nara 630-0101, Japan.
Antibiotics (Basel, Switzerland)
|October 25, 2024
概括
乌纳尼草药成分显示出作为天然抗生素的潜力. 机器学习确定了20种对抗抗生素耐药性和超级细菌有效的关键代谢物.
科学领域:
- 综合医学是一个整体的医学.
- 计算生物学 计算生物学
- 药理学 药理学是指药理学的学科.
背景情况:
- 希腊起源的医疗系统Unani Tibb在南亚和中亚普遍存在.
- 在初级医疗保健中使用来自植物的Unani草药.
- 越来越多的抗生素耐药性和超级细菌需要新的治疗战略.
研究的目的:
- 调查Unani草药成分的潜在天然抗生素特性.
- 解决抗生素耐药性,多药耐药性和超级细菌的出现问题.
- 使用机器学习识别Unani化合物的分子水平影响.
主要方法:
- 采用了12个机器学习算法,包括决策树,内核,神经网络和基于概率的方法.
- 使用的数据预处理技术:合成少数过量采样技术 (SMOTE),特征选择和主要组件分析 (PCA).
- 通过超参数的网格搜索调整优化机器学习模型.
主要成果:
- 采用SMOTE预处理的多层感知器 (MLP) 模型实现了高精度,精度和回忆.
- 确定了20种具有预测自然抗生素活性的关键代谢物.
- 通过文献审查和结构相似性分析与已知的抗生素进行验证的预测.
结论:
- 乌纳尼草药成分显示出作为新型天然抗生素的前景.
- 机器学习有效地预测了自然化合物的抗生素潜力.
- 这项研究提供了一种数据驱动的方法,用于发现针对耐药病原体的新抗菌剂.
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