使用机器学习方法预测小分子对肠道微生物群的影响,并与最佳分子特征集成
Binyou Wang1,2, Jianmin Guo1, Xiaofeng Liu1
1School of Basic Medical Sciences, Southwest Medical University, Luzhou, 646000, China.
BMC bioinformatics
|September 11, 2023
概括
这项研究开发了一种机器学习模型,用于识别对人类肠道微生物组 (HGM) 有害的药物. 该模型准确地预测了抗共生化合物,有助于早期药物发现和安全性评估.
科学领域:
- 微生物学 微生物学
- 药理学 药理学是指药理学的学科.
- 计算化学计算化学
背景情况:
- 人体肠道微生物组 (HGM) 对健康至关重要,但药物使用可能会破坏它.
- 在药物发现的早期确定具有抗相继作用的药物至关重要.
研究的目的:
- 开发一种新的机器学习分类模型,用于预测对HGM的反共生效应.
- 为了确定最佳的分子特征,准确预测药物诱导的HGM破坏.
主要方法:
- 探索了六个分子指纹和三个描述符的组合,以确定最佳的分子特征.
- 开发了一个共识机器学习模型,利用这些最佳功能.
主要成果:
- 最终的模型在五倍交叉验证中获得了0.725的F1得分,82.9%的准确性和0.791的AUC.
- 该模型在使用相同算法之前的研究中表现出色.
- 确定了七个结构性警报,负责化学抗相交效应,为药物设计提供了洞察力.
结论:
- 开发的模型可以作为一种有希望的工具,用于在早期药物发现中选抗共生化合物.
- 该模型有助于评估药物对HGM的潜在体内风险.
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