通过 node2vec 扩大草药化学品的潜在目标,基于草药相互作用
Dai-Yan Zhang1, Wen-Qing Cui1, Ling Hou1
1State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, 999078, Macao, China.
这项研究使用node2vec来预测草药相互作用,识别传统医学 (TM) 的新目标,并帮助药物开发. 它成功预测了32种草药化学标,并对几个关键相互作用进行了实验验证.
科学领域:
- 计算化学和药理学计算化学和药理学
- 生物信息学和网络分析
- 传统医学的研究.
背景情况:
- 鉴定化学标相互作用对于药物开发至关重要,但由于复杂的机制和低含量化合物,对传统医学 (TM) 具有挑战性.
- 传统医学的复杂性阻碍了对相互作用的实验性识别,尤其是低度化学物质.
研究的目的:
- 应用 node2vec 算法来预测药物-草药相互作用,并识别潜在的治疗点.
- 为了利用分子对接和实验验证,确认预测的草药向相互作用.
- 制定一个框架,以了解传统医学的机制,并评估临床风险.
主要方法:
- 使用心血管药物和草药 (Salvia miltiorrhiza和Ligusticum chuanxiong) 构建了一个数据集,其中包括化学目标,化学化学和蛋白质蛋白质相互作用数据.
- 将 node2vec 算法与其他四种用于药物草药相互作用中链接预测的算法进行了比较.
- 用分子对接和药理学实验来验证预测的相互作用并确定特定的分子效应.
主要成果:
- 在预测跨组合数据集的交互方面,Node2vec获得了最高的性能 (平均AUROC和AP为0.91).
- 该研究确定了32种草药化学品的点,预测了43种潜在的草药点相互作用.
- 分子对接证实了具有有利结合亲和力的11种相互作用,药理实验验验证了咖啡酸,ligustilide和neocryptotanshinone等化合物的特定分子作用.
结论:
- 开发的分析框架有效地帮助发现与传统医学相关的草药相互作用和潜在的临床风险.
- 该研究为研究人员研究传统医学的复杂机制并确定新的治疗点提供了宝贵的参考资料.
- 实验验证证证实了node2vec方法在发现传统医学背景下的特定分子相互作用方面的预测能力.
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