Ung Choong Yong, Li Hu, Cao Zhi Wei, Li Yi Xue, Chen Yu Zong
Bioinformatics and Drug Design Group, Department of Pharmacy, National University of Singapore, Blk S16, Level 8, 3 Science Drive 2, Singapore 117543, Singapore.
J Ethnopharmacol. 2007 May 4;111(2):371-7. doi: 10.1016/j.jep.2006.11.037. Epub 2006 Dec 16.
Multi-herb prescriptions of traditional Chinese medicine (TCM) often include special herb-pairs for mutual enhancement, assistance, and restraint. These TCM herb-pairs have been assembled and interpreted based on traditionally defined herbal properties (TCM-HPs) without knowledge of mechanism of their assumed synergy. While these mechanisms are yet to be determined, properties of TCM herb-pairs can be investigated to determine if they exhibit features consistent with their claimed unique synergistic combinations. We analyzed distribution patterns of TCM-HPs of TCM herb-pairs to detect signs indicative of possible synergy and used artificial intelligence (AI) methods to examine whether combination of their TCM-HPs are distinguishable from those of non-TCM herb-pairs assembled by random combinations and by modification of known TCM herb-pairs. Patterns of the majority of 394 known TCM herb-pairs were found to exhibit signs of herb-pair correlation. Three AI systems, trained and tested by using 394 TCM herb-pairs and 2470 non-TCM herb-pairs, correctly classified 72.1-87.9% of TCM herb-pairs and 91.6-97.6% of the non-TCM herb-pairs. The best AI system predicted 96.3% of the 27 known non-TCM herb-pairs and 99.7% of the other 1,065,100 possible herb-pairs as non-TCM herb-pairs. Our studies suggest that TCM-HPs of known TCM herb-pairs contain features distinguishable from those of non-TCM herb-pairs consistent with their claimed synergistic or modulating combinations.
中药复方通常包含特殊的药对,用于协同增效、辅助和制约。这些中药药对是根据传统定义的中药药性组合并阐释的,但其假定协同作用的机制尚不清楚。虽然这些机制有待确定,但可以对中药药对的药性进行研究,以确定它们是否具有与其所宣称的独特协同组合相一致的特征。我们分析了中药药对的中药药性分布模式,以检测可能存在协同作用的迹象,并使用人工智能(AI)方法来检验其中药药性组合是否与通过随机组合和对已知中药药对进行修改而组装的非中药药对有所区别。结果发现,394种已知中药药对中的大多数呈现出药对相关性迹象。通过使用394种中药药对和2470种非中药药对进行训练和测试的三个AI系统,正确分类了72.1%-87.9%的中药药对和91.6%-97.6%的非中药药对。最佳的AI系统将27种已知非中药药对中的96.3%以及其他1,065,100种可能药对中的99.7%预测为非中药药对。我们的研究表明,已知中药药对的中药药性具有与非中药药对不同的特征,这与其所宣称的协同或调节组合相一致。