中药成分相似性量化建模及寒热药性预测分析

Similarity Measurement of Traditional Chinese Medicine Components for Cold-hot Nature Discrimination

【目的】量化中药成分的相似性,并探索建立中药寒热药性的判别模型与方法。【方法】依据"物质成分相似的中药,其药性也相似"的理论,通过紫外图谱表征中药成分。利用已有的61味中药的紫外图谱数据集,根据中药药性数据的高维、复杂性和多元性,通过距离度量学习算法学习马氏距离度量紫外图谱的相似性,结合集成学习中的多数投票算法,构建符合中医药特色的中药寒热药性预测识别模型。通过交叉验证、外推预测等方式评价模型。【结果】基于紫外图谱相似性度量的预测模型,石油醚溶剂下,交叉验证和外推预测的ROC曲线下的面积分别为0.883、0.866,交叉验证和外推预测的准确率分别为0.754、0.776,多溶剂综合分析下,交叉验证和外推预测的准确率分别为0.672、0.686。【局限】中药化学成分提取的复杂性造成本研究的数据量较小。【结论】本文构建的预测模型对石油醚溶剂下的紫外图谱数据识别效果最好;与经典模型相比较,本文模型具有更好的预测稳定性和外推性;经实验验证,预测模型可行有效。

[Objective] This paper tries to measure the similarity of traditional Chinese medicine components, and then establish a discriminant method for their cold and hot natures. [Methods] Traditional Chinese medicines with similar compositions have similar medicinal properties. Therefore, we used ultraviolet spectra to characterize their components and retrieved the UV spectrum data of 61 traditional Chinese medicines. Then, we used the Mahalanobis distance to measure the similarities of these UV spectrum data. Finally, we constructed a prediction and recognition model for cold and hot natures based on the majority voting algorithm. [Results] We evaluated the proposed model with cross validation and extrapolation techniques. With the solvent of petroleum ether, areas under the ROC curve of cross validation and extrapolated prediction were 0.883 and 0.866. Predictive accuracies of cross validation and extrapolated prediction were 0.754 and 0.776. With multi-solvent comprehensive analysis,the accuracies of cross validation and extrapolation were 0.672 and 0.686. [Limitations] The data size of our study needs to be expanded. [Conclusions] The proposed model could effectively identify ultraviolet spectrum of traditional Chinese medicine components.