Drug-symptom networking: Linking drug-likeness screening to drug discovery.
Drug-symptom networking: Linking drug-likeness screening to drug discovery.
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药物症状网络：将药物相似性筛选与药物发现联系起来
DOI：
10.1016/j.phrs.2015.11.015
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发表时间：
2015
影响因子：
9.3
通讯作者：
Shang HongCai
中科院分区：
文献类型：
--
作者：
Xu Xue;Zhang Chao;Li PiDong;Zhang FeiLong;Gao Kuo;Chen JianXin;Shang HongCai
了解药物和症状之间的关系具有广泛的医学影响，但目前缺乏对药物-症状关联的全面描述。在这里，收集了1441种fda批准的药物，并使用PCA提取了122个描述符，解释了91%的方差。然后，采用ak-means++方法将药物数据集划分为3个聚类，构建了3个相应的SVDD模型（药物相似性筛选模型），总体准确率高达95.6%。利用上述3个SVDD模型对tcsp™数据库中的6878个中药分子进行筛选，得到5309个候选药物分子，分类接受度为77.19%。为了评估SVDD模型的准确性，从Pubmed摘要中挖掘了8559种草药分子-症状共现现象，涉及697种草药分子和314种症状。697种草药分子中的大部分都可以在公认的SVDD数据中找到（5309种分子），显示了SVDD筛选候选药物的潜力。此外，还构建了中药分子-中药分子网络和中药分子-症状。总体而言，该结果提供了一种独立于异常训练数据的药物相似性筛选新方法，并且草药分子-症状关联的全面收集为系统表征症状导向药物提供了新的数据资源。
Understanding the relationships between drugs and symptoms has broad medical consequences, yet a comprehensive description of the drug-symptom associations is currently lacking. Here, 1441 FDA-approved drugs were collected, and PCA was used to extract 122 descriptors which explained 91% of the variance. Then, ak-means++ method was employed to partition the drug dataset into 3 clusters, and 3 corresponding SVDD models (drug-likeness screening models) were constructed with an overall accuracy of up to 95.6%. Furthermore, 6878 herbal molecules from the TcmSP™ database were screened by the above 3 SVDD model to obtain 5309 candidate drug molecules with highly accept classification of 77.19%. To assess the accuracy of the SVDD models, 8559 herbal molecule-symptom co-occurrences were mined from Pubmed abstracts, involving 697 herbal molecules and 314 symptoms. Most of the 697 herbal molecules could be found in the accepted SVDD data (5309 molecules), showing the potential of the SVDD for the screening of drug candidates. Moreover, a herbal molecule-herbal molecule network and a herbal molecule-symptom were constructed. Overall, the results provided a new drug-likeness screening approach independent to abnormal training data, and the comprehensive collection of herbal molecule-symptom associations formed a new data resource for systematic characterization of the symptom-oriented medicines.