Drug-symptom networking: Linking drug-likeness screening to drug discovery
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论文类型：期刊论文
发表时间：2016-01-01
发表刊物：PHARMACOLOGICAL RESEARCH
收录刊物：SCIE、PubMed、Scopus
卷号：103
页面范围：105-113
ISSN号：1043-6618
关键字：Drug-symptom relationship; Drug-likeness; SVDD classification; Text mining; Herbal molecule
摘要：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, a k-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 (TM) 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. (C) 2015 Elsevier Ltd. All rights reserved.
发表时间：2016-01-01