title: Drug-symptom networking: Linking drug-likeness screening to drug discovery.
abstract: 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™ 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.
authors.affiliation: Key Laboratory of Chinese Internal Medicine of Ministry of Education and Beijing, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China; Beijing University of Chinese Medicine, Beijing 100029, China.
authors.forename: Xue
authors.initials: X
authors.lastname: Xu
authors.affiliation: School of Mathematical Sciences, Dalian University of Technology, Dalian, Liaoning 116023, China.
authors.forename: Chao
authors.initials: C
authors.lastname: Zhang
authors.affiliation: College of Life Science, Northwest A&F University, Yangling, Shannxi 712100, China.
authors.forename: PiDong
authors.initials: P
authors.lastname: Li
authors.affiliation: College of Life Science, Northwest A&F University, Yangling, Shannxi 712100, China.
authors.forename: FeiLong
authors.initials: F
authors.lastname: Zhang
authors.affiliation: Beijing University of Chinese Medicine, Beijing 100029, China.
authors.forename: Kuo
authors.initials: K
authors.lastname: Gao
authors.affiliation: Beijing University of Chinese Medicine, Beijing 100029, China. Electronic address: cjx@bucm.edu.cn.
authors.forename: JianXin
authors.initials: J
authors.lastname: Chen
authors.affiliation: Key Laboratory of Chinese Internal Medicine of Ministry of Education and Beijing, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China. Electronic address: shanghongcai@foxmail.com.
authors.forename: HongCai
authors.initials: H
authors.lastname: Shang
mesh_terms: D055808:Drug Discovery; D004365:Drugs, Chinese Herbal; D006801:Humans; D008962:Models, Theoretical; D025341:Principal Component Analysis
chemical_list: D004365:Drugs, Chinese Herbal
keywords: Drug-likeness; Drug–symptom relationship; Herbal molecule; SVDD classification; Text mining