title: Prediction of polypharmacological profiles of drugs by the integration of chemical, side effect, and therapeutic space.
abstract: Prediction of polypharmacological profiles of drugs enables us to investigate drug side effects and further find their new indications, i.e. drug repositioning, which could reduce the costs while increase the productivity of drug discovery. Here we describe a new computational framework to predict polypharmacological profiles of drugs by the integration of chemical, side effect, and therapeutic space. On the basis of our previous developed drug side effects database, named MetaADEDB, a drug side effect similarity inference (DSESI) method was developed for drug-target interaction (DTI) prediction on a known DTI network connecting 621 approved drugs and 893 target proteins. The area under the receiver operating characteristic curve was 0.882 ± 0.011 averaged from 100 simulated tests of 10-fold cross-validation for the DSESI method, which is comparative with drug structural similarity inference and drug therapeutic similarity inference methods. Seven new predicted candidate target proteins for seven approved drugs were confirmed by published experiments, with the successful hit rate more than 15.9%. Moreover, network visualization of drug-target interactions and off-target side effect associations provide new mechanism-of-action of three approved antipsychotic drugs in a case study. The results indicated that the proposed methods could be helpful for prediction of polypharmacological profiles of drugs.
authors.affiliation: Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China.
authors.forename: Feixiong
authors.initials: F
authors.lastname: Cheng
authors.forename: Weihua
authors.initials: W
authors.lastname: Li
authors.forename: Zengrui
authors.initials: Z
authors.lastname: Wu
authors.forename: Xichuan
authors.initials: X
authors.lastname: Wang
authors.forename: Chen
authors.initials: C
authors.lastname: Zhang
authors.forename: Jie
authors.initials: J
authors.lastname: Li
authors.forename: Guixia
authors.initials: G
authors.lastname: Liu
authors.forename: Yun
authors.initials: Y
authors.lastname: Tang
mesh_terms: D000465:Algorithms; D014150:Antipsychotic Agents / Q000009:adverse effects / Q000737:chemistry*; D019540:Area Under Curve; D003198:Computer Simulation; D057225:Data Mining; D062313:Databases, Pharmaceutical; D058492:Drug Repositioning / Q000706:statistics & numerical data*; D006801:Humans; D008024:Ligands; D015233:Models, Statistical; D011237:Predictive Value of Tests; D055553:Prescription Drugs / Q000009:adverse effects / Q000737:chemistry*; D011618:Psychotic Disorders / Q000188:drug therapy; D021281:Quantitative Structure-Activity Relationship; D012372:ROC Curve
chemical_list: D014150:Antipsychotic Agents; D008024:Ligands; D055553:Prescription Drugs