Prediction of Polypharmacological Profiles of Drugs by the Integration of Chemical, Side Effect, and Therapeutic Space

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.