Background#R##N#Predicting novel drug–target associations is important not only for developing new drugs, but also for furthering biological knowledge by understanding how drugs work and their modes of action. As more data about drugs, targets, and their interactions becomes available, computational approaches have become an indispensible part of drug target association discovery. In this paper we apply random walk with restart (RWR) method to a heterogeneous network of drugs and targets compiled from DrugBank database and investigate the performance of the method under parameter variation and choice of chemical fingerprint methods.
['Abhik Seal', 'Yong‑Yeol Ahn', 'David J. Wild']
Optimizing drug–target interaction prediction based on random walk on heterogeneous networks