  Background: In silico drug-target interaction (DTI) prediction plays an
integral role in drug repositioning: the discovery of new uses for existing
drugs. One popular method of drug repositioning is network-based DTI
prediction, which uses complex network theory to predict DTIs from a
drug-target network. Currently, most network-based DTI prediction is based on
machine learning methods such as Restricted Boltzmann Machines (RBM) or Support
Vector Machines (SVM). These methods require additional information about the
characteristics of drugs, targets and DTIs, such as chemical structure, genome
sequence, binding types, causes of interactions, etc., and do not perform
satisfactorily when such information is unavailable. We propose a new,
alternative method for DTI prediction that makes use of only network topology
information attempting to solve this problem.
  Results: We compare our method for DTI prediction against the well-known RBM
approach. We show that when applied to the MATADOR database, our approach based
on node neighborhoods yield higher precision for high-ranking predictions than
RBM when no information regarding DTI types is available.
  Conclusion: This demonstrates that approaches purely based on network
topology provide a more suitable approach to DTI prediction in the many
real-life situations where little or no prior knowledge is available about the
characteristics of drugs, targets, or their interactions.
