Meta-Path-based Probabilistic Soft Logic for Drug-Target Interaction
  Prediction
Shengming Zhang Yizhou Sun
Drug-target interaction (DTI) prediction, which aims at predicting whether a
drug will be bounded to a target, have received wide attention recently, with
the goal to automate and accelerate the costly process of drug design. Most of
the recently proposed methods use single drug-drug similarity and target-target
similarity information for DTI prediction, which are unable to take advantage
of the abundant information regarding various types of similarities between
them. Very recently, some methods are proposed to leverage multi-similarity
information, however, they still lack the ability to take into consideration
the rich topological information of all sorts of knowledge bases where the
drugs and targets reside in. More importantly, the time consumption of these
approaches is very high, which prevents the usage of large-scale network
information. We thus propose a network-based drug-target interaction prediction
approach, which applies probabilistic soft logic (PSL) to meta-paths on a
heterogeneous network that contains multiple sources of information, including
drug-drug similarities, target-target similarities, drug-target interactions,
and other potential information. Our approach is based on the PSL graphical
model and uses meta-path counts instead of path instances to reduce the number
of rule instances of PSL. We compare our model against five methods, on three
open-source datasets. The experimental results show that our approach
outperforms all the five baselines in terms of AUPR score and AUC score.