  Co-administration of two or more drugs simultaneously can result in adverse
drug reactions. Identifying drug-drug interactions (DDIs) is necessary,
especially for drug development and for repurposing old drugs. DDI prediction
can be viewed as a matrix completion task, for which matrix factorization (MF)
appears as a suitable solution. This paper presents a novel Graph Regularized
Probabilistic Matrix Factorization (GRPMF) method, which incorporates expert
knowledge through a novel graph-based regularization strategy within an MF
framework. An efficient and sounded optimization algorithm is proposed to solve
the resulting non-convex problem in an alternating fashion. The performance of
the proposed method is evaluated through the DrugBank dataset, and comparisons
are provided against state-of-the-art techniques. The results demonstrate the
superior performance of GRPMF when compared to its counterparts.
