Graph Regularized Probabilistic Matrix Factorization for Drug-Drug Interactions Prediction

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.

Automated summary

Simultaneous co-administration of multiple drugs may lead to adverse drug reactions. Identifying drug-drug interactions (DDIs) is crucial for drug development and repurposing of old drugs. This paper introduces a novel Graph Regularized Probabilistic Matrix Factorization (GRPMF) method that incorporates expert knowledge through graph-based regularization within a matrix factorization framework. An efficient optimization algorithm is proposed to solve the resulting non-convex problem. Evaluation using the DrugBank dataset demonstrates the superior performance of GRPMF compared to other techniques.