Graph Regularized Probabilistic Matrix Factorization for Drug-Drug Interactions Prediction
This addresses the need for identifying drug-drug interactions to prevent adverse reactions in drug development and repurposing, representing an incremental improvement with a novel regularization strategy.
The paper tackled the problem of predicting adverse drug-drug interactions by proposing a Graph Regularized Probabilistic Matrix Factorization method, which showed superior performance compared to state-of-the-art techniques on the DrugBank dataset.
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.