Graph Regularized Probabilistic Matrix Factorization for Drug-Drug Interactions Prediction

• Published in: bioRxiv
• Main Authors: Jain, Stuti, Chouzenoux, Emilie, Kumar, Kriti, Majumdar, Angshul
• Format: Paper
• Language: English
• Published: Cold Spring Harbor Cold Spring Harbor Laboratory Press 21.10.2022
• Subjects: Drug development
• DOI: 10.1101/2022.10.18.512676

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. Competing Interest Statement The authors have declared no competing interest.