# Graph Regularized Probabilistic Matrix Factorization for Drug-Drug Interactions Prediction

arXiv: https://arxiv.org/abs/2210.10784 · HTML: https://mlsanity.com/paper/2210.10784/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 52 references that are in the map.
- First posted on arXiv 19 Oct 2022.

## Builds on (most cited first, 4 of 4)

- [Drug-Drug Interaction Prediction Based on Knowledge Graph Embeddings and Convolutional-LSTM Network](https://mlsanity.com/paper/1908.01288/) (2019)
- [Scalable Probabilistic Matrix Factorization with Graph-Based Priors](https://mlsanity.com/paper/1908.09393/) (2019)
- [AttentionDDI: Siamese Attention-based Deep Learning method for drug-drug interaction predictions](https://mlsanity.com/paper/2012.13248/) (2020)
- [Detecting drug-drug interactions using artificial neural networks and classic graph similarity measures](https://mlsanity.com/paper/1903.04571/) (2019)

## Abstract (arXiv)

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.