# A comprehensive review of cluster methods for drug–drug interaction network

## TL;DR

This review summarizes clustering methods for analyzing drug-drug interactions, highlighting their advantages in predicting interactions and understanding drug mechanisms.

## Contribution

The paper provides a comprehensive review of unsupervised clustering methods for DDI network analysis, which is a novel focus compared to previous reviews on supervised learning.

## Key findings

- Clustering methods offer unique advantages in DDI prediction and uncovering drug mechanisms.
- The paper introduces drug information-based and network-based clustering algorithms for DDI analysis.
- Limitations and future research directions for clustering methods in DDI are discussed.

## Abstract

The detection of drug–drug interaction (DDI) is crucial to the rational use of drug combinations. Experimentally, DDI detection is time‐consuming and laborious. Currently, researchers have developed a variety of computational methods to predict DDI. Although there are many reviews that summarized these computational methods, these reviews focused on supervised learning. In this review, we provide a comprehensive and systematic summary of unsupervised (i.e., clustering) methods for DDI network analysis. Unlike previous studies, we highlight the unique advantages of clustering methods DDI prediction and uncovering mechanisms of action. We first introduced common drug information and discussed how to calculate drug similarity using this drug information. Then, we introduced representative clustering algorithms (i.e., drug information‐based and network‐based methods) and described clustering evaluation metrics. Finally, we discussed the limitations and challenges in this field, and proposed potential research directions. This review aims to promote further exploration and application of clustering methods in drug combination discovery and DDI network analysis.