# Advances in protein-protein interaction prediction: a deep learning perspective

## TL;DR

This paper reviews how deep learning methods are improving the prediction of protein interactions, which are crucial for understanding cellular processes and diseases.

## Contribution

The paper provides a comprehensive survey of deep learning models for PPI prediction, highlighting their strengths, limitations, and future directions.

## Key findings

- Deep learning models like CNNs, RNNs, and GCNs have shown promise in capturing complex PPI features.
- Current models face challenges in generalization and integration with biological knowledge.
- Benchmark datasets and evaluation strategies are critical for comparing model performance.

## Abstract

Protein–protein interactions (PPIs) are vital for regulating various cellular functions and understanding how diseases are developed. The traditional ways to identify the PPIs are costly and time-consuming. In recent years, the disruptive advances in deep learning (DL) have transformed computational PPI prediction by enabling automatic feature extraction from protein sequences and structures. This survey presents a comprehensive analysis of DL-based models developed for PPI prediction, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), deep neural networks (DNNs), graph convolutional networks (GCNs), and ensemble architectures. The review compares their feature representations, learning strategies, and evaluation benchmarks, emphasizing their strengths and limitations in capturing complex dependencies and structural relationships. In addition, the paper elaborates on different benchmarks and biological databases that are commonly used in different experiments for performance comparison. Finally, we outline open challenges and future research directions to enhance model generalization, interpretability, and integration with biological knowledge for reliable PPI prediction.