Recent advances in deep learning for protein-protein interaction: a review

Abstract

Deep learning, a cornerstone of artificial intelligence, is driving rapid advancements in computational biology. Protein-protein interactions (PPIs) are fundamental regulators of biological functions. With the inclusion of deep learning in PPI research, the field is undergoing transformative changes. Therefore, there is an urgent need for a comprehensive review and assessment of recent developments to improve analytical methods and open up a wider range of biomedical applications. This review meticulously assesses deep learning progress in PPI prediction from 2021 to 2025. We evaluate core architectures (GNNs, CNNs, RNNs) and pioneering approaches-attention-driven Transformers, multi-task frameworks, multimodal integration of sequence and structural data, transfer learning via BERT and ESM, and autoencoders for interaction characterization. Moreover, we examined enhanced algorithms for dealing with data imbalances, variations, and high-dimensional feature sparsity, as well as industry challenges (including shifting protein interactions, interactions with non-model organisms, and rare or unannotated protein interactions), and offered perspectives on the future of the field. In summary, this review systematically summarizes the latest advances and existing challenges in deep learning in the field of protein interaction analysis, providing a valuable reference for researchers in the fields of computational biology and deep learning.

Keywords: Artificial intelligence; Artificial neural networks; Computational biology; Deep learning; Machine learning; PPI prediction; Protein-protein interactions.

Fig. 1

Examples of three traditional artificial neural networks. (A) Graph Convolutional Network. (B) Graph autoencoder. (C) Convolutional Neural Network. (D) Recurrent Neural Network

Fig. 2

The transformer framework is comprised of two principal components. The encoder processes an input sequence to produce an internal representation using self-attention and feed-forward mechanisms. These elements enable the model to emphasise the pertinent components. The feed-forward layer transforms each input token independently in order to capture complex patterns. The encoder produces an encoded representation, which is then transmitted to the decoder. The decoder employs self-attention mechanisms to refine the output

Fig. 3

Overview of multi-task learning and multimodal learning (A) Multi-task learning mechanism (B) Interdisciplinary multimodal expansion applications

Fig. 4

Protein-protein interactions characterization learning

Fig. 5

The mechanism of autoencoders