Revealing Protein–Protein Interactions Using a Graph Theory‐Augmented Deep Learning Approach

Understanding protein–protein interactions is crucial in the fields of biology and biotechnology, offering insights into cellular functions and highlighting therapeutic targets. Deep learning (DL) models, particularly convoluted neural networks, have revolutionized biomolecular analysis, yet their success is often tethered to enormous computational costs and reliance on specialized and time‐consuming hardware, creating a significant bottleneck for scientific discovery. More specifically, image‐based analysis of protein–protein interactions requires time‐consuming and computationally intensive quantization of complex structural patterns. In this study, we present an approach for overcoming these computational barriers by leveraging graph theory (GT) and DL approaches synergistically for efficient and accurate classification of protein interactions. Experimentally, this approach utilized multiscale structural features extracted from graphs representing polarized light microscope images of protein stains. Prediction accuracies for categorizing protein binding strengths were as high as 98.67% after feature optimization, including the generation of new augmented descriptors through simple mathematical operations, input dataset size reduction, and feature selection based on minimum redundancy maximum relevance algorithms, while also reducing the training time to only 60 s. Our results highlight the potential for augmenting DL reasoning with GT parametrization, which improves accuracy and reduces training time. These findings establish a powerful and accelerated alternative to conventional image‐based DL for analyzing protein–protein interactions. This approach not only provides a cost‐effective and accessible alternative to traditional methods, but also facilitates the accurate prediction of protein interaction strengths, even for untrained samples.