CPI-GGS: A deep learning model for predicting compound-protein interaction based on graphs and sequences

Background: Compound-protein interaction (CPI) is essential to drug discovery and design, where traditional methods are often costly and have low success rates. Recently, the integration of machine learning and deep learning in CPI research has shown potential to reduce costs and enhance discovery efficiency by improving protein target identification accuracy. Additionally, with an urgent need for novel therapies against complex diseases, CPI investigation could lead to the identification of effective new drugs. Since drug-target interactions involve complex biological processes, refined models are necessary for precise feature extraction and analysis. Nevertheless, current CPI prediction methods still face significant limitations: predictions lack sufficient accuracy, models require improved generalization ability, and further validation across diverse datasets remains essential.

Results: To address some issues at the current stage, this paper proposes a combined deep learning method, CPI-GGS, for predicting and analyzing compound-protein interactions. The source code is available on GitHub at https://github.com/xingjie321/CPI-GGS.

Conclusions: The experimental results demonstrate improved accuracy in predicting compound-protein interactions and enhance the understanding of how compounds and proteins interact, providing a valuable new tool for drug discovery and development.