CPGL: Prediction of Compound-Protein Interaction by Integrating Graph Attention Network With Long Short-Term Memory Neural Network

• Published in: IEEE/ACM transactions on computational biology and bioinformatics 2023-05, Vol.20 (3), p.1-8
• Main Authors: Zhao, Minghua, Yuan, Min, Yang, Yaning, Xu, Steven X
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial intelligence; compound-protein interaction; Datasets; Deep learning; Feature extraction; G protein-coupled receptors; graph attention; Kinases; Long short-term memory; LSTM; Machine learning; Neural networks; Performance prediction; Proteins; Robustness; sequence analysis
• ISSN: 1545-5963; 1557-9964
• DOI: 10.1109/TCBB.2022.3225296

Recent advancements of artificial intelligence based on deep learning algorithms have made it possible to computationally predict compound-protein interaction (CPI) without conducting laboratory experiments. In this manuscript, we integrated a graph attention network (GAT) for compounds and a long short-term memory neural network (LSTM) for proteins, used end-to-end representation learning for both compounds and proteins, and proposed a deep learning algorithm, CPGL (CPI with GAT and LSTM) to optimize the feature extraction from compounds and proteins and to improve the model robustness and generalizability. CPGL demonstrated an excellent predictive performance and outperforms recently reported deep learning models. Based on 3 public CPI datasets, C.elegans, Human and BindingDB, CPGL represented 1 - 5% improvement compared to existing deep-learning models. Our method also achieves excellent results on datasets with imbalanced positive and negative proportions constructed based on the C.elegans and Human datasets. More importantly, using 2 label reversal datasets, GPCR and Kinase, CPGL showed superior performance compared to other existing deep learning models. The AUC were substantially improved by 20% on the Kinase dataset, indicative of the robustness and generalizability of CPGL.