Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (9): 71-79.doi: 10.11896/jsjkx.250100116

• Intelligent Medical Engineering • Previous Articles     Next Articles

Graph-based Compound-Protein Interaction Prediction with Drug Substructures and Protein 3D Information

LI Yaru1, WANG Qianqian1, CHE Chao1,2, ZHU Deheng1   

  1. 1 Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education,Dalian University,Dalian,Liaoning 116622,China
    2 College of Software Engineering,Dalian University,Dalian,Liaoning 116622,China
  • Received:2025-01-17 Revised:2025-03-30 Online:2025-09-15 Published:2025-09-11
  • About author:LI Yaru,born in 1999,postgraduate.Her main research interests include drug-target interactions and drug-drug interactions.
    ZHU Deheng,born in 1987,Ph.D.His main research interests include the study and development of robotic systems in specific scenarios,and drug repurposing.
  • Supported by:
    National Natural Science Foundation of China(62076045),the 111 Project(D23006),National Foreign Expert Project of China(D20240244) and Interdisciplinary Project of Dalian University(DLUXK-2024-QN-010,DLUXK-2024-QN-006).

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Abstract: Drugs exert therapeutic effects by interacting with proteins to inhibit or activate the functions of specific proteins.In recent years,deep learning methods have made significant progress in predicting compound-protein interactions.However,most existing studies still focus on extracting overall features from drugs and proteins,neglecting the exploration of drug target information,the three-dimensional spatial information of protein structures,and the role of drug key substructures in predicting compound-protein interactions.To address this issue,a new model is proposed,which combines the functional groups of drugs,the overall structural graphs of drugs,and the sequence and three-dimensional spatial graph information of proteins.By utilizing the fusion of graph neural networks and attention mechanisms,efficient feature learning and prediction are conducted.The experimental results on the public datasets of Human and C.elegans show that the proposed model performs excellently in CPI prediction,with an improvement of more than 1% in ACC,AUROC,and AUPR indicators,and demonstrates a stable performance advantage on imbalanced datasets.

Key words: Compound-protein interaction, Drug substructure, Protein structure prediction, Graph neural networks, Deep learning

CLC Number: 

  • TP391
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