Drug–target interaction prediction through fine-grained selection and bidirectional random walk methodology

• Published in: Scientific reports Vol. 14; no. 1; pp. 18104 - 14
• Main Authors: Wang, YaPing, Yin, ZhiXiang
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 05.08.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114; 631/154; Algorithms; Disease; Drug development; Drug Development - methods; Drugs; Drug–target interaction prediction; Embedding; Graph representations; Heterogeneous network; Humanities and Social Sciences; Humans; Information processing; multidisciplinary; Neural networks; Pharmaceutical Preparations; Predictions; R&D; Random walk; Research & development; Science; Science (multidisciplinary); Similarity integration; Therapeutic targets; Similarity integration; Heterogeneous network; Random walk; Drug–target interaction prediction
• DOI: 10.1038/s41598-024-69186-w
• ISSN: 2045-2322

The study of drug–target interaction plays an important role in the process of drug development. The subject of DTI forecasting has advanced significantly in the last several years, yielding numerous significant research findings and methodologies. Heterogeneous data sources provide richer information and comprehensive perspectives for drug–target interaction prediction, so many existing methods rely on heterogeneous networks, and graph embedding technology becomes an important technology to extract information from heterogeneous networks. These approaches, however, are less concerned with potential noisy information in heterogeneous networks and more focused on the extent of information extraction in those networks. Based on this, a potential DTI predictive network model called FBRWPC is proposed in this paper. It uses a fine-grained similarity selection program to first integrate similarity on similar networks and then a bidirectional random walk graph embedding learning method with restart to obtain an updated drug target interaction matrix. Through the use of similarity selection and fine-grained selection similarity integration, the framework can effectively filter out the noise present in heterogeneous networks and enhance the model's prediction performance. The experimental findings demonstrate that, even after being split up into four distinct types of data sets, FBRWPC can still retain great prediction performance, a sign of the model's resilience and good generalization.