<p>Conventional approaches to drug discovery often require considerable time and effort. The promising solution is to repurpose existing drugs by identifying new therapeutic roles, thereby enhancing development efficiency. Drug repositioning based on computational methods is gaining widespread attention. However, most computational methods primarily rely on similarity-based data to extract features of associations, but lack the mining of topological structural features in the association network, while ignoring valuable original biological and chemical information. Therefore, this article develops a drug repositioning approach via meta-path integration of multi-source biological information (MPMB-DR). This approach combines meta-path and biomolecular similarity information to construct high-quality negative links within heterogeneous networks. It considers both the topological structure of the association network and the relationships among biomolecules. Based on the negative sample strategy, potential drug–disease associations are predicted by leveraging the synergy between meta-paths and multi-source biological data. Experimental results and case studies demonstrate that the MPMB-DR method has significant advantages in identifying associations between potential drugs and diseases.</p> Graphical Abstract <p></p>