TriCloud: Drug-Target-Disease Ternary Network for Drug Repositioning Research Based on Point Cloud Modeling

In recent years, the "drug-target-disease" association has become increasingly complex and data-scarce. Existing methods are limited by information loss caused by explicit graph construction when modeling triplet relationships, making it difficult to effectively capture geometric structures and long-range dependencies, thereby affecting prediction performance. To address this, this paper proposes a new method based on point cloud modeling-TriCloud. This method represents each triad as a spatial point cloud, encoding semantic and topological relationships through geometric coordinates, and performs feature learning directly on an unordered point set, avoiding reliance on predefined graph structures. Based on the PointNet architecture, it introduces a multi-view feature extraction and fusion mechanism to enhance the modeling capability of global structures and complex spatial patterns. Experimental results show that TriCloud significantly outperforms existing methods on multiple benchmark datasets, achieving an AUC of 0.9995 and an AUPR of 0.9996, with all metrics ranking first. External validation demonstrates its excellent generalization ability. Feature analysis reveals that the geometric-semantic joint features of positive samples play a dominant role in classification. This study provides an efficient and reliable computational framework for drug repurposing, contributing to the development of precision medicine.