DIAM: Adaptive Drug Repositioning with Decoupled Biological Mechanism and Instance-Aware Modulation

Abstract

Drug repositioning has emerged as an attractive drug development strategy with deep learning-based computational methods showing great potential in predicting Drug-Disease Associations (DDAs). However, dominant computational paradigms typically rely on Random Negative Sampling (RNS) and static embedding fusion, leading to two fundamental limitations. First, RNS treats unobserved pairs uniformly, resulting in coarse decision boundaries that fail to distinguish true associations from ambiguous candidates. Second, static fusion applies a monolithic combination of heterogeneous features, failing to adapt to the sample-specific dominance of different biological mechanisms. To address these issues, we propose DIAM, which establishes a mechanism-adaptive paradigm by explicitly decoupling structural and molecular signals. Specifically, DIAM introduces a Dual-Stream Biological Mechanism Decoupling module to construct global structural propagation and local molecular interaction views explicitly. Leveraging these views, we design a biological plausibility score to guide the hard negative sampling, enforcing finer-grained decision boundaries. Furthermore, an Adaptive Residual Gating (ARG) is devised to perform instance-aware modulation, dynamically weighing the contribution of global and local views for each specific pair. Extensive experiments on three benchmark datasets demonstrate that DIAM outperforms seven state-of-the-art methods. A case study on Alzheimer's disease further validates the model's effectiveness in identifying potential candidate drugs for practical application.