BiBLDR: Bidirectional Behavior Learning for Drug Repositioning.

Abstract

Many deep learning methods represented by graph-based approaches achieve significant progress in drug repositioning. However, these graph-based methods face a critical limitation: they often fail in cold-start scenarios because the graph structure relies heavily on known association information from both the drug and disease sides. To address this challenge, we propose a bidirectional behavior learning strategy for drug repositioning, BiBLDR, an innovative framework that reformulates drug repositioning as a behavior sequence learning task. First, we construct bidirectional behavioral sequences based on drug and disease sides. Bidirectional behavior sequences ensure sufficient information for model learning in both drug and disease cold-start scenarios, while providing more precise feature representations for association prediction tasks. Subsequently, we propose a two-stage strategy for drug repositioning. In the first stage, we construct prototype spaces to characterise the representational attributes of drugs and diseases. In the second stage, these refined prototypes and bidirectional behavior sequence data are leveraged to predict potential drug-disease associations. This design allows BiBLDR to more robustly capture hidden pharmacological relationships from bidirectional behavioral sequences, delivering significant benefits in cold-start scenarios. Extensive experiments demonstrate that our method achieves state-of-the-art performance on benchmark datasets. Meanwhile, BiBLDR demonstrates significantly superior performance compared to previous methods in cold-start scenarios.