IIB-DDI: Invariant Information Bottleneck Theory for Out-of-Distribution Drug-Drug Interaction Prediction

Rapid and accurate identification of drug-drug interactions (DDIs) among multiple medications is crucial for various medical treatments, and clinical therapies. Currently, the increasing significance of molecular substructure interactions in DDI prediction has become a consensus. However, due to the uneven distribution of substructures in molecules and most methods do not consider the differences in substructure distributions, models may rely on spurious substructure relationships, thereby weakening their out-of-distribution (OOD) generalization ability. To address this challenge, we propose an OOD-DDI framework called IIB-DDI. Specifically, information bottleneck theory is initially utilized to extract core subgraphs of drug pairs. Then, considering the diversity and unknown nature of environments, we introduce the vector quantization to design an environment codebook, where the potential environments in the dataset are clustered into a specified number of categories. Subsequently, we position the extracted core subgraphs under various latent environmental factors to attain invariant core substructures (rationales). Additionally, the learned environmental distribution also could be acted as noise injection for optimizing mutual information, achieving a smoother and more stable training curve, thereby leading to lower loss. Extensive experiments conducted on real-world DDI datasets demonstrate the superiority of our model over state-of-the-art baselines.