AIM-DDI: A Model-Agnostic Multimodal Integration Module for Drug-Drug Interaction Prediction

• URL: http://arxiv.org/abs/2605.14327v1
• Date: Thu, 14 May 2026 03:42:22 GMT
• Title: AIM-DDI: A Model-Agnostic Multimodal Integration Module for Drug-Drug Interaction Prediction
• Abstract summary: AIM-DDI is an architecture-independent multimodal integration module that represents heterogeneous modality information as tokens in a shared latent space.<n>By modeling dependencies across modality tokens through a unified fusion module, AIM-DDI enables model-agnostic integration of structural, chemical, and semantic drug signals.
• Score: 3.731289189298451
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Drug-drug interaction (DDI) prediction is a critical task in computational biomedicine, as adverse interactions between co-administered drugs can cause severe side effects and clinical risks. A key challenge is unseen-drug generalization, where interactions must be predicted for drugs not observed during training. Although multimodal DDI models exploit diverse drug-related information, their fusion mechanisms are often tied to specific prediction architectures, limiting their reuse across models. To address this, we propose AIM-DDI, an architecture-independent multimodal integration module that represents heterogeneous modality information as tokens in a shared latent space. By modeling dependencies across modality tokens through a unified fusion module, AIM-DDI enables model-agnostic integration of structural, chemical, and semantic drug signals across different DDI prediction architectures. Extensive evaluations across diverse DDI models and DrugBank-based settings show that AIM-DDI consistently improves prediction performance, with the strongest gains under the most challenging both-unseen setting where neither drug in a test pair is observed during training. These results suggest that treating multimodal integration as a reusable module, rather than a model-specific fusion component, is an effective strategy for robust unseen-drug DDI prediction.

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