MIND-DDI: Multi-Omics Interpretable Drug-Drug Interaction Prediction with Joint Optimization of Graph Structure, Neural Architecture, and Symbolic Rules

Multi-omics drug-drug interaction (DDI) prediction forecasts DDI outcomes from heterogeneous multi-omics data and complex structured rules, where multi-omics interpretability of hierarchical biological factor interactions is extremely crucial, which remains unexplored in literature. In this paper, we are the first to study the problem of Multi-Omics Interpretable DDI Prediction, which is highly non-trivial with three challenges: i) how to identify interaction-relevant biological entities from large-scale knowledge sources, which lay the foundation for interpretability; ii) how to capture multi-omics cross-modal connections that elucidate underlying mechanisms under diverse interaction contexts; and iii) how to explicitly enable mechanism-aware reasoning over structured biological rules. To tackle the challenges, we propose a Multi-omics Interpretable prediction framework with joint optimization of graph structure, Neural architecture, and symbolic rules to hanDle DDI prediction (MIND-DDI), which follows a novel interpretable learning paradigm that tightly couples biomedical graph sampling, hierarchical neural representation learning, and symbolic rule modeling under a unified optimization scheme. Specifically, we propose a curriculum-guided biological subgraph sampler for progressively constructing informative subgraphs, then introduce a hierarchical multi-omics neural architecture search module to capture multi-omics alignment as well as cross-modal interactions, and finally present a mechanism-aware pathway-based symbolic reasoner for multi-hop reasoning over a unified neural-symbolic space. Extensive experiments show the superiority of MIND-DDI in both predictive accuracy and interpretability.