Collaborative Drug Design Based on A Drug-Drug Interaction-Guided Diffusion Model

Generating graph-structured molecular data involves understanding complex graph distributions. This is crucial for de novo drug molecule design, especially when incorporating drug-drug interactions (DDIs). Existing graph generative methods often struggle to capture the graphs' permutation invariance or fail to model the collaborative dependencies among various molecular components, including atom-, bond-, and textual-level DDI information. To address these limitations, we propose DDI-Diff, a knowledge-driven dual diffusion model for DDI-based drug design. DDI-Diff employs a continuous time framework and introduces a collaborative graph diffusion process, leveraging a stochastic differential equations (SDEs) system to jointly model node and edge distribution. During pretraining, we use a large-scale, unconditional dataset, followed by conditional training on DrugBank. This enhances the model's ability to generate molecular structures that align with DDI-aware knowledge, effectively capturing the collaborative effects among drugs. Then, we validated our model using DrugBank, demonstrating that DDI-Diff improves accuracy by 4.35% more than current state-of-the-art methods across all labels and highlighting its potential in collaborative drug design.