用多分支扩散模型生成带属性的图，提升分子设计效率
Diffusion Twigs with Loop Guidance for Conditional Graph Generation
- 主干+分支扩散流程，分别生成图结构和属性
- 通过环路引导机制实现跨流程信息交互，生成更合理结构
- 在分子逆向设计任务中显著优于现有方法，适合药物研发场景
我们提出一种名为 Twigs 的新型基于得分的扩散框架，用于增强条件图生成任务。该框架包含一个中心主干扩散过程（如图结构）和多个附属分支过程（如图属性或标签），共同演化。引入一种新策略——环路引导，在采样过程中有效协调主干与分支之间的信息流动。该方法揭示了复杂依赖关系，解锁了新的生成能力。大量实验表明，所提方法在条件图生成任务中显著优于当前主流基线，在逆向分子设计与分子优化等挑战性任务中展现出巨大潜力。
原文摘要 · Abstract (English)
We introduce a novel score-based diffusion framework named Twigs that incorporates multiple co-evolving flows for enriching conditional generation tasks. Specifically, a central or trunk diffusion process is associated with a primary variable (e.g., graph structure), and additional offshoot or stem processes are dedicated to dependent variables (e.g., graph properties or labels). A new strategy, which we call loop guidance, effectively orchestrates the flow of information between the trunk and the stem processes during sampling. This approach allows us to uncover intricate interactions and dependencies, and unlock new generative capabilities. We provide extensive experiments to demonstrate strong performance gains of the proposed method over contemporary baselines in the context of conditional graph generation, underscoring the potential of Twigs in challenging generative tasks such as inverse molecular design and molecular optimization.
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