用流匹配与最优传输提升分子图生成质量与稳定性
Improving Molecular Graph Generation with Flow Matching and Optimal Transport
- 基于流匹配与最优传输构建离散生成模型
- 在无条件与有条件生成任务中均超越现有基线
- 支持属性导向生成，适合药物分子设计场景
分子图生成在药物设计中至关重要，但因节点与边间复杂依赖关系而极具挑战。尽管扩散模型展现出潜力，却常面临训练不稳定和采样效率低的问题。为此，我们提出GGFlow，一种结合最优传输的离散流匹配生成模型，并引入边增强图变换器，实现化学键间的直接通信。此外，GGFlow设计了一种新型目标导向生成框架，可控制生成轨迹，以设计具备期望性质的新分子结构。实验表明，该模型在无条件与条件分子生成任务上均表现优异，显著优于现有基线，验证了其有效性与广泛应用潜力。
原文摘要 · Abstract (English)
Generating molecular graphs is crucial in drug design and discovery but remains challenging due to the complex interdependencies between nodes and edges. While diffusion models have demonstrated their potentiality in molecular graph design, they often suffer from unstable training and inefficient sampling. To enhance generation performance and training stability, we propose GGFlow, a discrete flow matching generative model incorporating optimal transport for molecular graphs and it incorporates an edge-augmented graph transformer to enable the direct communications among chemical bounds. Additionally, GGFlow introduces a novel goal-guided generation framework to control the generative trajectory of our model, aiming to design novel molecular structures with the desired properties. GGFlow demonstrates superior performance on both unconditional and conditional molecule generation tasks, outperforming existing baselines and underscoring its effectiveness and potential for wider application.
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