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.

翻译：分子图生成在药物设计与发现中至关重要，但由于节点与边之间复杂的相互依赖关系，该任务仍具挑战性。尽管扩散模型已在分子图设计中展现出潜力，但其常面临训练不稳定与采样效率低的问题。为提升生成性能与训练稳定性，我们提出了GGFlow，一种融合最优传输的离散流匹配生成模型，用于分子图生成，并引入边增强图Transformer以实现化学键间的直接信息交互。此外，GGFlow提出了一种新颖的目标引导生成框架，以控制模型的生成轨迹，旨在设计具有期望性质的新型分子结构。GGFlow在无条件与条件分子生成任务上均表现出优越性能，超越了现有基线，凸显了其有效性与更广泛应用的潜力。