Structure-based drug design (SBDD) aims to efficiently discover high-affinity ligands within vast chemical spaces. However, current generative models struggle with objective misalignment and rigid sampling budgets. We present MolFORM, a fast multi-modal flow matching framework for discrete atom types and continuous coordinates. Crucially, to bridge the gap between generative capability and biochemical objectives, we introduce two distinct post-training strategies: (1) Direct Preference Optimization (DPO), which performs offline alignment using ranked preference pairs; and (2) an online reinforcement learning paradigm that optimizes the generative flow directly on the forward process. Both strategies effectively navigate the chemical space toward high-affinity regions. MolFORM achieves state-of-the-art results on the CrossDocked2020 benchmark (Vina Score -7.60, Diversity 0.75), demonstrating that incorporating preference alignment mechanisms-whether via offline optimization or online reinforcement-is crucial for steering generative models toward high-affinity binding regions. The source code for MolFORM is publicly available at https://github.com/daiheng-zhang/SBDD-MolFORM.

翻译：结构药物设计旨在从广阔的化学空间中高效发现高亲和力配体。然而，现有生成模型普遍面临目标错位与采样预算僵化的问题。本文提出MolFORM，一种针对离散原子类型与连续坐标的快速多模态流匹配框架。关键之处在于，为弥合生成能力与生化目标之间的鸿沟，我们引入了两种不同的后训练策略：（1）直接偏好优化，利用排序偏好对进行离线对齐；（2）在线强化学习范式，直接在正向过程上优化生成流。两种策略均能有效引导化学空间探索至高亲和力区域。MolFORM在CrossDocked2020基准测试中取得领先成果（Vina评分-7.60，多样性0.75），证明无论是通过离线优化还是在线强化学习，整合偏好对齐机制对于引导生成模型聚焦高亲和力结合区域具有关键作用。MolFORM源代码已公开于https://github.com/daiheng-zhang/SBDD-MolFORM。