DrugFlow通过融合流匹配与马尔可夫桥，实现三维药物设计的高效生成。
Multi-domain Distribution Learning for De Novo Drug Design
- 结合连续流匹配与离散马尔可夫桥，统一建模分子结构与几何特性。
- 在三种蛋白-配体数据上达到当前最优性能，且具备分布外样本检测能力。
- 支持侧链角度与分子联合采样，适用于多构象空间探索。
我们提出DrugFlow，一种基于结构的生成模型，用于从头药物设计，将连续流匹配与离散马尔可夫桥相结合，在学习三维蛋白-配体数据的化学、几何和物理特性方面表现优异。该模型具备不确定性估计能力，可有效识别分布外样本。为提升采样效率，我们设计了一种适用于流匹配与马尔可夫桥框架的联合偏好对齐方案，引导生成向理想性质区域集中。此外，模型进一步扩展至联合采样蛋白侧链角度与小分子，以探索蛋白质的构象空间。
原文摘要 · Abstract (English)
We introduce DrugFlow, a generative model for structure-based drug design that integrates continuous flow matching with discrete Markov bridges, demonstrating state-of-the-art performance in learning chemical, geometric, and physical aspects of three-dimensional protein-ligand data. We endow DrugFlow with an uncertainty estimate that is able to detect out-of-distribution samples. To further enhance the sampling process towards distribution regions with desirable metric values, we propose a joint preference alignment scheme applicable to both flow matching and Markov bridge frameworks. Furthermore, we extend our model to also explore the conformational landscape of the protein by jointly sampling side chain angles and molecules.
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