用于从头药物设计的多域分布学习
Multi-domain Distribution Learning for De Novo Drug Design
AI总结：
该研究提出生成模型DrugFlow，结合连续流匹配与离散马尔可夫桥实现三维蛋白质-配体数据多方面学习的最优性能，具备分布外检测能力，还提出联合偏好对齐方案并扩展模型探索蛋白质构象景观。
摘要：
我们提出了DrugFlow，这是一种用于基于结构的药物设计的生成模型，它将连续流匹配与离散马尔可夫桥相结合，在学习三维蛋白质-配体数据的化学、几何和物理方面表现出最先进的性能。我们为DrugFlow赋予了不确定性估计能力，能够检测分布外样本。为了进一步增强采样过程以趋向具有理想度量值的分布区域，我们提出了一种适用于流匹配和马尔可夫桥框架的联合偏好对齐方案。此外，我们通过联合采样侧链角度和分子，将模型扩展到探索蛋白质的构象景观。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
