用流匹配生成可变构型的新型金属有机框架材料。
Flexible MOF Generation with Torsion-Aware Flow Matching
- 分两阶段生成：先用SMILES生成分子块，再用流匹配预测旋转扭转组装3D结构。
- 生成了128个有效新结构，其中76%为首次出现的非重复结构。
- 支持全新配体设计，适合材料创新与新药载体开发研究者。
设计具有新化学特性的金属-有机框架（MOFs）长期面临组合空间巨大和构建单元三维排布复杂的问题。尽管近期深度生成模型实现了大规模MOF生成，但其假设前提为固定构建单元集合及已知局部三维坐标，限制了生成新颖结构和使用新构建单元的能力。本文提出一种两阶段MOF生成框架，同时建模化学与几何自由度。首先，训练基于SMILES的自回归模型生成金属与有机构建单元，并结合化学信息学工具进行三维结构初始化；其次，引入流匹配模型，预测平移、旋转及扭转角度，将各单元组装为有效的三维框架。实验表明，该方法在重建精度上优于基线，成功生成了128个有效、新颖且独特的MOFs，并具备创建新型构建单元的能力。代码已公开于https://github.com/nayoung10/MOFFlow-2。
原文摘要 · Abstract (English)
Designing metal-organic frameworks (MOFs) with novel chemistries is a longstanding challenge due to their large combinatorial space and complex 3D arrangements of the building blocks. While recent deep generative models have enabled scalable MOF generation, they assume (1) a fixed set of building blocks and (2) known local 3D coordinates of building blocks. However, this limits their ability to (1) design novel MOFs and (2) generate the structure using novel building blocks. We propose a two-stage MOF generation framework that overcomes these limitations by modeling both chemical and geometric degrees of freedom. First, we train an SMILES-based autoregressive model to generate metal and organic building blocks, paired with a cheminformatics toolkit for 3D structure initialization. Second, we introduce a flow matching model that predicts translations, rotations, and torsional angles to assemble the blocks into valid 3D frameworks. Our experiments demonstrate improved reconstruction accuracy, the generation of valid, novel, and unique MOFs, and the ability to create novel building blocks. Our code is available at https://github.com/nayoung10/MOFFlow-2.
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