用深度学习直接从低分辨率数据生成原子结构，免去人工解读
XDXD: End-to-end crystal structure determination with low resolution X-ray diffraction
- 基于扩散模型端到端生成晶体结构，不依赖人工解析电子密度图
- 在2.0~Å分辨率下结构匹配率达70.4%，均方根误差低于0.05
- 适用于小肽等复杂体系，有望解决以往无法处理的结构难题
从X射线衍射数据确定晶体结构是多个科学领域的基础，但在低分辨率条件下仍具挑战性。尽管近期深度学习模型在解决晶体学相位问题上取得突破，所得低分辨率电子密度图仍常模糊难解。为此，我们提出XDXD，据我们所知首个直接从低分辨率单晶衍射数据端到端生成完整原子模型的深度学习框架。其基于扩散的生成模型跳过人工地图解读，根据衍射图案生成化学合理结构。实验表明，当数据分辨率限制在2.0~Å时，XDXD达到70.4%的结构匹配率，均方根误差（RMSE）低于0.05。在包含24,000个实验结构的基准测试中，模型表现稳健且准确。此外，对小肽的案例研究展示了其向更复杂系统扩展的潜力，为以往难以处理的情况提供自动化结构解析新路径。
原文摘要 · Abstract (English)
Determining crystal structures from X-ray diffraction data is fundamental across diverse scientific fields, yet remains a significant challenge when data is limited to low resolution. While recent deep learning models have made breakthroughs in solving the crystallographic phase problem, the resulting low-resolution electron density maps are often ambiguous and difficult to interpret. To overcome this critical bottleneck, we introduce XDXD, to our knowledge, the first end-to-end deep learning framework to determine a complete atomic model directly from low-resolution single-crystal X-ray diffraction data. Our diffusion-based generative model bypasses the need for manual map interpretation, producing chemically plausible crystal structures conditioned on the diffraction pattern. We demonstrate that XDXD achieves a 70.4\% match rate for structures with data limited to 2.0~Å resolution, with a root-mean-square error (RMSE) below 0.05. Evaluated on a benchmark of 24,000 experimental structures, our model proves to be robust and accurate. Furthermore, a case study on small peptides highlights the model's potential for extension to more complex systems, paving the way for automated structure solution in previously intractable cases.
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