发表机构
University of Bristol（布里斯托大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
Inverse-IMPRESSION平台基于倒置图形变压器网络，通过预测原子键连接性、校正结构、多步预测等阶段，利用¹H和¹³C NMR数据，能有效解析分子结构，为自动分子结构解析提供了基于图形机器学习的有效方法。
AI 中文摘要
在此，我们展示了一个基于倒置图形变压器网络IMPRESSION-G2构建的平台，它能够直接从实验核磁共振（NMR）光谱信息中准确快速地重建分子键合。该平台包括三个相互关联的阶段：一个预测原子间键连接性的一次性模型；一个通过去除不确定键并迭代重新分配来校正预测结构的结构校正阶段；噪声增强多步预测，生成候选结构集合并排序以确定最佳拟合结构。通过整合一系列¹H和¹³C NMR数据，包括二维（2D）实验（如COSY、HSQC和HMBC），Inverse-IMPRESSION平台使用模拟NMR数据正确识别了77.8%的重原子数高达30个（H、C、N、O和F）的分子结构，使用实验NMR数据正确识别了19个分子中的10个（53%）。所解析的实验结构分子量高达480 Da，代表了合成和天然产物中经常给化学家带来挑战的复杂结构。因此，Inverse-IMPRESSION框架提供了第一种使用基于图形的机器学习对实验数据进行自动分子结构解析的有效方法。
英文摘要
Here, we present a platform built on our inverted Graph Transformer Network, IMPRESSION-G2, which can accurately and rapidly reconstruct molecular bonding directly from experimental nuclear magnetic resonance (NMR) spectroscopic information. It comprises three interconnected stages: a one-shot model that predicts bond connectivity between atoms; a structure-correction stage that corrects the predicted structures by removing uncertain bonds and iteratively reassigning them; noise-augmented multi-shot prediction, generating an ensemble of candidate structures, which are ranked to identify the best-fit structure. By integrating a range of $^{1}$H and $^{13}$C NMR data, including two-dimensional (2D) experiments such as COSY, HSQC, and HMBC, the inverse-IMPRESSION platform correctly identifies the structures of 77.8% of molecules with up to 30 heavy atoms (H, C, N, O and F) using simulated NMR data, and 10 of 19 (53%) molecules using experimental NMR data. The experimental structures solved have molecular weights of up to 480 Da and are representative of the complex structures in synthetic and natural products that routinely challenge chemists. The inverse-IMPRESSION framework thus provides the first effective approach for automated molecular structure elucidation using graph-based machine learning on experimental data.
Comments15-page manuscript (4 figures), plus 59 pages of Supporting Information (45 figures); Submitted to the Journal of the American Chemical Society
