This article introduces neural graph distance embedding (nGDE), a method for generating 3D molecular geometries. Leveraging a graph neural network trained on the OE62 dataset of molecular geometries, nGDE predicts interatomic distances based on molecular graphs. These distances are then used in multidimensional scaling to produce 3D geometries, subsequently refined with standard bioorganic forcefields. The machine learning‐based graph distance introduced herein is found to be an improvement over the conventional shortest path distances used in graph drawing. Comparative analysis with a state‐of‐the‐art distance geometry method demonstrates nGDE's competitive performance, particularly showcasing robustness in handling polycyclic molecules—a challenge for existing methods.

中文翻译：

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用于分子几何生成的神经图距离嵌入

本文介绍神经图距离嵌入 (nGDE)，一种生成 3D 分子几何形状的方法。 nGDE 利用在分子几何 OE62 数据集上训练的图神经网络，根据分子图预测原子间距离。然后将这些距离用于多维缩放以生成 3D 几何形状，随后使用标准生物有机力场进行细化。发现本文引入的基于机器学习的图形距离比图形绘制中使用的传统最短路径距离有所改进。与最先进的距离几何方法的比较分析证明了 nGDE 的竞争性能，特别是展示了处理多环分子的鲁棒性——这是对现有方法的挑战。