Ground-state 3D geometries of molecules are essential for many molecular
analysis tasks. Modern quantum mechanical methods can compute accurate 3D
geometries but are computationally prohibitive. Currently, an efficient
alternative to computing ground-state 3D molecular geometries from 2D graphs is
lacking. Here, we propose a novel deep learning framework to predict 3D
geometries from molecular graphs. To this end, we develop an equilibrium
message passing neural network (EMPNN) to better capture ground-state
geometries from molecular graphs. To provide a testbed for 3D molecular
geometry analysis, we develop a benchmark that includes a large-scale molecular
geometry dataset, data splits, and evaluation protocols. Experimental results
show that EMPNN can efficiently predict more accurate ground-state 3D
geometries than RDKit and other deep learning methods. Results also show that
the proposed framework outperforms self-supervised learning methods on property
prediction tasks.

该论文通过提出一种基于消息传递神经网络的深度学习框架，实现从分子结构图计算分子的 3D 几何结构。实验结果表明，该方法比 RDKit 和其他深度学习方法更准确地预测了分子的 3D 几何结构，并在性质预测任务中表现出良好的性能，同时提供了一个基准数据集，可用于 3D 分子几何结构分析。