3D Molecular Geometry Analysis with 2D Graphs
This addresses the need for efficient 3D geometry prediction in molecular analysis, though it appears incremental as it builds on existing deep learning methods for a known bottleneck.
The paper tackles the problem of efficiently predicting ground-state 3D molecular geometries from 2D graphs, which is computationally prohibitive with quantum methods, and shows that their EMPNN framework predicts more accurate geometries than existing methods like RDKit and other deep learning approaches.
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.