论文标题
3D分子几何形状的距离几何图形注意网络（DG-GAT）
Distance-Geometric Graph Attention Network (DG-GAT) for 3D Molecular Geometry
论文作者
论文摘要
到目前为止，分子科学的深度学习主要集中在2D分子图上。然而，最近，由于其科学意义和在现实世界应用中的重要性，已经进行了将其扩展到3D分子几何形状。 3D距离几何图表示（DG-GR）采用代表3D图几何形状的统一方案（距离）。它与图形的旋转和翻译是不变的，它反映了成对的节点相互作用及其通常的局部性质，尤其与3D分子几何相关。为了促进分子科学深度学习中3D分子几何形状的结合，我们采用了具有动态注意力的新图形注意力网络（GATV2）与DG-gr一起使用，并提出了3D距离几何图形注意力网络（DG-GAT）。 GATV2非常适合DG-GR，因为注意力可能因节点和节点之间的距离而异。 ESOL和FREESOLV数据集的DG-GAT的实验结果显示出基于2D分子图的标准图卷积网络的重大改进（分别为31％和38％）。 QM9数据集也是如此。我们的工作证明了基于3D分子几何形状的深度学习的DG-GAT的效用和价值。
Deep learning for molecular science has so far mainly focused on 2D molecular graphs. Recently, however, there has been work to extend it to 3D molecular geometry, due to its scientific significance and critical importance in real-world applications. The 3D distance-geometric graph representation (DG-GR) adopts a unified scheme (distance) for representing the geometry of 3D graphs. It is invariant to rotation and translation of the graph, and it reflects pair-wise node interactions and their generally local nature, particularly relevant for 3D molecular geometry. To facilitate the incorporation of 3D molecular geometry in deep learning for molecular science, we adopt the new graph attention network with dynamic attention (GATv2) for use with DG-GR and propose the 3D distance-geometric graph attention network (DG-GAT). GATv2 is a great fit for DG-GR since the attention can vary by node and by distance between nodes. Experimental results of DG-GAT for the ESOL and FreeSolv datasets show major improvement (31% and 38%, respectively) over those of the standard graph convolution network based on 2D molecular graphs. The same is true for the QM9 dataset. Our work demonstrates the utility and value of DG-GAT for deep learning based on 3D molecular geometry.
