Three-Dimensionally Embedded Graph Convolutional Network (3DGCN) for Molecule Interpretation

Understand

We present a three-dimensional graph convolutional network (3DGCN), which predicts molecular properties and biochemical activities, based on 3D molecular graph.

• In the 3DGCN, graph convolution is unified with learning operations on the vector to handle the spatial information from molecular topology.
• The 3DGCN model exhibits significantly higher performance on various tasks compared with other deep-learning models, and has the ability of generalizing a given conformer to targeted features regardless of its rotations in the 3D space.
• More significantly, our model also can distinguish the 3D rotations of a molecule and predict the target value, depending upon the rotation degree, in the protein-ligand docking problem, when trained with orientation-dependent datasets.