Three-Dimensionally Embedded Graph Convolutional Network (3DGCN) for Molecule Interpretation
This work addresses challenging biochemical problems, such as protein-ligand docking, by enabling rotation distinguishability and equivariance in deep-learning chemistry, though it is incremental in applying 3D graph methods to this domain.
The authors tackled the problem of predicting molecular properties and biochemical activities by introducing a 3D graph convolutional network (3DGCN) that incorporates spatial information from molecular topology, achieving significantly higher performance on various tasks compared to other deep-learning models.
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. The rotation distinguishability of 3DGCN, along with rotation equivariance, provides a key milestone in the implementation of three-dimensionality to the field of deep-learning chemistry that solves challenging biochemical problems.