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

arXiv: https://arxiv.org/abs/1811.09794 · HTML: https://mlsanity.com/paper/1811.09794/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Nov 2018.

## Built on by (most cited first, 7 of 7)

- [Deep Molecular Representation Learning via Fusing Physical and Chemical Information](https://mlsanity.com/paper/2112.04624/) (2021)
- [Graph Neural Networks for Molecules](https://mlsanity.com/paper/2209.05582/) (2022)
- [HamNet: Conformation-Guided Molecular Representation with Hamiltonian Neural Networks](https://mlsanity.com/paper/2105.03688/) (2021)
- [Lo-Hi: Practical ML Drug Discovery Benchmark](https://mlsanity.com/paper/2310.06399/) (2023)
- [Predicting solvation free energies with an implicit solvent machine learning potential](https://mlsanity.com/paper/2406.00183/) (2024)
- [3D-Mol: A Novel Contrastive Learning Framework for Molecular Property Prediction with 3D Information](https://mlsanity.com/paper/2309.17366/) (2023)
- [MolNet: A Chemically Intuitive Graph Neural Network for Prediction of Molecular Properties](https://mlsanity.com/paper/2203.09456/) (2022)

## Abstract (arXiv)

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.