Embedding of Molecular Structure Using Molecular Hypergraph Variational Autoencoder with Metric Learning
Core code for the paper "Embedding of Molecular Structure Using Molecular Hypergraph Variational Autoencoder with Metric Learning" (https://doi.org/10.1002/minf.202000203) by Daiki Koge, Naoaki ONO, Ming Huang, Md. Altaf-Ul-Amin, Shigehiko Kanaya.
PyTorch
We have updated the code such that it is using version 0.4.1.
RDKit
version 2017.09.1.
Python
version 3.6.6.
Jupyter
version 1.0.0.
mol_smooth_embedding/extract_mhg.py
mol_smooth_embedding/Prepare_RDKit_descriptors.ipynb
mol_smooth_embedding/Metric_MHG-VAE_with_DRL.ipynb
using qm9 physical properties
mol_smooth_embedding/Evaluate_Model.ipynb
using rdkit descriptors
mol_smooth_embedding/Evaluate_Model_RDkit_desc.ipynb
As mentioned in our paper, the VAE architecture uses the same model as kajino's MHG-VAE (http://proceedings.mlr.press/v97/kajino19a/kajino19a.pdf).
The code for the MHG-VAE can be found in mol_smooth_embedding/mhg
. And kajino's original code can be found in https://github.com/ibm-research-tokyo/graph_grammar.