https://github.com/1128bian/C-SGEN
Molecule Property Prediction based on Spatial Graph Embedding
https://github.com/1128bian/C-SGEN
Last synced: about 2 months ago
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Molecule Property Prediction based on Spatial Graph Embedding
- Host: GitHub
- URL: https://github.com/1128bian/C-SGEN
- Owner: 1128bian
- Created: 2019-07-20T01:36:51.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2019-07-18T05:48:44.000Z (almost 6 years ago)
- Last Synced: 2024-10-30T14:43:28.235Z (7 months ago)
- Language: Python
- Size: 1.2 MB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-graph-classification - [Python Reference
README
# C-SGEN
A PyTorch implementation of "Molecule Property Prediction based on Spatial Graph Embedding"
### Requirements
The codebase is implemented in Python 3.6.7. package versions used for development are just below.
```
Theano 1.0.3
numpy 1.16.4
scipy 1.3.0
torch 1.1.0
timeit 1.1.0
sklearn 0.0
deepchem 2.1.1
torch-scatter 1.2.0
torch-sparse 0.4.0
torch-cluster 1.4.2
torch-geometric 1.2.1
torch-spline-conv 1.1.0
torchvision 0.3.0
rdkit 2019.03.3.0
ChemoPy 1.0.0
pickle 0.7.5
```
### Datasets
Feature.npy, Normed_adj.npy, fingerprint_stand.npy and Interactions.npy are molecular features, adjacency matrices, molecular fingerprints and corresponding target values in the data, respectively.Input for C-SGEN Model
full_feature, edge and Interactions.npy are molecular features, adjacency matrices and corresponding target values of pytorch_geometric specific data format in the data, respectively.
#### Model Hyper-Parameters
```
--epochs INT Number of epochs. Default is 33.
--batch-size INT Number fo molecules per batch. Default is 8.
--C-SGEL-layers INT Number of C-SGELs. Default is 2.
--ch_num INT Number of neurons in Graph embedding layer. Default is 16.
--k INT Number of filters in conv1d. Default is 4.
--lr_decay FLOAT Weight decay / 10 epochs. Defatuls is 0.5.
--learning-rate FLOAT Adam learning rate. Default is 5e-4.
```
### Examples
The following commands learn a model and save the predictions. Training C-SGEN model on the default dataset,the data is ready to be saved in a folder. You can execute the above model directly.
```
python C-SGEN_trian.py
```
Training a PyG model directly.
```
python pyg_trian.py
```
Load data from DeepChem.
```
python load_FreeSolv.py
```