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