YunjaeChoi / ggnnmols

Gated Graph Neural Network for Molecules

Gated Graph Neural Network for Molecules

Implementation of Gated graph neural network for molecules in tensorflow.

Gated graph neural network paper

Dependencies

1. Rdkit

conda install -c rdkit rdkit

2. Tensorflow

• cpu-version

pip install tensorflow

• gpu-version

pip install tensorflow-gpu

3. others(for preprocessing): pandas, tqdm, sklearn

conda install pandas tqdm sklearn

Preprocessing

1. Data

Tox21 Data was used for training.

sdf data was converted to graph.(Only mols valid by rdkit was used.) graph data consists:

• node features:
  • atomic number(onehot)
  • atomic number
  • formal charge
  • radical eletrons
  • is aromatic(bool)
  • hybridization(onehot)
• edge features:
  • bond type(onehot)
  • is in ring(bool)

2. preprocess.py

Does the following steps:

1. Extracts labels from csv file.
2. Check mols.
3. Get features.
4. Saves train/val/test data.

Training

1. Model

Variant of Gated Graph Neural Network(GGNN)

• modifications:
  • modified for undirected graph
  • edge hidden features are used instead of weights per edge types
  • node hidden features are updated via stacked GRUs instead of one GRU

2. train.ipynb

Does the following steps:

1. Loads preprcessed data
2. Trains with missing labels
3. Saves weights(best)

Result

1. saved_models

Current trained model(weights.best.hdf5) is in this folder.

2. evaluate.ipynb

Model can be evaluated.

ex) Receiver Operating Characteristic curve of current saved model.