This is an early release; things are still being updated and added. Hence, API compatibility may break in the future.
Any feedback is welcomed!
See pre-print
See readthedocs
Install via pip:
pip install molgraph
Install via docker:
git clone https://github.com/akensert/molgraph.git cd molgraph/docker docker build -t molgraph-tf[-gpu][-jupyter]/molgraph:0.0 molgraph-tf[-gpu][-jupyter]/ docker run -it [-p 8888:8888] molgraph-tf[-gpu][-jupyter]/molgraph:0.0
Now run your first program with MolGraph:
from tensorflow import keras
from molgraph import chemistry
from molgraph import layers
from molgraph import models
# Obtain dataset, specifically ESOL
qm7 = chemistry.datasets.get('esol')
# Define molecular graph encoder
atom_encoder = chemistry.Featurizer([
chemistry.features.Symbol(),
chemistry.features.Hybridization(),
# ...
])
bond_encoder = chemistry.Featurizer([
chemistry.features.BondType(),
# ...
])
encoder = chemistry.MolecularGraphEncoder(atom_encoder, bond_encoder)
# Obtain features and associated labels
x_train = encoder(qm7['train']['x'])
y_train = qm7['train']['y']
x_test = encoder(qm7['test']['x'])
y_test = qm7['test']['y']
# Build model via Keras API
gnn_model = keras.Sequential([
keras.layers.Input(type_spec=x_train.spec),
layers.GATConv(name='gat_conv_1'),
layers.GATConv(name='gat_conv_2'),
layers.Readout(),
keras.layers.Dense(units=1024, activation='relu'),
keras.layers.Dense(units=y_train.shape[-1])
])
# Compile, fit and evaluate
gnn_model.compile(optimizer='adam', loss='mae')
gnn_model.fit(x_train, y_train, epochs=50)
scores = gnn_model.evaluate(x_test, y_test)
# Compute gradient activation maps
gam_model = models.GradientActivationMapping(
model=gnn_model, layer_names=['gat_conv_1', 'gat_conv_2'])
maps = gam_model.predict(x_train)
- Python (version ~= 3.8.10)
- TensorFlow (version ~= 2.7.0)
- RDKit (version ~= 2022.3.3)
- NumPy (version ~= 1.21.2)
- Pandas (version ~= 1.0.3)
- Ubuntu 20.04 - Python 3.8.10
- MacOS Monterey (12.3.1) - Python 3.10.3