molgraph 0.0.1

Implementations of graph neural networks for molecular machine learning

MolGraph: Graph Neural Networks for Molecular Machine Learning

This is an early release; things are still being updated and added. Hence, API compatibility may break in the future.

Any feedback is welcomed!

Manuscript

See pre-print

Documentation

See readthedocs

Installation

Install via pip:

pip install git+https://github.com/akensert/molgraph.git

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

Requirements/dependencies

• 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)

Tested with

• Ubuntu 20.04 - Python 3.8.10
• MacOS Monterey (12.3.1) - Python 3.10.3

Minimalistic implementation

A complete GNN implementation for small molecular graphs in about 30 lines of code:

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.AtomFeaturizer([
    chemistry.features.Symbol(),
    chemistry.features.Hybridization(),
    # ...
])

bond_encoder = chemistry.BondFeaturizer([
    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)
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)