graphium 2.3.5

Graphium: Scaling molecular GNNs to infinity.

Scaling molecular GNNs to infinity

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A deep learning library focused on graph representation learning for real-world chemical tasks.

• ✅ State-of-the-art GNN architectures.
• 🐍 Extensible API: build your own GNN model and train it with ease.
• ⚗️ Rich featurization: powerful and flexible built-in molecular featurization.
• 🧠 Pretrained models: for fast and easy inference or transfer learning.
• ⮔ Read-to-use training loop based on Pytorch Lightning.
• 🔌 Have a new dataset? Graphium provides a simple plug-and-play interface. Change the path, the name of the columns to predict, the atomic featurization, and you’re ready to play!

Documentation

Visit https://graphium-docs.datamol.io/.

You can try running Graphium on Graphcore IPUs for free on Gradient by clicking on the button above.

Installation for developers

For CPU and GPU developers

Use mamba:

# Install Graphium's dependencies in a new environment named `graphium`
mamba env create -f env.yml -n graphium

# Install Graphium in dev mode
mamba activate graphium
pip install --no-deps -e .

For IPU developers

# Install Graphcore's SDK and Graphium dependencies in a new environment called `.graphium_ipu`
./install_ipu.sh .graphium_ipu

The above step needs to be done once. After that, enable the SDK and the environment as follows:

source enable_ipu.sh .graphium_ipu

The Graphium CLI

Installing graphium makes two CLI tools available: graphium and graphium-train. These CLI tools make it easy to access advanced functionality, such as training a model, extracting fingerprints from a pre-trained model or precomputing the dataset. For more information, visit the documentation.

License

Under the Apache-2.0 license. See LICENSE.

Documentation

• Diagram for data processing in Graphium.

• Diagram for Muti-task network in Graphium