graphium 2.3.1

Graphium: Scaling molecular GNNs to infinity.

Scaling molecular GNNs to infinity

---

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

Training a model

To learn how to train a model, we invite you to look at the documentation, or the jupyter notebooks available here.

If you are not familiar with PyTorch or PyTorch-Lightning, we highly recommend going through their tutorial first.

Running an experiment

We have setup Graphium with hydra for managing config files. To run an experiment go to the expts/ folder. For example, to benchmark a GCN on the ToyMix dataset run

graphium-train dataset=toymix model=gcn

To change parameters specific to this experiment like switching from fp16 to fp32 precision, you can either override them directly in the CLI via

graphium-train dataset=toymix model=gcn trainer.trainer.precision=32

or change them permamently in the dedicated experiment config under expts/hydra-configs/toymix_gcn.yaml. Integrating hydra also allows you to quickly switch between accelerators. E.g., running

graphium-train dataset=toymix model=gcn accelerator=gpu

automatically selects the correct configs to run the experiment on GPU. Finally, you can also run a fine-tuning loop:

graphium-train +finetuning=admet

To use a config file you built from scratch you can run

graphium-train --config-path [PATH] --config-name [CONFIG]

Thanks to the modular nature of hydra you can reuse many of our config settings for your own experiments with Graphium.

License

Under the Apache-2.0 license. See LICENSE.

Documentation

• Diagram for data processing in molGPS.

• Diagram for Muti-task network in molGPS