MaMoMo

MaMoMo: Masked Molecular Modeling with Graph Attention Networks

Requirements

1. Dependencies

Ensure you have the following dependencies installed:

Python (>=3.6)
PyTorch (>=1.6.0)
DGL (>=1.1.2)

2. Python Libraries

Install the required Python libraries using:

pip install -r requirements.txt

NumPy (>=1.18.0)
Matplotlib (>=3.1.0)
Pytorch Lightning (>=1.8.0,<=2.0.0)
matgl (==0.7.0)
Hydra(>=1.3.1)
pymatgen

3. Hardware Requirements

This project is optimized for GPU acceleration. Ensure you have access to a compatible GPU and install the necessary CUDA toolkit and cuDNN library. Visit PyTorch's GPU support page for detailed instructions. Note that DGL supports CUDA up to 11.8 version, please make sure to check the compatibility with CUDA and pytorch versions.

4. Dataset

5. Configuration

Adjust the configuration parameters inside src/config/ files to customize the model training and evaluation settings.

6. Environment Setup

Create and activate a virtual environment to isolate the project dependencies.

# Example: Create virtual environment
python -m venv venv

# Activate virtual environment
source venv/bin/activate  # On Windows: .\venv\Scripts\activate

7. Running the Code

To run the training procedure, please ensure that you are in the src folder, then run

python train_lightning.py

Hydra allows configuration parameters to be overriden by command line arguments:

#Example
python train_lightning.py train.num_epochs=500 train.batch_size==32 #hydra overrides are optional and refer to parameters in src/config files

Data visualization and model evaluation are contained in notebooks at the moment (see node_prediction.ipynb, graph_prediction.ipynb and node+graph_prediction.ipynb) and are still a WIP.

kazeevn/MaMoMo