Source code for the paper "Emergence of molecular structures from repository-scale self-supervised learning on tandem mass spectra".

This GitHub repository is a work in progress. We are planning to transform it into a user-friendly Python package during July-August 2024.

Run the following code from the command line.

git clone https://github.com/pluskal-lab/DreaMS.git; cd DreaMS
. scripts/install.sh
. scripts/download_models.sh

git clone command will download this GitHub repository and install.sh will install it. download_models.sh script will download pre-trained DreaMS models from Zenodo. The installation script will create a conda environment named dreams. If you are not familiar with conda or do not have it installed, please refer to the official documentation.

To compute DreaMS representations for MS/MS spectra from .mgf file, run the following Python code.

from dreams.api import compute_dreams_embeddings
dreams_embeddings = compute_dreams_embeddings('data/examples/example_5_spectra.mgf')

The resulting dreams_embeddings object is a matrix with 5 rows and 1024 columns, representing 5 1024-dimensional DreaMS representations for 5 input spectra stored in the .mgf file.

• Wrap the repository into a pip package.
• Import utilities to matchms.
• DreaMS Atlas exploration demo.
• Upload weights of all models.
• Provide scripts to collect/download GeMS datasets.
• Extend dreams.api with more functionality (e.g. attention heads analysis).
• Add tutorial notebooks.
• Upload Murcko splits and detailed tutorial notebook.