DiffMS: Diffusion Generation of Molecules Conditioned on Mass Spectra

This is the codebase for our preprint DiffMS: Diffusion Generation of Molecules Conditioned on Mass Spectra.

The DiffMS codebase is adapted from DiGress.

Environment installation

This code was tested with PyTorch 2.3.1, cuda 11.8 and torch_geometrics 2.3.1

• Download anaconda/miniconda if needed

• Create a conda environment with rdkit:

  conda create -y -c conda-forge -n diffms rdkit=2024.09.4 python=3.9

• conda activate diffms

• Install a corresponding version of pytorch, for example:

  pip install torch==2.3.1 --index-url https://download.pytorch.org/whl/cu118

• Run:

  pip install -e .

Run the code

For fingerprint-molecule pretraining run fp2mol_main.py. The primary pretraining dataset in our paper is referred to as 'combined' in the fp2mol.yaml config.

To finetune the end-to-end model on spectra-molecule generation, run fp2mol_main.py and select the spectra dataset by modifying the dataset property in configs/config.yaml to canopus (NPLIB1) or msg (MassSpecGym)

We rely on the MIST Codebase for pretraining the spectra encoder.

License

DiffMS is released under the MIT license.

Contact

If you have any questions, please reach out to mbohde@tamu.edu