Machine learning models to convert molecules to ESI mass spectra (and maybe back again in a future version) trained on GNPS data. Currently the following models are available:
| model | description | hidden unit dim | num layers |
|---|---|---|---|
| mlp | MLP with residual blocks trained on 1024 Morgan fingerprints | 1024 | 6 |
| gcn | Simple GCN with deepchem like node features | 1024 | 3 |
| egnn | Equivariant GNN trained on (RDkit optimized) 3D structures | 1024 | 2 |
| bert | MLP trained on representations from the (smaller) ChemBERTa SMILES model | 1024 | 6 |
You need to have torch and torch_geometric installed. I don't provide these as part of the dependencies since torch_geometric installs depends a lot on your CUDA and torch setup. To install torch_geometric from scratch use their documentation; e.g. can do it with pip using their wheels:
pip install torch-scatter torch-sparse torch-cluster torch-spline-conv torch-geometric -f https://data.pyg.org/whl/torch-1.9.0+cpu.html
Once torch and torch_geometric are installed, you can install molxspec:
pip install https://github.com/dimenwarper/molxspec/releases/download/v.0.1.0/molxspec-0.1.0-py3-none-any.whl
You can predict spectra from the command line:
mol2spec --model [mlp | gcn | egnn | bert] input_smiles.txt output.txt
Where input_smiles.txt is a file containing one molecule SMILES for each line. For the egnn model, molecules will have their 3D structure computed and optimized automatically using RDKit. First time use will download the pretrained models automatically, which can take some time, though it is a one-time thing only.
You can also predict spectra programmatically:
from molxspec import mol2spec
dict_of_smiles_and_spectra = mol2spec.predict(list_of_smiles)