KavrakiLab/Spec2Mol

Spec2Mol

Spec2Mol is a deep learning architecture for recommending molecular structures from MS/MS spectra.

Spec2Mol is an encoder-decoder architecture: The endoder creates an embedding from a given set of MS/MS spectra. The decoder reconstructs the molecular structure, in a SMILES format, given the embedding that the encoder generates.

The implementation of the Spec2Mol architecture is based on the Pytorch library.

Processing of the chemical data is based on the RDKit software.

Installation

Create a conda environment:

conda create -n spec2mol python=3.7
source activate spec2mol
conda install rdkit -c rdkit
conda install pytorch=1.6.0 torchvision -c pytorch

Generate spectra embeddings:

python predict_embs.py -pos_low_file 'sample_data/[M+H]_low.csv' \
                     -pos_high_file 'sample_data/[M+H]_high.csv' \
                     -neg_low_file 'sample_data/[M-H]_low.csv' \
                     -neg_high_file 'sample_data/[M-H]_high.csv' \

where pos_low_file, pos_high_file, neg_low_file, neg_high_file are the csv files with the four input spectra:

pos_low_file: precursor [M+H]+, energy 35% NCE (Normalized Collision Energy)

pos_high_file: precursor [M+H]+, energy 130% NCE

neg_low_file: precursor [M-H]-, energy 35% NCE

neg_high_file: precursor [M-H]-, energy 130% NCE

Each csv file has the m/z values in the first column and the intensity values in the second column. The columns are separated with commas. See file sample_data.

Dataset

The spectra encoder has been trained on the NIST Tandem Mass Spectral Library 2020 which is a commercial dataset.

Citation

@article{metatrans,
  author = {Litsa, Eleni E. and Chenthamarakshan, Vijil and Das, Payel and Kavraki, Lydia E.},
  title = {An end-to-end deep learning framework for translating mass spectra to de-novo molecules},
  journal = {Communications Chemistry},
  year = {2023},
}