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deep learning based prediction of structures and functional groups from MS/MS spectra

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Mass2SMILES is an open-source Python based deep learning approach for structure and functional group prediction from mass spectrometry data (MS/MS). Spectral data can be provided as MGF files (GNPS-syle) and model inference is most effciently performed via the provided docker container.

supplementary data with container and model at (you must have a vaild licence for NIST): DOI

the pre-print is available at: https://doi.org/10.1101/2023.07.06.547963

# the container is available as tarball in supplementary or via docker pull delser292/mass2smiles:final
# unzip the docker.zip, the mass2smiles folder contains the model files and scripts to execute everything and it is important to specify the path to this folder when starting predictions.

# The predictions can be started through this command:

docker run -v c:/your_path/to_the_folder/mass2smiles/:/app  mass2smiles:transformer_v1 conda run -n tf python app/mass2smiles_transformer.py your_mgf_file.mgf /app

The model architecture:

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