This repository accompanies

Rapid Prediction of Electron–Ionization Mass Spectrometry Using Neural Networks
Jennifer N. Wei, David Belanger, Ryan P. Adams, and D. Sculley
ACS Central Science 2019 5 (4), 700-708
DOI: 10.1021/acscentsci.9b00085

We predict the mass spectrometry spectra of molecules using deep learning techniques applied to various molecule representations. The performance behavior is evaluated with a custom-made library matching task. In this task we identify molecules by matching its spectra to a library of labeled spectra. As a baseline, this library contains all of the molecules in the NIST main library, which mimics the behavior currently used by experimental chemists. To test our predictions, we replace portions of the library with spectra predictions from our model. This task is described in more detail below.

It is recommended to use Anaconda with a Python 3.6 environment to install these packages.

• RDKit
• Tensorflow

Most of the packages required here can be installed with conda install tensorflow=1.13.2 rdkit matplotlib and pip install absl-py.

1. Create a directory and download the weights for the model.

$ MODEL_WEIGHTS_DIR=/home/path/to/model
$ mkdir $MODEL_WEIGHTS_DIR
$ pushd $MODEL_WEIGHTS_DIR
$ curl -o https://storage.googleapis.com/deep-molecular-massspec/massspec_weights/massspec_weights.zip
$ unzip massspec_weights.zip
$ popd

2. Run the model prediction on the example molecule

$ python make_spectra_prediction.py \
--input_file=examples/pentachlorobenzene.sdf \
--output_file=/tmp/annotated.sdf \
--weights_dir=$MODEL_WEIGHTS_DIR/massspec_weights

The molecules used for the training, validation, and test sets can be found under the directory training_splits. The molecules are provided in inchikey and smiles format.

@article{doi:10.1021/acscentsci.9b00085,
author = {Wei, Jennifer N. and Belanger, David and Adams, Ryan P. and Sculley, D.},
title = {Rapid Prediction of Electron–Ionization Mass Spectrometry Using Neural Networks},
journal = {ACS Central Science},
volume = {5},
number = {4},
pages = {700-708},
year = {2019},
doi = {10.1021/acscentsci.9b00085},
URL = {https://doi.org/10.1021/acscentsci.9b00085},\ }