Evaluating the generalizability of graph neural networks for predicting collision cross section

This repository contains code and data described in detail in our paper (Engler et al., 2024).

Table of Contents

• Citation
• Reproducibility
• Data

Citation

If you have found our manuscript useful in your work, please consider citing:

Engler Hart*, C., Preto*, A. J., Chanana*, S., Healey, D., Kind, T., Domingo-Fernandez, D. (2024). Evaluating the generalizability of graph neural networks for predicting collision cross section. ChemRxiv. https://doi.org/10.26434/chemrxiv-2024-32j2t

Reproducibility

Installation for development

poetry install
poetry run pre-commit install

Train the models

See the commands in the Makefile to train the models. Run them as make train-metlin-test-metlin

Reproduce results

Run the notebooks located in the notebooks corresponding to each analysis. There are two folders:

• data_processing: notebooks to process the METLIN-CCS and CCSBase and make the data splits
• reproduce_figures: the name of the notebooks indicates which notebook can reproduce which figures of the manuscript manuscript.
• exploring_predictions: notebooks to explore the predictions of the models in detail

Data

Predictions are available and can be directly downloaded from . The files should be unzipped and placed in the data directory.

The original datasets are available here:

• CCSBase: https://ccsbase.net/query
• Metlin: https://metlin.scripps.edu/

Predicting CCS

Train the model based on your own training dataset with [wrapper_train] and predict with wrapper_predict function.

The baseline original repositories are:

• SigmaCCS: https://github.com/zmzhang/SigmaCCS
• GraphCCS: https://github.com/tingxiecsu/GraphCCS

References

Ross, D. H., Cho, J. H., and Xu, L. (2020). Breaking down structural diversity for comprehensive prediction of ion-neutral collision cross sections. Analytical chemistry, 92(6), 4548-4557. https://doi.org/10.1021/acs.analchem.9b05772

Baker, E. S., Hoang, C., Uritboonthai, W., Heyman, H. M., Pratt, B., MacCoss, M., et al. (2023). METLIN-CCS: an ion mobility spectrometry collision cross section database. Nature methods, 20(12), 1836-1837. https://doi.org/10.1038/s41592-023-02078-5

Guo, R., Zhang, Y., Liao, Y., Yang, Q., Xie, T., Fan, X., et al. (2023). Highly accurate and large-scale collision cross sections prediction with graph neural networks. Communications Chemistry, 6(1), 139. https://doi.org/10.1038/s42004-023-00939-w

Xie, T., Yang, Q., Sun, J., Zhang, H., Wang, Y., and Lu, H. Large-Scale Prediction of Collision Cross-Section with Graph Convolutional Network for Compound Identification.