Welcome to 3DMolMS Documentation

3D Molecular Network for Mass Spectra Prediction (3DMolMS) is a deep neural network model to predict the tandem mass (MS/MS) spectra of compounds from their 3D conformations. It can be used to:

• Predict MS/MS spectra for small molecules

• Generate reference libraries of small molecule MS/MS spectra for identification

• Predict molecular properties like retention time (RT) and collision cross section (CCS)

• Pretrain models on molecular datasets

This document provides a guide to using 3DMolMS for inference through the molnetpack package and comprehensive usages of the source code for training and inference.

PyPI package

• PyPI package installation and usage
  • Installing from PyPI
  • Using molnetpack for MS/MS prediction
  • Plot predicted MS/MS
  • Using molnetpack for properties prediction
  • Training with molnetpack

Source code

• Source code setup
  • Installing from source code
  • Update package locally
  • Requirements
• Source code usage
  • Tandem mass spectra prediction
  • Train your own model for MS/MS prediction
  • Retention time prediction
  • Collision cross-collision section prediction
  • Fine-tune on your own data
• Advanced features
  • Pretraining 3DMolMS on QM9
  • Generating a reference library for molecular identification

Addtional information

• Supported formats

References

@article{hong20233dmolms,
  title={3DMolMS: prediction of tandem mass spectra from 3D molecular conformations},
  author={Hong, Yuhui and Li, Sujun and Welch, Christopher J and Tichy, Shane and Ye, Yuzhen and Tang, Haixu},
  journal={Bioinformatics},
  volume={39},
  number={6},
  pages={btad354},
  year={2023},
  publisher={Oxford University Press}
}
@article{hong2024enhanced,
  title={Enhanced structure-based prediction of chiral stationary phases for chromatographic enantioseparation from 3D molecular conformations},
  author={Hong, Yuhui and Welch, Christopher J and Piras, Patrick and Tang, Haixu},
  journal={Analytical Chemistry},
  volume={96},
  number={6},
  pages={2351--2359},
  year={2024},
  publisher={ACS Publications}
}