molnetpack 1.2.1

3DMolMS: prediction of tandem mass spectra from 3D molecular conformations

3DMolMS

(free for academic use)

3D Molecular Network for Mass Spectra Prediction (3DMolMS) is a deep neural network model to predict the MS/MS spectra of compounds from their 3D conformations. This model's molecular representation, learned through MS/MS prediction tasks, can be further applied to enhance performance in other molecular-related tasks, such as predicting retention times (RT) and collision cross sections (CCS).

Paper | Document | Workflow on Konia | PyPI package

🔔 Latest Release: 3DMolMS v1.2.1 is now available!

The changes log can be found at ./CHANGE_LOG.md.

Accessed in 3 ways:

1. 🧪 Source Code: Clone the repository for both training and inference functionality (document for source code).

2. 📦 PyPI Package: Install molnetpack for easy inference with pre-trained models (document for PyPI package).

3. ☁️ Web Service: Access our no-installation web service with API support for inference (workflow on Konia).

Citation

@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}
}

License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.