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 v1.2.0 is now available!
The changes log can be found at ./CHANGE_LOG.md.
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🧪 Source Code: Clone the repository for both training and inference functionality (document for source code).
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📦 PyPI Package: Install
molnetpackfor easy inference with pre-trained models (document for PyPI package). -
☁️ Web Service: Access our no-installation web service with API support for inference (workflow on Konia).
@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}
}
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