YoujiaZhang/SigmaCCS
[Communications Chemistry 2023] Highly accurate and large-scale collision cross section prediction with graph neural network for compound identification
This project helps analytical chemists and mass spectrometry researchers predict the collision cross section (CCS) of chemical compounds. By inputting the SMILES string or other molecular identifiers, you get highly accurate CCS values for various adduct ion types, which is crucial for identifying unknown compounds in complex samples. This is for scientists working with ion mobility-mass spectrometry data.
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Use this if you need to predict collision cross section (CCS) values for molecules to enhance compound identification in your ion mobility-mass spectrometry experiments.
Not ideal if you are solely focused on traditional mass spectrometry without an ion mobility component or if you only need exact mass information for compound identification.
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60
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2
Language
Jupyter Notebook
License
MIT
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Last pushed
Sep 22, 2021
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