PaccMann/paccmann_kinase_binding_residues
Comparison of active site and full kinase sequences for drug-target affinity prediction and molecular generation. Full paper: https://pubs.acs.org/doi/10.1021/acs.jcim.1c00889
This project helps drug discovery scientists accurately predict how strongly a potential drug molecule will bind to a kinase protein. By focusing on the kinase's active site protein sequence rather than its full sequence, it takes in active site sequences and molecular structures to output a binding affinity prediction. Medicinal chemists and computational biologists focused on kinase inhibitor design would use this.
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Use this if you are developing new drug candidates and need to predict their binding affinity to kinases, especially when looking to optimize for specific active site interactions.
Not ideal if your primary interest is in designing molecules for non-kinase protein targets or if you need to model protein interactions beyond binding affinity.
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Language
Python
License
MIT
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Last pushed
Oct 03, 2022
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