jkarolczak/ligand-classification
Project examing sparse deep learning architectures for ligand classification.
This project helps structural biologists and biochemists accurately identify small-molecule ligands bound to active sites using electron density maps from X-ray diffraction or cryo-electron microscopy (cryoEM). It takes a 3D point cloud representation of an electron density map as input and outputs the probable ligand classifications. This tool is designed for researchers in drug discovery and structural biology who analyze molecular structures.
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Use this if you need an automated, unbiased method to classify small-molecule ligands from X-ray or cryoEM electron density maps, especially when interpreting these maps is challenging or prone to cognitive bias.
Not ideal if you primarily work with other structural biology data types beyond X-ray diffraction or cryoEM density maps, or if you require an approach that doesn't rely on deep learning methodologies.
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May 27, 2025
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