BioinfoMachineLearning/PoseBench
Comprehensive benchmarking of protein-ligand structure prediction methods. (Nature Machine Intelligence)
This tool helps computational chemists and drug discovery scientists evaluate the effectiveness of different methods for predicting how small molecules (ligands) bind to proteins. By taking in protein and ligand structures, it runs various prediction algorithms and then provides detailed comparative plots of their accuracy, allowing users to select the most reliable approach for their research.
213 stars. Available on PyPI.
Use this if you need to rigorously compare and benchmark multiple protein-ligand docking or binding pose prediction software to find the best performing one for your drug discovery or structural biology research.
Not ideal if you are looking for a simple, single solution to predict protein-ligand interactions without the need for extensive comparative analysis or benchmarking.
Stars
213
Forks
16
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Mar 13, 2026
Commits (30d)
0
Dependencies
38
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