BioinfoMachineLearning/PoseBench
Comprehensive benchmarking of protein-ligand structure prediction methods. (Nature Machine Intelligence)
Provides standardized evaluation across 10+ structure prediction methods (DiffDock, FABind, RoseTTAFold-All-Atom, Chai-1, Boltz, and others) using Hydra-based configuration management for reproducible experimental sweeps and ensemble inference. Includes curated benchmark datasets (Astex Diverse, DockGen-E, PoseBusters) with automated scoring pipelines and comparative visualization tools, available as both a pip-installable package and full development environment with pre-configured method implementations.
213 stars and 148 monthly downloads. Available on PyPI.
Stars
213
Forks
16
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Mar 13, 2026
Monthly downloads
148
Commits (30d)
0
Dependencies
38
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