Augus1999/bayesian-flow-network-for-chemistry
ChemBFN: Bayesian Flow Network Framework for Chemistry Tasks. Developed in Hiroshima University.
ChemBFN helps chemists, materials scientists, and drug designers efficiently generate novel molecular structures and protein sequences. You provide a general chemical space or specific design goals, and it outputs new molecular structures (SMILES or SELFIES) or protein sequences, along with their predicted properties, to accelerate research and development workflows. It also helps optimize existing molecular templates.
Use this if you need to rapidly explore chemical spaces, design new molecules or proteins with specific properties, or optimize existing chemical structures for better performance.
Not ideal if you are not working with molecular or protein design tasks, or if you need to perform complex 3D simulations of molecular dynamics.
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28
Forks
5
Language
Python
License
AGPL-3.0
Category
Last pushed
Mar 13, 2026
Commits (30d)
0
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