Liu-Group-UF/PropMolFlow
SE(3) Equivariant Flow Matching for Property-Guided Conditional Molecular Generation.
This tool helps computational chemists and materials scientists design new molecules with specific desired properties. By inputting target property values (like atomization energy or electronic spatial extent), it generates 3D molecular structures. It's for researchers who need to efficiently explore chemical space and discover novel compounds tailored to particular applications.
Use this if you need to generate novel molecular structures that are predicted to exhibit specific chemical or physical properties.
Not ideal if you are looking to predict properties of existing molecules or optimize known structures rather than generate new ones.
Stars
53
Forks
8
Language
Python
License
MIT
Category
Last pushed
Jan 23, 2026
Commits (30d)
0
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