BioinfoMachineLearning/GCPNet
A geometry-complete SE(3)-equivariant perceptron network (GCPNet) for 3D graphs. (Bioinformatics)
This project offers a sophisticated AI model for analyzing and predicting properties of 3D molecular structures. It takes detailed information about the geometry of molecules, such as protein structures or drug compounds, and outputs predictions on their binding affinities, stability, or other relevant characteristics. Researchers in drug discovery, materials science, or biochemistry can use this to understand molecular interactions.
No commits in the last 6 months.
Use this if you need to accurately predict how different 3D molecular structures will behave or interact, such as determining ligand-binding affinity or ranking protein structures.
Not ideal if your primary goal is generating new molecular structures or if you require only simple, non-geometric analysis of molecular data.
Stars
52
Forks
5
Language
Python
License
MIT
Category
Last pushed
Apr 20, 2025
Commits (30d)
0
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