Degiacomi-Lab/molearn
protein conformational spaces meet machine learning
This package helps structural biologists and computational chemists design and train machine learning models to generate realistic protein conformations. You input experimental or simulation data of protein dynamics, and it outputs a model capable of producing new, valid protein structures. This tool is for researchers working on protein folding, dynamics, or drug discovery who need to explore conformational spaces efficiently.
Use this if you need to build machine learning models that can generate protein structures from existing experimental or simulation data.
Not ideal if you are looking for an off-the-shelf solution for predicting protein structures without building and training your own generative model.
Stars
50
Forks
18
Language
Python
License
GPL-3.0
Category
Last pushed
Mar 26, 2026
Commits (30d)
0
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