Conditional generation
Nobody has claimed this yet.
Assessment
- Difficulty
- 5/5
- Estimated time
- Over a week
- Newbie friendliness
- 20/100
- Issue type
- Feature
- Clarity
- Needs clarification
- Activity status
- Stale
- Tech stack
- jupyter-notebook
- Domain
- bioinformatics, machine-learning
Research direction
The issue does not name any files, tests, or entry points. Start by reading the FoldingDiff implementation and documentation to determine whether conditioning on an amino acid sequence is supported; the requested changes and completion criteria are not defined.
Written by the indexing model from the issue text.
Description
Thanks to the author for sharing, this is the wonderful work.
I'd like to ask you a question. I want to achieve the conditional generation of the three-dimensional structure of the protein. For example, input the target protein amino acid sequence, generate the corresponding amino acid sequence of the protein three-dimensional structure. Can condition generation be realized by modifying Foldingdiff? Can you briefly explain how to modify it? I would appreciate it if you could give me some help.
- Dominant language
- Jupyter Notebook
- Stars
- 568
- Forks
- 74
- Avg merge
- 20h 2m
- Merged PRs (30d)
- 1
Contributor guide
First steps
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- Open a pull request that references the issue number.
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