How can I use Deepfusion to predict the binding affinity of a new complex
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- Dominant language
- Python
- Stars
- 92
- Forks
- 31
- PR merge metrics
- No merged PRs in 30d
Description
Hi,
It seems that the code here can only evaluate the model on PDBBind. How can I use the code to predict the binding affinity of a new complex not in PDBBind? If you can provide a script or any other methods, it would be very helpful. Thanks.
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First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by tracing how the repository evaluates Deepfusion on PDBBind and identify the entry point that consumes a complex. Determine what inputs a new complex requires, then document or expose a reproducible method for running affinity prediction outside PDBBind; done means a user can follow the provided method for a new complex.
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Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100