tensorflow / tensorflow/datasets
[data request] BBBP(Blood-brain barrier penetration)
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Description
- Name of dataset: BBBP
- URL of dataset: http://moleculenet.ai/datasets-1
- License of dataset: MIT
- Short description of dataset and use case(s): The Blood-brain barrier penetration (BBBP) dataset comes from a recent study 52 on the modeling and prediction of the barrier permeability. As a membrane separating circulating blood and brain extracellular fluid, the blood-brain barrier blocks most drugs, hormones and neurotransmitters. Thus penetration of the barrier forms a long-standing issue in development of drugs targeting central nervous system. This dataset includes binary labels for over 2000 compounds on their permeability properties. Scaffold splitting is also recommended for this well-defined target.
Folks who would also like to see this dataset in tensorflow/datasets, please thumbs-up so the developers can know which requests to prioritize.
And if you'd like to contribute the dataset (thank you!), see our guide to adding a dataset.
I think I could implement this dataset, following the guideline.
After this one, I would like to implement all other datasets in MoleculeNet(Tox21, HIV, etc)
May I proceed to contribute?
ps. Certainly, there are none of the categories for the molecule/physiology dataset. I suggest that register this dataset with a new category - physiology
Contributor guide
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 with docs/add_dataset.md and the MoleculeNet BBBP dataset page linked in the issue. Confirm the dataset details and MIT license, then follow the contribution guide to add BBBP to tensorflow/datasets. Done means the BBBP dataset is available in the repository according to the guide.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, tensorflow
- Domain
- data, machine-learning
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100