Comments (5)
Hi! Yes, we are considering this as a part of our future release. May I know what pretrained models you would prefer to have?
from torchdrug.
I'm not an expert in this field but I work adjacent to it. So I don't have a great grasp on SOTA models that would serve folks best. Papers with code has a drug discovery task leaderboard.
I think any reasonably well-benchmarked models trained on the largest datasets available would provide the most utility. Then again there are other non-graph based representation learning models that work quite well. Would be happy to continue the conversation.
from torchdrug.
Thanks! The leaderboard seems to be a good place to find SOTA models.
We would probably start from existing pretraining models in this platform, try to pretrain them on large datasets, and then move on to newer models.
from torchdrug.
I think that is a good place to start. It would be interesting and useful to get a benchmark going with the models right here. I would be interested to see the effect of dataset & model scaling for the available methods. I'm sure some of the methods have done these experiments but nonetheless in this new era of pretrained models it is useful information.
from torchdrug.
Hi! Any updates in this regard?
from torchdrug.
Related Issues (20)
- Question about the swissprot data size used in alphafolddb.py
- Adding interactive examples
- Read-only file system: for importing models
- torch.nn.Module Memory Address Changes After Importing torchdrug
- torch_ext.so cannot be found error on Macbook macOS HOT 2
- Issue converting PDB file to torchdrug.data.PackedProtein / Protein HOT 1
- Passing CIF file directly into TorchDrug Protein?
- To load huge dataset, and train the model
- example for tasks.MultipleBinaryClassification HOT 2
- Statistics and data splitting scheme on PPI datasets HOT 2
- Nan error while using GraphAF to optimize QED
- Small issue in atom2valence dictionary iteration in `Molecule.is_valid`
- torchdrug.patch.py breaks use of ConcatDataset HOT 1
- [Note] Dead lock when running `layers.GraphIsomorphismConv` (issue located in `sparse_coo_tensor`)
- graph with global features, and mydataset
- atom_feature="symbol" is only available for class GCPNGeneration() ? HOT 1
- class Molecule(Graph), definition of "self.atom2valence"
- TypeError during validation
- Segmentation fault HOT 1
- issues with reproducing pLogP benchmark for GPCN
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from torchdrug.