Comments (5)
Hi @ngszyba,
What @rmwu is absolutely correct.
The first 16 values are the "input features" consisting of 10 mean and gaussian curvatures at different scales + 6 learned chemical features, then there come 16 values which are the point descriptors after the convolution ("embedding"), then there are interface predictions (1 value) and interface labels (1 value) if there are any
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Not sure if the authors are still replying but you can find the information that it saves in data_iteration.py
line 94
coloring = torch.cat([inputs, embedding, predictions, labels], axis=1)
Where inputs
is computed from line 358 of model.py
and embedding
is the output from their model, computed at line 413 of the same file. If I recall correctly, embedding is only dim=16 or so.
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Hi, @rmwu
Thanks so much for such a quick answer and pointing towards data_iteration.py and model.py.
This is already a great hint. I assume that the columns could be deciphered in this section of the model.py (line 481) :
"P1": P1,
"P2": P2,
"R_values": R_values,
"conv_time": conv_time,
"memory_usage": memory_usage,
However, I still find it pretty difficult to get whatever is packed into P1 and P2.
It would be great to get a feature list with a brief description e.g. is time in seconds?, etc.
cheers,
from dmasif.
Thanks for your interest in our work!
Just a quick word to say that we are still replying but:
- Unfortunately, it’s hard to be as reactive as we would like to be (e.g. I’m now working mostly on public health problems, quite far away from proteins, and am discovering the joys of paperwork as a “tenured/senior” researcher…).
- I’m only sharp on low-level problems related to e.g. KeOps or the convolutional layers.
- @FreyrS is by far the sharpest person on the global structure of the dMaSIF repository, but he may be very busy at the moment - plus his PhD is coming to an end soon.
I have lost track of the structure of our files, but an important thing to note is that our dMaSIF model produces trained ”neural” features that do not have a clear physical meaning. P1 and P2 just contain “one vector of trained features per surface point”, where our feature extractor has been optimized with respect to a certain task (see our three papers on this: Nature methods, CVPR and MLDD). The lack of interpretability is unavoidable here, even though we’re trying to mitigate this as much as possible with our architectures.
I hope that @FreyrS will be able to confirm that predfeatures_emb1.npy
does indeed contain the output of our convolutional layers :-)
Best regards,
Jean
from dmasif.
Hi @jeanfeydy!
Thanks for your input!
I understand that interpretation of these features in an "absolute" manner is not advisable. In our project we are focusing on side-by-side comparisons of the surface features and projecting it on other protein properties like enzymatic activity and we wanted to give dMaSIF a shot.
Would be great if @FreyrS can come back with the column/features list at a later time.
thank you guys,
cheers!
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Related Issues (20)
- about ply files HOT 1
- main_inference result analysis HOT 1
- If i have duplicate pdb 1NPU and 1NPU.can you provide a example of main_inferency.py for masif_site and masif search
- RuntimeError: legacy constructor expects device type: cpubut device type: cuda was passed
- How to perform massif-ligand "type" tasks with this framework.
- Choice of the atom RADII HOT 1
- How to predict the interface characters of single protein? HOT 1
- dMaSIF for interaction prediction, how to find complementary regions? HOT 2
- No save model checkpoints for site prediction HOT 2
- Input seems to require a y value HOT 2
- Running you example does not provide same ROC-AUC as claimed in the paper HOT 14
- Using main_training.py
- Problems regarding loss function and auroc
- Does anyone have a docker image that can run this project? Can you share it with me? HOT 4
- How to replace with my own dataset
- why the interface is computed like this ? HOT 4
- About helper.py
- Pre-trained model in Hugging face
- [Question] Are ligands or PTMs considered
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