Training Classifier Dataset about deeppocket HOT 8 CLOSED

+1

I have the same question. This issue needs to be clarified before I cite.

from deeppocket.

The coordinates in these files are pocket centers found using fpocket. If the pocket center is within 4 angstrom of any ligand atom it is labelled as 1, else 0.

from deeppocket.

Sorry but I couldn't get the your sentence "If the pocket center is within 4 angstrom of any ligand atom it is labelled as 1, else 0."

Are you using extra ligand files (sdf) to label the pocket coordinates as 1 or 0 after running fpocket? So how do you decide which coordinates you get after running Fpocket are 0? Are you using another tool for this?

from deeppocket.

Yes the ligands are present in separate sdf files in these datasets (for coach420 and holo4k i have provided extracted ligand files). To detect if the coordinate is within 4 angstrom, you can write a simple python script.

from deeppocket.

see "make_types.py" for example python script on how to do this

from deeppocket.

Thank you for the reply.

95% of the dataset is non-pocket (0), 5% of the dataset is pocket (1). Do you do anything to prevent overfitting or do you use the dataset in this way for your training? I couldn't find the place where this part is mentioned in the paper.

from deeppocket.

positive samples are oversampled during training

from deeppocket.

closing this, feel free to reopen with further issues

from deeppocket.