Comments (3)
Dear Dimitrios,
Yes, this makes sense and it is one way to go.
Another way would be separating a set of compounds as test set and using them only for testing to see if the model can predict completely unseen compounds.
Best
from deepscreen.
Dear Dimitros,
Thank you for your interest in DEEPScreen and pointing out this issue. Yes, this should be corrected as it may lead overfitting. PyTorch implementation of DEEPScreen is new and it is still going on. This was just a code hack for me for the datasets that have only test samples without the validation dataset. I forgot to change it and sorry for the inconvenience. I will upload the correct version.
The original DeepScreen implementation (paper version) was on tflearn. It does not have this issue so you can have look from the link below:
https://github.com/cansyl/DEEPScreen/blob/master/bin/trainDEEPScreen.py
We are planning to release the Pytorch version soon. In its current state; datasets have been updated (ChEMBL v28) for the PyTorch version, different bioactivity thresholds is being tried, initial implementation with a simple CNN model has finished. However, it is still under development and another colleague is now working on these improvements. We are planning to release the new implementation, models and a hopefully web server soon.
--
Ahmet
from deepscreen.
Dear Ahmet,
Thank you for the quick response and the clarification. I am quite interested in the multi-task formulation of the problem (basically treat the different protein targets as tasks).
By doing that, I can compare the single-task models (what is done in this implementation and the corresponding paper) with an approach that trains with all targets simultaneously. The current train-val-test splits that you offer have compound overlaps between the train and test sets of different targets (for example a compound can be in the training set of target A and the test set of target B). So, if I want to create a multi-task version of the dataset I end up with compounds that show up in both the train and test sets. Does it make sense to create a dataset (see right figure) by combining all the interactions and then randomly split at a specific compound (see right figure)??
Thanks,
Dimitrios
from deepscreen.
Related Issues (14)
- ValueError: cannot reshape array of size 13797420 into shape (200,200,1) HOT 26
- How to define “bioactivity values” ? HOT 1
- training model _ another species HOT 1
- DEEPScreen Supporting Data for Output/results HOT 1
- New compound prediction - not in CHEMBL and doesn't have a CHEMBL_ID HOT 1
- How do I use a pre-trained model to generate predictions? HOT 1
- test_threshold problem and zip file problem HOT 7
- Receiving "There was a problem during..." Error HOT 7
- How to screen for drugs with a protein not found in the file? HOT 9
- How to predict DTI of new compounds with trained protein models? HOT 10
- UnboundLocalError: local variable 'best_test_performance_dict' referenced before assignment HOT 4
- Error for running HOT 6
- Dataset in the Code HOT 2
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