Comments (2)
The approach we recommend is to generate many samples (100-1000) and then filter the molecules based on some criteria such as drug-likeness. You can also get more realistic molecules by reducing the variability factor, though they tend to be more similar to the reference molecule.
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Thank you very much for your kind reply.
Bests
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Related Issues (20)
- AttributeError: type object 'object' has no attribute 'dtype' HOT 9
- where is the output for generated molecules and matrix? HOT 4
- gnina minmize problem HOT 2
- Can not access to https://bits.csb.pitt.edu/files/ to download those files HOT 2
- Segmentation Fault when running tests HOT 1
- tests ValueError: File does not exist: data/test_pockets/xxx HOT 3
- change configuration file for prior (no lig) sampling HOT 1
- Error when run cmake of libmogrid HOT 4
- Errors when run pytest tests HOT 12
- how I can export embeddings HOT 3
- Get Stuck when runing generate.py HOT 2
- Train_file and test_file are missing HOT 1
- CUDNN Error (out of memory?): CUDNN_STATUS_EXECUTION_FAILED HOT 2
- Get killed when running generate.py HOT 5
- Cannot use model with `fit_atoms: True` HOT 8
- Changing parameters while testing the model HOT 6
- KeyError: 'minimizedAffinity' in tests HOT 6
- Environment.yml isnt complete and libmolgrid git command fails HOT 3
- Changes required to train the CGAN model
- Data link failure
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