Comments (12)
That's weird, I must have stored the wrong model. Let me double-check and I will upload the correct model.
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Hey it seems this model is wrong. You can use "MPNN_CNN_BindingDB_IC50" instead. It is trained on a much larger training set (~10^5 -> 10^6) and should have higher quality. Do note that the units now switches from Kd to IC50.
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Did you use the latest BindingDB to train this model?
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Hey, it is using the past version 2020m2. There should be some minor difference with the current most up to date version regarding the number of training points.
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Thank you for your reply 👍🏽 Please let me know if you want the trained MPNN_CNN on BindingDB using Kd.
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No problem! Did you mean you are managed to train the model? If so, would be great to share with me ([email protected]), thanks!
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You can simply use the model.save('XXX') function and then send me the model file; i will upload to the server and update the link, thanks again!
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Hi Kexin,
It seems that the pre-trained model MPNN_CNN downloaded using pretrained_dir = download_pretrained_model('pretrained_models')
in the oneliner.py still showing the old configuration:
{'input_dim_drug': 1024,
'input_dim_protein': 8420,
'hidden_dim_drug': 128,
'hidden_dim_protein': 256,
'cls_hidden_dims': [1024, 1024, 512],
'batch_size': 16,
'train_epoch': 1,
'LR': 0.001,
'drug_encoding': 'MPNN',
'target_encoding': 'CNN',
'result_folder': './result/',
'binary': False,
'mpnn_hidden_size': 128,
'mpnn_depth': 3,
'cnn_target_filters': [32, 64, 96],
'cnn_target_kernels': [4, 8, 12]}
Maybe you need to update the model file on the https://dataverse.harvard.edu/api/access/datafile/
Maybe the configure files corresponding to pretrained_dir = download_pretrained_model('models_configs')
also need a update.
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Sounds good, do you want to contribute and train a new model for it?
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I'd like to have a try. Could you please give me the dataset of BindDB Kd? And what preproccess or data cleaning is needed before I start the train?
Subsequent help may be needed since I am a complete newbie for ML :)
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Sounds good, it should be the one in the https://github.com/kexinhuang12345/DeepPurpose/blob/master/DEMO/Transformer%2BCNN_BindingDB.ipynb
simply replacing the model and parameter should be good
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Thank you for your fruitful discussion and big thank-you to the developers of this library. My question is:
In the latest release od DeepPurpose, was the MPNN_CNN model corrected and it works fine now?
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Related Issues (20)
- package nbconvert-core-7.2.1-pyhd8ed1ab_0 requires jinja2 >=3.0, but none of the providers can be installed - package nbclient-0.7.0-pyhd8ed1ab_0 requires jupyter_client >=6.1.5, but none of the providers can be installed - nothing provides importlib-metadata >=4.7.0,<4.7.1.0a0 needed by importlib_metadata-4.7.0-hd8ed1ab_0 - package libsqlite-3.39.4-h753d276_0 requires libzlib >=1.2.12,<1.3.0a0, but none of the providers can be installed HOT 1
- Possible typo in the initialization of the MPNN encoder HOT 1
- Error Message when Using Custom Data
- Question about data format for prediciton HOT 1
- The GNN model does not work properly HOT 3
- Error while downloading BindingDB HOT 1
- DGL_GCN-Transformer cannot work. HOT 1
- Accuracy of BindingDB pre-trained models HOT 1
- Query Regarding MPNN Drug Encoder HOT 3
- BindingDB: Amino acid sequences and y_pred values HOT 2
- Query Regarding y_pred values
- running on GPU HOT 1
- Dataset - def process_Binding_DB - extra space on "BindingDB Target Chain Sequence"
- Install ERROR: Could not find a version that satisfies the requirement rdkit (from descriptastorus) HOT 1
- validation curve plot
- duplicate issue
- Confusion matrix
- The meaning of the score in the document 'toy_data/ppi.txt'
- error when using TDC dataset to predict DTI
- HTTPError
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