Drug Target Binding Affinity (DTBA) Model¶

class DeepPurpose.models.DBTA

Drug Target Binding Affinity (DBTA) (Source) include all component, including drug encoder, target encoder and classifier/regressor.

constructor create an instance of DBTA.

__init__(self, **config)

config (kwargs, keyword arguments) - specify the parameter of DBTA.
- drug_encoding (str) - Encoder mode for drug. It can be “transformer”, “MPNN”, “CNN”, “CNN_RNN” …,
- target_encoding (str) - Encoder mode for protein. It can be “transformer”, “CNN”, “CNN_RNN” …,
- result_folder (str) - directory that store the learning log/results.
- concrete parameter for encoder model (repeated)

test_ include all the test procedure.

test_(self, data_generator, model, repurposing_mode = False, test = False):

data_generator (iterator) - iterator of torch.utils.data.DataLoader. It can be test data or validation data.
model (DeepPurpose.models.Classifier) - model of DBTA.
repurposing_mode (bool) - If repurposing_mode is True, then do repurposing. Otherwise, do compute the accuracy (including AUC score).
test (bool) - If test is True, plot ROC-AUC and PR-AUC curve. Otherwise, pass.

train include all the training procedure.

train(self, train, val, test = None, verbose = True)

train (torch.utils.data.dataloader) - Train data loader
val (torch.utils.data.dataloader) - Valid data loader
test (torch.utils.data.dataloader) - Test data loader
verbose (bool) - If verbose is True, then print training record every 100 iterations.

predict include all the inference procedure.

predict(self, df_data)

save_model save the well-trained model to specific directory.

save_model(self, path_dir)

load_pretrained load the well-trained model so that we are able to make inference directly and don’t have to train model from scratch.

load_pretrained(self, path)