text: import os
  os.chdir('../')
  import DeepPurpose.DTI as models
  from DeepPurpose.utils import *
  from DeepPurpose.dataset import *
  from DeepPurpose import utils, dataset
  import DeepPurpose.DTI as models
  
  from time import time
  
  t1 = time()
  
  path_to_text_file = '/content/drive/MyDrive/Colab Notebooks/data/NPASS_for_DeepPurpose.txt'
  
  X_drugs, X_targets, y = dataset.read_file_training_dataset_drug_target_pairs(path_to_text_file)
  
  print(X_drugs[:5])
  print(X_targets[:5])
  print(y[:5])
  
  drug_encoding = 'MPNN'
  target_encoding = 'CNN'
  train, val, test = data_process(X_drugs, X_targets, y, 
                                  drug_encoding, target_encoding, 
                                  split_method='random',frac=[0.85,0.1,0.05])
  
  config = generate_config(drug_encoding = drug_encoding, 
                           target_encoding = target_encoding, 
                           cls_hidden_dims = [1024,1024,512], 
                           train_epoch = 100,  
                           LR = 0.001, 
                           batch_size = 32,
                           hidden_dim_drug = 128,
                           mpnn_hidden_size = 128,
                           mpnn_depth = 3, 
                           cnn_target_filters = [32,64,96],
                           cnn_target_kernels = [4,8,12],
                           result_folder = "/content/drive/MyDrive/Colab Notebooks/DeepPurpose_results/NPASS_MPNN_2"
                          )
  
  
  model = models.model_initialize(**config)
  t2 = time()
  print("cost about " + str(int(t2-t1)) + " seconds")
  model.train(train, val, test)
  
  model.save_model('/content/drive/MyDrive/Colab Notebooks/models/NPASS_MPNN_2')
  