DeepCombDTI

DeepCombDTI is a model for drug-target interaction prediction using deep learning and various molecular fingerprints.

Overview

Dataset

Molecular fingerprints

Prediction

import sys, warnings
warnings.filterwarnings('ignore')

import DeepCombDTI
from DeepCombDTI.DeepCombDTI import Drug_Target_Prediction
from DeepCombDTI.utils import get_args, get_params, run_validation, run_prediction

Using TensorFlow backend.

def predict(dti_num, test_dti):
    # get parameters from arguments
    sys.argv = [
        'DeepCombDTI.py',
        f'data/training/training_dti/{dti_num}.csv',
        'data/training/training_drug.csv',
        'data/training/training_protein.csv',
        '-i', f'data/test/{test_dti}.csv',
        '-d', 'data/test/test_drug.csv',
        '-t', 'data/test/test_protein.csv',
        '-V', 'mol2vec', 'neural_fp', 'seq2seq', '-L', '300', '265', '256', '-c', '150,37', '132,66,33', '128,64,32,16',
        '-v', 'protvec', '-l', '100', '-p', '50,25,12', '50,25', '50,25', '-f', '49,24', '41,20', '41,20',
        '-e', '32', '-y', '0.0001', '-a', 'elu', '-D', '0.0', '-b', '32', '-pt', False,
        '-m', 'model/0/mol_neu_seq_150,37_132,66,33_128,64,32,16_pro_50,25,12_50,25_50,25_fc_49,24_41,20_41,20',
        '--predict', '-n', 'PubChem'
    ]
    args = get_args()
    train_dic, test_dic, train_params, type_params, model_params, output_file = get_params(args)

    # construct DTI prediction model
    dti_prediction_model = Drug_Target_Prediction(**model_params)

    # run prediction
    run_prediction(dti_prediction_model, train_params, output_file, train_dic, test_dic)

test_dtis = ['test_dti_all', 'test_dti_unseen_drug', 'test_dti_unseen_prot', 'test_dti_unseen_both']
for test_dti in test_dtis:
    predict(0, test_dti)

=====================================================
Model parameters summary
=====================================================
drug_layers_list     : [[150, 37], [132, 66, 33], [128, 64, 32, 16]]
protein_layers_list  : [[50, 25, 12], [50, 25], [50, 25]]
fc_layers_list       : [[49, 24], [41, 20], [41, 20]]
learning_rate        : 0.0001    
decay                : 0.0001    
activation           : elu       
dropout              : 0.0       
model_output         : model/0/mol_neu_seq_150,37_132,66,33_128,64,32,16_pro_50,25,12_50,25_50,25_fc_49,24_41,20_41,20
drug_vecs            : ['mol2vec', 'neural_fp', 'seq2seq']
drug_lens            : [300, 265, 256]
prot_vec             : protvec   
prot_len             : 100       
=====================================================
Parsing data/test/test_dti_all.csv, data/test/test_drug.csv
	Positive data : 18228
	Negative data : 18228
Prediction
	Predction of PubChem
	Area Under ROC Curve(AUC): 0.917
	Area Under PR Curve(AUPR): 0.926
=================================================
Prediction is completed.

=====================================================
Model parameters summary
=====================================================
drug_layers_list     : [[150, 37], [132, 66, 33], [128, 64, 32, 16]]
protein_layers_list  : [[50, 25, 12], [50, 25], [50, 25]]
fc_layers_list       : [[49, 24], [41, 20], [41, 20]]
learning_rate        : 0.0001    
decay                : 0.0001    
activation           : elu       
dropout              : 0.0       
model_output         : model/0/mol_neu_seq_150,37_132,66,33_128,64,32,16_pro_50,25,12_50,25_50,25_fc_49,24_41,20_41,20
drug_vecs            : ['mol2vec', 'neural_fp', 'seq2seq']
drug_lens            : [300, 265, 256]
prot_vec             : protvec   
prot_len             : 100       
=====================================================
Parsing data/test/test_dti_unseen_drug.csv, data/test/test_drug.csv
	Positive data : 7740
	Negative data : 15404
Prediction
	Predction of PubChem
	Area Under ROC Curve(AUC): 0.852
	Area Under PR Curve(AUPR): 0.784
=================================================
Prediction is completed.

=====================================================
Model parameters summary
=====================================================
drug_layers_list     : [[150, 37], [132, 66, 33], [128, 64, 32, 16]]
protein_layers_list  : [[50, 25, 12], [50, 25], [50, 25]]
fc_layers_list       : [[49, 24], [41, 20], [41, 20]]
learning_rate        : 0.0001    
decay                : 0.0001    
activation           : elu       
dropout              : 0.0       
model_output         : model/0/mol_neu_seq_150,37_132,66,33_128,64,32,16_pro_50,25,12_50,25_50,25_fc_49,24_41,20_41,20
drug_vecs            : ['mol2vec', 'neural_fp', 'seq2seq']
drug_lens            : [300, 265, 256]
prot_vec             : protvec   
prot_len             : 100       
=====================================================
Parsing data/test/test_dti_unseen_prot.csv, data/test/test_drug.csv
	Positive data : 1464
	Negative data : 6205
Prediction
	Predction of PubChem
	Area Under ROC Curve(AUC): 0.910
	Area Under PR Curve(AUPR): 0.794
=================================================
Prediction is completed.

=====================================================
Model parameters summary
=====================================================
drug_layers_list     : [[150, 37], [132, 66, 33], [128, 64, 32, 16]]
protein_layers_list  : [[50, 25, 12], [50, 25], [50, 25]]
fc_layers_list       : [[49, 24], [41, 20], [41, 20]]
learning_rate        : 0.0001    
decay                : 0.0001    
activation           : elu       
dropout              : 0.0       
model_output         : model/0/mol_neu_seq_150,37_132,66,33_128,64,32,16_pro_50,25,12_50,25_50,25_fc_49,24_41,20_41,20
drug_vecs            : ['mol2vec', 'neural_fp', 'seq2seq']
drug_lens            : [300, 265, 256]
prot_vec             : protvec   
prot_len             : 100       
=====================================================
Parsing data/test/test_dti_unseen_both.csv, data/test/test_drug.csv
	Positive data : 1464
	Negative data : 4980
Prediction
	Predction of PubChem
	Area Under ROC Curve(AUC): 0.914
	Area Under PR Curve(AUPR): 0.826
=================================================
Prediction is completed.

References

• https://github.com/GIST-CSBL/DeepConv-DTI