Create LCIdb and Train/Val/Test in different prediction scenarii
Preprocessing
Download the file ‘Consensus_CompoundBioactivity_Dataset_v1.1.csv’ from https://zenodo.org/record/6398019#.Y6A4nrKZPn4
[ ]:
from komet import process_LCIdb
LCIdb = process_LCIdb('Consensus_CompoundBioactivity_Dataset_v1.1.csv', data_dir = "./", max_length_fasta = 1000, bioactivity_choice = "checkand1database",min_weight = 100, max_weight = 900, interaction_plus = 1e-7, interaction_minus = 1e-4)
Create Train/Val/Test in different prediction scenarii
Random (S1)
Unseen_drugs (S2)
Unseen_targets (S3)
Orphan (S4)
[1]:
%load_ext autoreload
%autoreload 2
[2]:
%load_ext autoreload
%autoreload 2
import matplotlib.pyplot as plt
import numpy as np
from sklearn import svm
import pandas as pd
The autoreload extension is already loaded. To reload it, use:
%reload_ext autoreload
Load LCIdb
The dataset can be downloaded in Zenodo \url{https://zenodo.org/records/10731713} as LCIdb_v1.1.csv.
[7]:
base_name_p = 'LCIdb_v1.1.csv'
df_p = pd.read_csv(base_name_p,low_memory=False)
df_p.head()
[7]:
| smiles | fasta | ChEMBL ID | PubChem ID | IUPHAR ID | Ligand names | Target | uniprot | mean | mean pIC50 | ... | min pIC50 | min pKi | min pKd | max | max pIC50 | max pKi | max pKd | score | indsmiles | indfasta | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | BrC1CCN(Cc2ccc(OCCCN3CCCCC3)cc2)CC1 | MERAPPDGPLNASGALAGEAAAAGGARGFSAAWTAVLAALMALLIV... | CHEMBL472466 | 44592131.0 | NaN | 4-bromo-1-(4-(3-(piperidin-1-yl)propoxy)benzyl... | hrh3 | Q9Y5N1 | 9.7 | NaN | ... | NaN | 9.7 | NaN | 9.7 | NaN | 9.7 | NaN | 1.0 | 0.0 | 0.0 |
| 1 | BrCC(Br)C1CCC(Br)C(Br)C1 | MEVQLGLGRVYPRPPSKTYRGAFQNLFQSVREVIQNPGPRHPEAAS... | CHEMBL375107 | 18728.0 | NaN | 1,2-dibromo-4-(1,2-dibromoethyl)cyclohexane\nn... | ar | P10275 | 7.4 | 7.4 | ... | 7.4 | NaN | NaN | 7.4 | 7.4 | NaN | NaN | 1.0 | 1.0 | 1.0 |
| 2 | BrC[C@H]1CC[C@H](c2nnn3cnc4[nH]ccc4c23)CC1 | MAPPSEETPLIPQRSCSLLSTEAGALHVLLPARGPGPPQRLSFSFG... | CHEMBL3918308 | 136641870.0 | NaN | us9216999, 74 | jak3 | P52333 | 7.1 | 7.1 | ... | 7.1 | NaN | NaN | 7.1 | 7.1 | NaN | NaN | 1.0 | 2.0 | 2.0 |
| 3 | BrC[C@H]1CC[C@H](c2nnn3cnc4[nH]ccc4c23)CC1 | MGMACLTMTEMEGTSTSSIYQNGDISGNANSMKQIDPVLQVYLYHS... | CHEMBL3918308 | 136641870.0 | NaN | us9216999, 74 | jak2 | O60674 | 8.5 | 8.5 | ... | 8.5 | NaN | NaN | 8.5 | 8.5 | NaN | NaN | 1.0 | 2.0 | 3.0 |
| 4 | BrC[C@H]1CC[C@H](c2nnn3cnc4[nH]ccc4c23)CC1 | MPLRHWGMARGSKPVGDGAQPMAAMGGLKVLLHWAGPGGGEPWVTF... | CHEMBL3918308 | 136641870.0 | NaN | us9216999, 74 | tyk2 | P29597 | 8.6 | 8.6 | ... | 8.6 | NaN | NaN | 8.6 | 8.6 | NaN | NaN | 1.0 | 2.0 | 4.0 |
5 rows × 23 columns
Drugs
[9]:
#list of smiles strings
dict_ind2smiles = df_p[['indsmiles','smiles']].set_index('indsmiles').to_dict()['smiles']
#Sort the indexes according to the ind2mol
dict_ind2smiles = {k:dict_ind2smiles[k] for k in sorted(dict_ind2smiles.keys())}
print(len(dict_ind2smiles))
dict_smiles2ind = {v:k for k,v in dict_ind2smiles.items()}
smiles = list(dict_ind2smiles.values())
print("# drugs",len(smiles))
274515
# drugs 274515
Proteins
[10]:
#list of fasta strings
dict_ind2fasta = df_p[['indfasta','fasta']].set_index('indfasta').to_dict()['fasta']
# Sort the indexes according to the ind2fasta
dict_ind2fasta = {k:dict_ind2fasta[k] for k in sorted(dict_ind2fasta.keys())}
print(len(dict_ind2fasta))
dict_fasta2ind = {v:k for k,v in dict_ind2fasta.items()}
fastas = list(dict_ind2fasta.values())
print("# targets ",len(fastas))
2069
# targets 2069
number of interactions
[11]:
df_p["score"].value_counts()
[11]:
1.0 402538
0.5 265006
0.0 8296
Name: score, dtype: int64
list of target/drug indices with only interaction 1
[12]:
# targets indices
J = df_p['indfasta'].values
print(len(J))
# drugs indices
I = df_p['indsmiles'].values
print(len(I))
J
676125
676125
[12]:
array([ 0., 1., 2., ..., 180., 87., 50.])
Make train/val/test Random (S1)
[14]:
from komet import make_train_test_val_S1
train, test, val = make_train_test_val_S1(df_p,0.8,0.12)
test.shape[0]/train.shape[0], val.shape[0]/train.shape[0]
train 322030 test 48304 val 32204
train (644060, 3)
nb of interactions + in test 48304
number of real interactions - in test 6664
number of interactions - in test 48304
test (96608, 3)
nb of interactions + in val 32204
number of real interactions - in val 0
number of interactions - in val 32204
val (64408, 3)
Train/test/val datasets prepared.
[14]:
(0.1499984473496258, 0.10000310530074838)
[15]:
print("number of proteins in train", len(set(train[:,0])))
print("number of proteins in test", len(set(test[:,0])))
print("number of proteins in val", len(set(val[:,0])))
print("*"*50)
print("number of drugs in train", len(set(train[:,1])))
print("number of drugs in test", len(set(test[:,1])))
print("number of drugs in val", len(set(val[:,1])))
print("*"*50)
print("number of interactions in train", train.shape[0])
print("number of interactions in test", test.shape[0])
print("number of interactions in val", val.shape[0])
print("*"*50)
print("number of interactions + in train", train[:,2].sum())
print("number of interactions + in test", test[:,2].sum())
print("number of interactions + in val", val[:,2].sum())
print("*"*50)
print("number of interactions - in train", train.shape[0]-train[:,2].sum())
print("number of interactions - in test", test.shape[0]-test[:,2].sum())
print("number of interactions - in val", val.shape[0]-val[:,2].sum())
number of proteins in train 2015
number of proteins in test 2069
number of proteins in val 2069
**************************************************
number of drugs in train 231276
number of drugs in test 79170
number of drugs in val 57272
**************************************************
number of interactions in train 644060
number of interactions in test 96608
number of interactions in val 64408
**************************************************
number of interactions + in train 322030
number of interactions + in test 48304
number of interactions + in val 32204
**************************************************
number of interactions - in train 322030
number of interactions - in test 48304
number of interactions - in val 32204
Make a dataframe with smiles/fasta to save in .csv
[16]:
# dataframes
df_train = pd.DataFrame(train,columns=['indfasta','indsmiles','label'])
print("train : #target/drugs : ",df_train["indfasta"].nunique(),df_train["indsmiles"].nunique())
df_test = pd.DataFrame(test,columns=['indfasta','indsmiles','label'])
print("test : #target/drugs : ",df_test["indfasta"].nunique(),df_test["indsmiles"].nunique())
df_val = pd.DataFrame(val,columns=['indfasta','indsmiles','label'])
print("val : #target/drugs : ",df_val["indfasta"].nunique(),df_val["indsmiles"].nunique())
# add smiles and fasta to the dataframes
df_train_S1 = df_train.merge(df_p[["indfasta","fasta"]].drop_duplicates(),on="indfasta")
df_train_S1 = df_train_S1[["indsmiles","fasta","label"]]
df_train_S1 = df_train_S1.merge(df_p[["indsmiles","smiles"]].drop_duplicates(),on="indsmiles")
df_train_S1 = df_train_S1[["smiles","fasta","label"]]
df_train_S1.columns = ["SMILES","Target Sequence","Label"]
df_test_S1 = df_test.merge(df_p[["indfasta","fasta"]].drop_duplicates(),on="indfasta")
df_test_S1 = df_test_S1[["indsmiles","fasta","label"]]
df_test_S1 = df_test_S1.merge(df_p[["indsmiles","smiles"]].drop_duplicates(),on="indsmiles")
df_test_S1 = df_test_S1[["smiles","fasta","label"]]
df_test_S1.columns = ["SMILES","Target Sequence","Label"]
df_val_S1 = df_val.merge(df_p[["indfasta","fasta"]].drop_duplicates(),on="indfasta")
df_val_S1 = df_val_S1[["indsmiles","fasta","label"]]
df_val_S1 = df_val_S1.merge(df_p[["indsmiles","smiles"]].drop_duplicates(),on="indsmiles")
df_val_S1 = df_val_S1[["smiles","fasta","label"]]
df_val_S1.columns = ["SMILES","Target Sequence","Label"]
train : #prot/durgs : 2015 231276
test : #prot/durgs : 2069 79170
val : #prot/durgs : 2069 57272
[17]:
print("train")
df_train['label'].value_counts()
train
[17]:
1 322030
0 322030
Name: label, dtype: int64
[18]:
print("test")
df_test['label'].value_counts()
test
[18]:
1 48304
0 48304
Name: label, dtype: int64
[19]:
print("val")
df_val['label'].value_counts()
val
[19]:
1 32204
0 32204
Name: label, dtype: int64
[ ]:
# for each pair, we look if the protein is in the train and the test
for elt in test:
for x in train:
if elt[0]==x[0]:
print(elt,x)
[21]:
# for each pair, we look if the drug is in the train and the test
for elt in test:
for x in train:
if elt[1]==x[1]:
print(elt,x)
[ 1264 40293 1] [ 186 40293 0]
[ 1264 40293 1] [ 837 40293 1]
[ 207 270858 1] [ 208 270858 1]
[ 207 270858 1] [ 86 270858 0]
[ 25 103530 1] [ 44 103530 1]
[ 25 103530 1] [ 115 103530 0]
[ 25 239743 1] [ 44 239743 1]
[ 25 239743 1] [ 162 239743 0]
[ 120 10888 1] [ 51 10888 1]
[ 120 10888 1] [ 25 10888 0]
[ 120 10888 1] [ 368 10888 0]
[ 120 10888 1] [ 287 10888 1]
[ 194 81092 1] [ 324 81092 0]
[ 194 81092 1] [ 193 81092 1]
[ 28 95164 1] [ 226 95164 0]
[ 28 95164 1] [ 452 95164 1]
[ 566 16455 1] [ 286 16455 0]
[ 566 16455 1] [ 207 16455 0]
[ 566 16455 1] [ 172 16455 0]
[ 566 16455 1] [ 391 16455 0]
[ 566 16455 1] [ 49 16455 1]
[ 566 16455 1] [ 275 16455 1]
[ 566 16455 1] [ 564 16455 1]
[ 566 16455 1] [ 590 16455 1]
[ 1086 191222 1] [ 547 191222 0]
[ 1086 191222 1] [ 1085 191222 1]
[ 129 262869 1] [ 3 262869 0]
[ 129 262869 1] [ 220 262869 0]
[ 129 262869 1] [ 73 262869 0]
[ 129 262869 1] [ 335 262869 0]
[ 129 262869 1] [ 126 262869 1]
[ 129 262869 1] [ 131 262869 1]
[ 129 262869 1] [ 128 262869 1]
[ 129 262869 1] [ 130 262869 1]
[ 296 14122 1] [ 64 14122 0]
[ 296 14122 1] [ 0 14122 0]
[ 296 14122 1] [ 32 14122 0]
[ 296 14122 1] [ 45 14122 0]
[ 296 14122 1] [ 29 14122 0]
[ 296 14122 1] [ 155 14122 0]
[ 296 14122 1] [ 174 14122 0]
[ 296 14122 1] [ 153 14122 0]
[ 296 14122 1] [ 540 14122 0]
[ 296 14122 1] [ 208 14122 0]
[ 296 14122 1] [ 233 14122 0]
[ 296 14122 1] [ 469 14122 0]
[ 296 14122 1] [ 70 14122 0]
[ 296 14122 1] [ 52 14122 1]
[ 296 14122 1] [ 63 14122 1]
[ 296 14122 1] [ 62 14122 1]
[ 296 14122 1] [ 53 14122 1]
[ 296 14122 1] [ 54 14122 1]
[ 296 14122 1] [ 289 14122 1]
[ 296 14122 1] [ 290 14122 1]
[ 296 14122 1] [ 292 14122 1]
[ 296 14122 1] [ 297 14122 1]
[ 296 14122 1] [ 291 14122 1]
[ 296 14122 1] [ 294 14122 1]
[ 296 14122 1] [ 288 14122 1]
[ 296 14122 1] [ 295 14122 1]
[ 224 82657 1] [ 3 82657 0]
[ 224 82657 1] [ 77 82657 1]
[ 224 82657 1] [ 651 82657 0]
[ 224 82657 1] [ 374 82657 1]
[ 171 239644 1] [ 6 239644 0]
[ 171 239644 1] [ 189 239644 0]
[ 171 239644 1] [ 163 239644 1]
[ 171 239644 1] [ 286 239644 0]
[ 171 239644 1] [ 234 239644 0]
[ 171 239644 1] [ 172 239644 1]
[ 171 239644 1] [ 174 239644 0]
[ 171 239644 1] [ 153 239644 0]
[ 171 239644 1] [ 247 239644 0]
[ 171 239644 1] [ 106 239644 0]
[ 171 239644 1] [ 164 239644 1]
[ 171 239644 1] [ 166 239644 1]
[ 171 239644 1] [ 167 239644 1]
[ 171 239644 1] [ 168 239644 1]
[ 171 239644 1] [ 169 239644 1]
[ 171 239644 1] [ 170 239644 1]
[ 171 239644 1] [ 165 239644 1]
[ 171 239644 1] [ 235 239644 0]
[ 873 257102 1] [ 64 257102 0]
[ 873 257102 1] [ 51 257102 1]
[ 873 257102 1] [ 155 257102 0]
[ 873 257102 1] [ 174 257102 0]
[ 873 257102 1] [ 540 257102 0]
[ 873 257102 1] [ 287 257102 1]
[ 873 257102 1] [ 274 257102 1]
[ 873 257102 1] [ 305 257102 1]
[ 873 257102 1] [ 944 257102 0]
[ 873 257102 1] [ 875 257102 1]
[ 8 1342 1] [ 286 1342 0]
[ 8 1342 1] [ 11 1342 1]
[ 8 1342 1] [1081 1342 0]
[ 8 1342 1] [ 9 1342 1]
[ 159 52825 1] [ 368 52825 0]
[ 159 52825 1] [ 789 52825 1]
[ 13 178714 1] [ 187 178714 0]
[ 13 178714 1] [ 179 178714 0]
[ 13 178714 1] [ 494 178714 0]
[ 13 178714 1] [ 500 178714 1]
[ 13 178714 1] [ 14 178714 1]
[ 13 178714 1] [ 15 178714 1]
[ 67 179708 1] [ 220 179708 0]
[ 67 179708 1] [ 286 179708 0]
[ 67 179708 1] [ 307 179708 1]
[ 67 179708 1] [ 136 179708 0]
[ 67 179708 1] [ 582 179708 1]
[ 67 179708 1] [ 586 179708 1]
[ 120 263284 1] [ 64 263284 0]
[ 120 263284 1] [ 51 263284 1]
[ 120 263284 1] [ 55 263284 1]
[ 120 263284 1] [ 1003 263284 0]
[ 208 41986 1] [ 31 41986 0]
[ 208 41986 1] [ 207 41986 1]
[ 208 41986 1] [ 206 41986 1]
[ 208 41986 1] [ 373 41986 0]
[ 172 135262 1] [ 0 135262 0]
[ 172 135262 1] [ 163 135262 1]
[ 172 135262 1] [ 174 135262 0]
[ 172 135262 1] [ 28 135262 0]
[ 172 135262 1] [ 247 135262 0]
[ 172 135262 1] [ 540 135262 0]
[ 172 135262 1] [ 208 135262 0]
[ 172 135262 1] [ 162 135262 1]
[ 172 135262 1] [ 164 135262 1]
[ 172 135262 1] [ 171 135262 1]
[ 172 135262 1] [ 92 135262 0]
[ 172 135262 1] [ 166 135262 1]
[ 172 135262 1] [ 169 135262 1]
[ 172 135262 1] [ 165 135262 1]
[ 89 260064 1] [ 44 260064 0]
[ 89 260064 1] [ 32 260064 0]
[ 89 260064 1] [ 155 260064 0]
[ 89 260064 1] [ 77 260064 1]
[ 89 260064 1] [ 180 260064 1]
[ 89 260064 1] [ 199 260064 0]
[ 89 260064 1] [ 374 260064 1]
[ 89 260064 1] [ 961 260064 1]
[ 130 153092 1] [ 5 153092 0]
[ 130 153092 1] [ 48 153092 0]
[ 130 153092 1] [ 163 153092 0]
[ 130 153092 1] [ 29 153092 0]
[ 130 153092 1] [ 107 153092 0]
[ 130 153092 1] [ 126 153092 1]
[ 130 153092 1] [ 131 153092 1]
[ 130 153092 1] [ 128 153092 1]
[ 130 153092 1] [ 127 153092 1]
[ 130 153092 1] [ 129 153092 1]
[ 1031 210689 1] [ 247 210689 1]
[ 1031 210689 1] [ 265 210689 0]
[ 368 81375 1] [ 367 81375 1]
[ 368 81375 1] [ 1471 81375 0]
[ 32 206607 1] [ 187 206607 1]
[ 32 206607 1] [ 149 206607 0]
[ 129 77342 1] [ 25 77342 0]
[ 129 77342 1] [ 73 77342 0]
[ 129 77342 1] [ 32 77342 0]
[ 129 77342 1] [ 358 77342 0]
[ 129 77342 1] [ 126 77342 1]
[ 129 77342 1] [ 131 77342 1]
[ 129 77342 1] [ 128 77342 1]
[ 129 77342 1] [ 130 77342 1]
[ 616 236060 1] [ 256 236060 0]
[ 616 236060 1] [ 1147 236060 1]
[ 32 61574 1] [ 207 61574 0]
[ 32 61574 1] [ 28 61574 0]
[ 32 61574 1] [ 153 61574 0]
[ 32 61574 1] [ 188 61574 1]
[ 32 61574 1] [ 237 61574 1]
[ 32 61574 1] [ 295 61574 0]
[ 32 61574 1] [ 236 61574 1]
[ 32 61574 1] [ 752 61574 1]
[ 84 231588 1] [ 286 231588 0]
[ 84 231588 1] [ 206 231588 0]
[ 84 231588 1] [ 106 231588 0]
[ 84 231588 1] [ 540 231588 0]
[ 84 231588 1] [ 208 231588 0]
[ 84 231588 1] [ 233 231588 0]
[ 84 231588 1] [ 94 231588 0]
[ 84 231588 1] [ 152 231588 0]
[ 84 231588 1] [ 391 231588 0]
[ 84 231588 1] [ 494 231588 0]
[ 84 231588 1] [ 651 231588 1]
[ 84 231588 1] [ 259 231588 0]
[ 84 231588 1] [ 550 231588 0]
[ 84 231588 1] [ 22 231588 0]
[ 84 231588 1] [ 49 231588 1]
[ 84 231588 1] [ 98 231588 1]
[ 84 231588 1] [ 275 231588 1]
[ 84 231588 1] [ 564 231588 1]
[ 84 231588 1] [ 65 231588 1]
[ 84 231588 1] [ 181 231588 0]
[ 84 231588 1] [ 204 231588 1]
[ 84 231588 1] [ 709 231588 1]
[ 84 231588 1] [ 603 231588 1]
[ 84 231588 1] [ 866 231588 1]
[ 84 231588 1] [ 582 231588 1]
[ 84 231588 1] [ 585 231588 1]
[ 84 231588 1] [ 588 231588 1]
[ 84 231588 1] [ 970 231588 1]
[ 320 62150 1] [ 126 62150 1]
[ 320 62150 1] [ 772 62150 0]
[ 313 180416 1] [ 307 180416 0]
[ 313 180416 1] [ 183 180416 1]
[ 6 169852 1] [ 189 169852 0]
[ 6 169852 1] [ 335 169852 1]
[ 6 169852 1] [ 359 169852 1]
[ 6 169852 1] [ 1081 169852 0]
[ 307 90058 1] [ 48 90058 0]
[ 307 90058 1] [ 98 90058 1]
[ 307 90058 1] [ 244 90058 1]
[ 307 90058 1] [ 317 90058 0]
[ 1047 153944 1] [ 163 153944 0]
[ 1047 153944 1] [ 55 153944 0]
[ 1047 153944 1] [ 495 153944 1]
[ 1047 153944 1] [ 1046 153944 1]
[ 44 52440 1] [ 25 52440 1]
[ 44 52440 1] [ 121 52440 0]
[ 31 29081 1] [ 187 29081 1]
[ 31 29081 1] [ 181 29081 0]
[ 331 150730 1] [ 564 150730 0]
[ 331 150730 1] [ 136 150730 1]
[ 73 258462 1] [ 70 258462 1]
[ 73 258462 1] [ 230 258462 0]
[ 647 129309 1] [ 23 129309 0]
[ 647 129309 1] [ 120 129309 0]
[ 647 129309 1] [ 91 129309 1]
[ 647 129309 1] [ 645 129309 1]
[ 647 129309 1] [ 644 129309 1]
[ 647 129309 1] [ 578 129309 0]
[ 11 88581 1] [ 108 88581 0]
[ 11 88581 1] [ 31 88581 0]
[ 11 88581 1] [ 12 88581 1]
[ 11 88581 1] [ 101 88581 0]
[ 11 88581 1] [ 10 88581 1]
[ 11 88581 1] [ 9 88581 1]
[ 187 31187 1] [ 259 31187 1]
[ 187 31187 1] [ 733 31187 0]
[ 647 90892 1] [ 187 90892 0]
[ 647 90892 1] [ 189 90892 0]
[ 647 90892 1] [ 91 90892 1]
[ 647 90892 1] [ 646 90892 1]
[ 647 90892 1] [ 645 90892 1]
[ 647 90892 1] [ 1150 90892 0]
[ 199 44062 1] [ 195 44062 1]
[ 199 44062 1] [ 181 44062 0]
[ 180 90729 1] [ 28 90729 0]
[ 180 90729 1] [ 206 90729 0]
[ 180 90729 1] [ 374 90729 1]
[ 180 90729 1] [ 182 90729 1]
[ 180 90729 1] [ 1055 90729 0]
[ 180 90729 1] [ 817 90729 1]
[ 55 200360 1] [ 64 200360 1]
[ 55 200360 1] [ 45 200360 0]
[ 55 200360 1] [ 156 200360 0]
[ 55 200360 1] [ 155 200360 0]
[ 55 200360 1] [ 174 200360 0]
[ 55 200360 1] [ 206 200360 0]
[ 55 200360 1] [ 247 200360 0]
[ 55 200360 1] [ 106 200360 0]
[ 55 200360 1] [ 208 200360 0]
[ 55 200360 1] [ 233 200360 0]
[ 55 200360 1] [ 469 200360 1]
[ 55 200360 1] [ 94 200360 0]
[ 55 200360 1] [ 152 200360 0]
[ 55 200360 1] [ 391 200360 0]
[ 55 200360 1] [ 77 200360 1]
[ 55 200360 1] [ 494 200360 0]
[ 55 200360 1] [ 651 200360 1]
[ 55 200360 1] [ 259 200360 0]
[ 55 200360 1] [ 550 200360 0]
[ 55 200360 1] [ 22 200360 0]
[ 55 200360 1] [ 195 200360 0]
[ 55 200360 1] [ 96 200360 1]
[ 55 200360 1] [ 486 200360 0]
[ 55 200360 1] [ 335 200360 0]
[ 55 200360 1] [ 148 200360 0]
[ 55 200360 1] [ 81 200360 0]
[ 55 200360 1] [ 2 200360 1]
[ 55 200360 1] [ 107 200360 0]
[ 55 200360 1] [ 121 200360 0]
[ 55 200360 1] [ 254 200360 0]
[ 55 200360 1] [ 69 200360 0]
[ 55 200360 1] [ 475 200360 1]
[ 55 200360 1] [ 141 200360 0]
[ 55 200360 1] [ 508 200360 0]
[ 55 200360 1] [ 49 200360 1]
[ 55 200360 1] [ 349 200360 0]
[ 55 200360 1] [ 479 200360 0]
[ 55 200360 1] [ 180 200360 0]
[ 55 200360 1] [ 70 200360 0]
[ 55 200360 1] [ 98 200360 0]
[ 55 200360 1] [ 26 200360 0]
[ 55 200360 1] [ 58 200360 0]
[ 55 200360 1] [ 307 200360 1]
[ 55 200360 1] [ 162 200360 0]
[ 55 200360 1] [ 358 200360 1]
[ 55 200360 1] [ 50 200360 0]
[ 55 200360 1] [ 87 200360 1]
[ 55 200360 1] [ 71 200360 1]
[ 55 200360 1] [ 373 200360 1]
[ 55 200360 1] [ 12 200360 0]
[ 55 200360 1] [ 197 200360 0]
[ 55 200360 1] [ 368 200360 0]
[ 55 200360 1] [ 789 200360 0]
[ 55 200360 1] [ 500 200360 0]
[ 55 200360 1] [ 275 200360 1]
[ 55 200360 1] [ 47 200360 0]
[ 55 200360 1] [ 151 200360 1]
[ 55 200360 1] [ 101 200360 1]
[ 55 200360 1] [ 67 200360 1]
[ 55 200360 1] [ 611 200360 1]
[ 55 200360 1] [ 4 200360 1]
[ 55 200360 1] [ 390 200360 1]
[ 55 200360 1] [ 99 200360 1]
[ 55 200360 1] [ 379 200360 1]
[ 55 200360 1] [ 177 200360 1]
[ 55 200360 1] [ 15 200360 0]
[ 55 200360 1] [ 82 200360 1]
[ 55 200360 1] [ 18 200360 1]
[ 55 200360 1] [ 554 200360 1]
[ 55 200360 1] [ 772 200360 1]
[ 55 200360 1] [ 176 200360 1]
[ 55 200360 1] [ 85 200360 1]
[ 55 200360 1] [ 229 200360 1]
[ 55 200360 1] [ 586 200360 1]
[ 55 200360 1] [ 19 200360 1]
[ 55 200360 1] [ 598 200360 1]
[ 55 200360 1] [ 575 200360 1]
[ 55 200360 1] [ 707 200360 1]
[ 55 200360 1] [ 1688 200360 1]
[ 55 200360 1] [ 1689 200360 1]
[ 55 200360 1] [ 857 200360 1]
[ 55 200360 1] [ 1104 200360 1]
[ 55 200360 1] [ 1715 200360 1]
[ 55 200360 1] [ 1330 200360 1]
[ 55 200360 1] [ 1558 200360 1]
[ 55 200360 1] [ 1356 200360 1]
[ 242 97308 1] [ 120 97308 0]
[ 242 97308 1] [ 286 97308 0]
[ 242 97308 1] [ 241 97308 1]
[ 242 97308 1] [ 811 97308 0]
[ 242 97308 1] [ 1325 97308 1]
[ 242 97308 1] [ 1716 97308 1]
[ 475 97465 1] [ 6 97465 1]
[ 475 97465 1] [ 31 97465 0]
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KeyboardInterrupt Traceback (most recent call last)
Input In [21], in <cell line: 2>()
1 # for each pair, we look if the drug is in the train and the test
2 for elt in test:
----> 3 for x in train:
4 if elt[1]==x[1]:
5 print(elt,x)
KeyboardInterrupt: