add mol encode

.gitignore

@@ -2,6 +2,7 @@ model/
 *.ipynb_checkpoints/
 .idea/
 .DS_Store
+data/
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]

config/config.json

@@ -15,5 +15,5 @@
   "num_hidden_layers": 12,
   "pad_token_id": 0,
   "type_vocab_size": 2,
-  "vocab_size": 23615
+  "vocab_size": 23614
 }

config/config_layer_3.json

@@ -15,5 +15,5 @@
   "num_hidden_layers": 3,
   "pad_token_id": 0,
   "type_vocab_size": 2,
-  "vocab_size": 23615
+  "vocab_size": 23614
 }

config/config_layer_3_mol.json

+{
+  "architectures": [
+    "BertForMaskedLM"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 595,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 3,
+  "pad_token_id": 0,
+  "type_vocab_size": 2,
+  "vocab_size": 40235
+}
\ No newline at end of file

config/protein_codes_uniprot.txt

+#version: 0.2
+L L
+A A
+A L
+V L
+G L
+E L
+S L
+G G
+S S
+E E
+T L
+D L
+R L
+I L
+A V
+K L
+A G
+V V
+A E
+K K
+S G
+A I
+P L
+A R
+A D
+A S
+Q L
+T G
+A K
+V E
+N L
+F L
+V I
+V G
+A T
+K E
+R R
+V D
+V S
+P G
+I E
+P E
+I G
+I D
+V T
+R E
+I S
+A Q
+D G
+V K
+D E
+P S
+Y L
+R G
+I T
+A F
+N G
+K G
+A P
+V R
+T T
+I K
+F G
+S E
+A N
+V P
+H L
+I N
+M L
+I R
+T S
+T E
+V N
+Q Q
+F S
+D D
+I P
+F E
+K S
+Q E
+Q G
+D S
+R S
+Y G
+T P
+K R
+K N
+K D
+V F
+A Y
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+K T
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+A M
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+DL QS
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+VG DD
+T VIE
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+ML AR
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+IV PS
+IG TG
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+DR DG
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+T AKE
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+M LLS
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+FG EE
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+YL DE
+W QI
+VS ALLG
+VD PG
+TS PS
+RR VR
+RL TT
+R VVD
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+I GLE
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+I YEG
+FV AK
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+FG DY
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+DG VL
+DG EL
+AK RP
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+VN KL
+VG NL
+TW NIG
+TR AE
+TK AE
+RL IQ
+PG AT
+IT AR
+FD IE
+AV RQ
+AT QG
+AS QE
+AD DE
+VT VH
+VP AR
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+TE IK
+T DLE
+P APS
+KV GG
+KN KN
+GL QL
+GL QE
+FL GD
+FG Y
+AM KK
+W CE
+VH LLFLHETGSNN
+PG RR
+PG AG
+KK VR
+KI Y
+KE SS
+IT DS
+IS VV
+HR SGE
+DV AE
+DL QG
+D NG
+VV AN
+VT PS
+VS AN
+VEW IWGGFSVDKATLTRFFAFHF
+TD PE
+RE RS
+R TLE
+QF MD
+Q ARG
+PL AI
+M AQL
+GL FF
+DG TG
+AD II
+VL DT
+VK VF
+SG Y
+ML KG
+KK EL
+AT FS
+AK RF
+AK AY
+AA FE
+VV VL
+RG TG
+NL N
+M ALS
+KL EN
+KE QL
+FL ET
+AG KTT
+SL QN
+PE DE
+KF SS
+ID PS
+AV EQ
+W RI
+W KW
+T TLS
+RV AD
+RL RN
+RL QR
+QS QS
+PK VL
+LL TF
+KN P
+IN SS
+FK DD
+DG VE
+DE IS
+AT KL
+VE KT
+TK AQ
+RE RF
+N ALS
+KV FE
+IV KL
+IR AL
+ID VL
+EI AR
+VK PD
+VI AI
+ST SG
+PG EE
+MT EE
+ML SG
+ID VS
+ID FD
+HL EE
+DV AF
+C HI
+TG IP
+RE VL
+RE VI
+RE KS
+KI AP
+C CY
+RP IS
+QS VV
+PL KS
+NV SS
+KR AA
+IV FG
+GL HQ
+FS KD
+FL SP
+DV SG
+AR EK
+AQ QQ
+VAT E
+NN IN
+M SSG
+IK SD
+AQ VK
+VS F
+T ANG
+R ADG
+PS IS
+PE TS
+NP DL
+N AEL
+KI AL
+ID DE
+HL VE
+H MK
+FL KQ
+AI RN
+AG TF
+VK IN
+VEE S
+TG TL
+TG DS
+TG DL
+SL DN
+RP DG
+QL TS
+KV QE
+KK KY
+IQ AL
+GL SR
+DI IN
+C HN
+AA VF
+Y CR
+VR PD
+VL QD
+SE VE
+RVK DLPG
+PR PP
+PL EL
+PKG RN
+D RD
+C YI
+AT PL
+AN VI
+AA TQ
+VT LL
+TK TT
+RG AR
+NP KG
+IG DS
+I SLP
+FS DL
+D NS
+W MP
+VV NG
+VR YH
+VG LL
+VG AR
+RL IK
+RK SG
+R FLG
+PG ES
+N LLS
+KE IN
+IR SG
+IL H
+IE VK
+IE GS
+GG DS
+AK SL
+VQ AL
+TQ HS
+T PLS
+SI SS
+RG EL
+PV GL
+MS KR
+KN VL
+KL VL
+K V</w>
+IL VI
+ID IN
+F ASS
+D KR
+AG VF
+VT SE
+VS QE
+VQ PG
+SSSS SS
+RQ PE
+R RLE
+KD KD
+H VAL
+GG AF
+AWL E
+VE P
+TV FL
+TG ED
+RR P
+RK EE
+R AKG
+NL PD
+NL NN
+KT RR
+HGGG EG
+FD EE
+AQ IR
+AE RD
+VD AF
+TK AT
+SL YE
+PV EE
+NL VF
+IE PL
+H MR
+DL VP
+D AY
+AV AP
+VD PD
+TL VI
+N GGG
+IT VG
+HL KK
+FL QQ
+AR EE
+AE VF
+AD PD
+TE RG
+RL SL
+RG GL
+QL VT
+Q MM
+KH KS
+IT LL
+IP KE
+IL QN
+HKL GE
+FG EG
+F K</w>
+VS VN
+VR KG
+VE IK
+Q VVE
+LL AM
+FI EE
+FD QL
+AE NN
+YL RR
+W YT
+VQ AG
+TP RE
+T VEG
+RL KT
+RG YG
+QP AA
+PG AF
+NL VL
+NL EL
+N AEE
+FS DE
+DD VL
+D ML
+TR TF
+TM AE
+TI AQ
+RR VS
+RK AG
+RHG NKG
+R RLS
+IP TS
+GL QR
+VT PP
+TI IS
+TE SS
+SL DR
+RL EL
+PL ED
+KN GG
+ID IS
+H AGL
+DI SS
+AS DP
+AG YG
+VV VR
+VP AQ
+VG AK
+TD AA
+QI ID
+NV EE
+M AVG
+KE AF
+IL ER
+IG DD
+C HY
+AE DS
+VK VR
+VD DD
+TP VV
+RG YE
+Q ILE
+PR EL
+ME VW
+KV FG
+HL DG
+GG RS
+AE PE
+YL KT
+VPL S
+VD TD
+TE LL
+N L</w>
+FL FS
+F AEE
+AN GG
+YR EL
+VK Y
+Q AEG
+DG KD
+D KT
+C IM
+VK SS
+SV LL
+NI AE
+MS EE
+KS VS
+KK ID
+IE KF
+I AVS
+FG TG
+DI FG
+YG SS
+VI ER
+QS AV
+QG VP
+PG SD
+NL QE
+N SLG
+KN VV
+KK TG
+IE NS
+HG SG
+H ADL
+FDG DQ
+AML S
+VY GG
+VQ P
+VI EN
+VI ED
+VG AY
+VF SS
+VAE S
+TL ET
+SV AS
+RL IL
+QL KL
+PE SE
+NL TT
+IK VD
+HL TE
+GL FE
+VI SR
+TV FG
+TF FE
+RI INE
+QE RE
+PG AP
+ML AK
+IV FS
+IL SR
+IG Y
+F ANL
+AR ER
+AM SS
+AF AL
+VV KD
+VH PD
+TL AH
+QV AS
+Q CD
+PS LL
+PL DG
+PE AL
+NN SS
+IT IP
+IT EK
+ID SG
+FD P
+DEG KG
+AI AH
+TP VF
+T GLD
+QN VN
+Q VLD
+NL EN
+II YD
+ID VE
+F WN
+DE KN
+AV KL
+Y MR
+VS AD
+T ELS
+RQ QL
+RL GD
+ML AS
+KK K</w>
+IR QG
+ID FE
+I TGE
+GL PT
+AA M
+VQ QL
+VP QE
+VG KD
+V G</w>
+TL ND
+SG ST
+SE KL
+RK VL
+PL NE
+NG IP
+IT AN
+IS EN
+IE AT
+I ALD
+H TLG
+DL AF
+AR AD
+ACL G
+VK DS
+VI RD
+T VIS
+QV GG
+PS TE
+MS RR
+KI AG
+IT TD
+FD RY
+EL DK
+AE AT
+VG RS
+VE RF
+TS VL
+RLE RE
+RK TG
+RI IN
+R VVE
+QG AG
+N AKK
+IG YG
+HNL QEHS
+FT VT
+AQ SG
+AP VN
+AA DP
+VD FE
+QK KL
+ML RR
+MG IP
+K IDG
+IS VL
+H AAG
+EL ME
+AR FE
+AG YE
+AE Y
+YL TG
+VV PL
+RL NS
+QL SR
+PQ SE
+NN P
+LL VM
+KV FS
+IID S
+GG SL
+F SSS
+D IF
+AA ET
+VP TP
+T VVE
+RV AS
+RD NG
+PG Y
+N TLG
+N CR
+KG ES
+ID RR
+ID FG
+AV QE
+AK VK
+AK SS
+AA YG
+W CP
+VS VK
+TS VS
+RK IG
+MV GG
+IK Y
+FV KE
+FD GL
+DS VI
+D TT
+VP SL
+VN SE
+VAT S
+TL VK
+RI AT
+RG AV
+RE IS
+QL DD
+Q WF
+NL VS
+KP VT
+KL IR
+KAI S
+K DGE
+FG AG
+AP IH
+A T</w>
+W HR
+VE DD
+TL YS
+RE QG
+RE DL
+PE RR
+IL EQ
+ID VV
+F GLS
+DI EE
+AV FE
+VE PL
+TT SP
+TD AE
+NP EE
+NL FD
+ML DE
+KV FL
+KK GG
+KI RG
+IS TE
+IE RR
+DE QG
+AL MD
+VT RR
+SG SL
+RL PD
+QL PG
+NI IG
+LLGD PDNY
+KV EL
+AK IK
+VQG S
+VG TS
+V KLS
+RV EL
+NL TR
+NI PL
+IP ML
+IL RN
+DL ST
+DD IT
+YG KD
+V D</w>
+TG SE
+TG AE
+T TGE
+QL PS
+I ILE
+DL QR
+AF KD
+AE QQ
+YL EL
+VR AI
+VN VV
+VK TE
+TP VD
+T ILE
+M WS
+M ALE
+KV AQ
+KE IP
+IL TF
+IF EE
+I AHL
+VK GS
+TL RN
+TE AQ
+PV PS
+ND DG
+K SGS
+IL QD
+IE KY
+ID YD
+IAE S
+YL DS
+VN DL
+TP ANPL
+SG AT
+RI AG
+RE FG
+PG VF
+PG PP
+LL HE
+C TV
+W LLL
+RN RR
+RL VP
+PS VF
+PS KP
+ML KK
+M GLE
+K PLG
+FT PE
+DE KS
+AY AE
+AF SL
+AF RE
+AE RY
+VT IP
+VT FE
+VL QF
+RL VQ
+QQQQQQQQ QQQQQQQQ
+QL DL
+LL NR
+KL YD
+KD EE
+IT AV
+IQ SG
+DG VM
+AYG E
+AS RP
+YL AN
+VL YG
+RI AD
+RG KK
+R GGS
+QE AL
+PP Y
+NK IK
+NI KE
+HP VLL
+FS FL
+FS DS
+EE KS
+W QF
+TL VQ
+PS ES
+PG KY
+K APL
+DG TF
+DG AS
+VI FG
+RV AT
+RH RH
+RG VN
+RG AS
+NP SS
+NK VI
+NE KK
+KT SL
+IL AH
+IE H
+I ANS
+FE VT
+DI IT
+AT HG
+AL SI
+VN TG
+VISITDG QI
+TS RS
+TE AT
+T VLD
+NG Y
+K D</w>
+FS RS
+FL EL
+AV AH
+AK VR
+SL FP
+R ITS
+R AAS
+QV AN
+PV AP
+II VV
+DG RT
+DD AR
+AV RN
+AR IS
+VQ VF
+TP AS
+RL ND
+QL NS
+PG DN
+NV KK
+KG VE
+IL SF
+I RRG
+FS VG
+FS HL
+FI SG
+VN P
+RF FL
+R KLS
+R GLE
+QS GG
+PL H
+N SSG
+MD IK
+KV PE
+IQ AA
+GL DK
+FR KG
+C IV
+VLG D
+VL SQ
+VL NP
+VG EP
+VE KP
+VD GG
+TS DE
+RL FL
+RH RR
+DG IT
+YS LL
+VI PG
+TR P
+TG AN
+NK PE
+NG TF
+M RLG
+KP TT
+KL EL
+IR TT
+FL PS
+DL SQ
+AV QQ
+AI VR
+A Y</w>
+W TW
+TS DD
+TG LL
+QL YE
+NK IL
+IR KQ
+FS AG
+FR EL
+DI IS
+AS DE
+YS VS
+W HF
+VV PP
+VD IS
+TR IG
+TD DE
+SE KG
+RY KG
+RK VR
+RG SD
+NP SG
+NG RR
+KV AT
+IK YL
+IK IT
+FS DG
+D IY
+AQ P
+AE FL
+W FV
+VK IE
+Q AIE
+LL QT
+K AGE
+I TLS
+H MY
+GL KQ
+F EEG
+DL AY
+AV QS
+VI SL
+TI TL
+TG AD
+TD TE
+RL RT
+QL VV
+IK AL
+GL DY
+AF EK
+VN SG
+VE NS
+VE AY
+VD DS
+RT HL
+QG EG
+PS YS
+NTP PHIKPEWYFLFAYAILRSIPNKL
+NG IL

config/vocab.txt

-_PAD
-_GO
-_EOS
-_UNK
+[PAD]
+[MASK]
 [CLS]
 [SEP]
+[UNK]
 [unused1]
 [unused2]
 [unused3]

dataset.py

@@ -145,6 +145,51 @@ class Data_Encoder(data.Dataset):
         return torch.from_numpy(input).long(), torch.from_numpy(token_type_ids).long(), torch.from_numpy(input_mask).long(), y
         # return len(d), len(p)
 
+class Data_Encoder_mol(data.Dataset):
+    def __init__(self, train_file, tokenizer_config):
+        'Initialization'
+        # load data
+        # with open(train_file["sps"], 'r') as f:
+        #     self.sps = f.readlines()
+        with open(train_file['seq'], 'r') as f:
+            self.seq = f.readlines()
+        with open(train_file["smile"], 'r') as f:
+            self.smile = f.readlines()
+        with open(train_file["affinity"], 'r') as f:
+            self.affinity = f.readlines()
+        # define tokenizer
+        self.begin_id = tokenizer_config["begin_id"]
+        self.sep_id = tokenizer_config["separate_id"]
+        self.max_len = tokenizer_config["max_len"]
+        self.vocab = load_vocab(tokenizer_config["vocab_file"])
+
+        bpe_codes_drug = codecs.open(tokenizer_config["vocab_pair"])
+        self.dbpe = BPE(bpe_codes_drug, merges=-1, separator='')
+
+        bpe_codes_prot = codecs.open(tokenizer_config["vocab_pair_p"])
+        self.pbpe = BPE(bpe_codes_prot, merges=-1, separator='')
+
+
+    def __len__(self):
+        'Denotes the total number of samples'
+        return len(self.smile)
+
+    def __getitem__(self, index):
+        'Generates one sample of data'
+        # Select sample
+        # Load data and get label
+        # tokenization
+        d = self.dbpe.process_line(self.smile[index].strip()).split()
+        p = self.pbpe.process_line(self.seq[index].strip()).split()
+        y = np.float64(self.affinity[index].strip())
+
+        input_seq = [self.begin_id] + d + [self.sep_id] + p + [self.sep_id]
+        token_type_ids = np.concatenate((np.zeros((len(d) + 2), dtype=np.int), np.ones((len(p) + 1), dtype=np.int)))
+        token_type_ids = np.pad(token_type_ids, (0, self.max_len-len(input_seq)), 'constant', constant_values=0)
+        input, input_mask = seq2emb_encoder(input_seq, self.max_len, self.vocab)
+        return torch.from_numpy(input).long(), torch.from_numpy(token_type_ids).long(), torch.from_numpy(input_mask).long(), y
+        # return len(d), len(p)
+
 
 def get_task(task_name):
 
@@ -195,6 +240,24 @@ def get_task(task_name):
 
         return df, tokenizer_config
 
+    elif task_name.lower() == 'train_mol':
+        df_train = {"sps": './data/train/train_sps',
+                    'seq': './data/train/train_protein_seq',
+                   "smile": './data/train/train_smile',
+                   "affinity": './data/train/train_ic50',
+                   }
+
+        tokenizer_config = {"vocab_file": './config/vocab_mol.txt',
+                            "vocab_pair": './config/drug_codes_chembl.txt',
+                            "vocab_pair_p": './config/protein_codes_uniprot.txt',
+                            "begin_id": '[CLS]',
+                            "separate_id": "[SEP]",
+                            "max_len": 595
+                            }
+
+
+        return df_train, tokenizer_config
+
 
 
 
@@ -211,30 +274,14 @@ if __name__ == "__main__":
     # vocab = load_vocab(vocab_file)
 
     # test train
-    df_train = {"sps": './data/train_sps',
-                "smile": './data/train_smile',
-                "affinity": './data/train_ic50',
-                }
-    df_test = {"sps": './data/test_sps',
-               "smile": './data/test_smile',
-               "affinity": './data/test_ic50',
-               }
-    tokenizer_config = {"vocab_file": './config/vocab.txt',
-                        "vocab_pair": './config/drug_codes_chembl.txt',
-                        "begin_id": '[CLS]',
-                        "separate_id": "[SEP]",
-                        "max_len": 256
-                        }
+    task = 'train_mol'
+    data_file, tokenizer_config = get_task(task)
     params = {'batch_size': 1,
               'shuffle': False,
               'num_workers': 0
               }
-    # trainset = Data_Encoder(df_train, tokenizer_config)
-    # training_generator = data.DataLoader(trainset, **params)
-    # with open('utils/train_data_analyse.csv', 'w', newline='') as f:
-    #     csv_f = csv.writer(f)
-    #     csv_f.writerow(['drup_len', 'protein_len'])
-    #     for i, (len_d, len_p) in tqdm(enumerate(training_generator)):
-    #         d = len_d.numpy()[0]
-    #         p = len_p.numpy()[0]
-    #         csv_f.writerow([str(d), str(p)])
+    trainset = Data_Encoder_mol(data_file, tokenizer_config)
+    training_generator = data.DataLoader(trainset, **params)
+    for i, (len_d, len_p) in tqdm(enumerate(training_generator)):
+        d = len_d.numpy()[0]
+        p = len_p.numpy()[0]

run_interaction.py

 from argparse import ArgumentParser
-from dataset import Data_Encoder, get_task
+from dataset import Data_Encoder, get_task, Data_Encoder_mol
 import torch
 from torch.utils.data import DataLoader
 from configuration_bert import BertConfig
@@ -102,7 +102,8 @@ def test(args, model, dataset):
 def main(args):
     # load data
     data_file, tokenizer_config = get_task(args.task)
-    dataset = Data_Encoder(data_file, tokenizer_config)
+    # dataset = Data_Encoder(data_file, tokenizer_config)
+    dataset = Data_Encoder_mol(data_file, tokenizer_config)
     # creat model
     print('------------------creat model---------------------------')
     config = BertConfig.from_pretrained(args.config)
@@ -110,7 +111,7 @@ def main(args):
     print('model name : BertAffinity')
     print('task name : {}'.format(args.task))
 
-    if args.task in ['train', 'train_z_1', 'train_z_10', 'train_z_100']:
+    if args.task in ['train', 'train_z_1', 'train_z_10', 'train_z_100', 'train_mol']:
         train(args, model, dataset)
 
     elif args.task in ['test']:
@@ -119,9 +120,6 @@ def main(args):
 
 
 
-
-
-
 if __name__ == '__main__':
     # get parameter
     parser = ArgumentParser(description='BertAffinity')
@@ -152,11 +150,11 @@ if __name__ == '__main__':
 
     # local test
 
-    # args.task = 'train_z_10'
-    # args.savedir = 'local_test_train'
-    # args.epochs = 10
-    # args.lr = 1e-5
-    # args.config = './config/config_layer_3.json'
+    args.task = 'train_mol'
+    args.savedir = 'local_test_train'
+    args.epochs = 10
+    args.lr = 1e-5
+    args.config = './config/config_layer_3_mol.json'
 
 
 

train.sh

-CUDA_VISIBLE_DEVICES=4 python run_interaction.py --b=64 --task=train --epochs=30 --lr=1e-5 --savedir=lr-1e-5-batch-64-e-30-layer3-0505 --config=./config/config_layer_3.json
+CUDA_VISIBLE_DEVICES=4 python run_interaction.py --b=64 --task=train_mol --epochs=30 --lr=1e-5 --savedir=lr-1e-5-batch-64-e-30-layer3-0505 --config=./config/config_layer_3.json
 

utils/analyse_data.py

+from subword_nmt.apply_bpe import BPE
+import codecs
+import collections
+
+bpe_codes_drug = codecs.open('../config/drug_codes_chembl.txt')
+dbpe = BPE(bpe_codes_drug, merges=-1, separator='')
+
+bpe_codes_prot = codecs.open('../config/protein_codes_uniprot.txt')
+pbpe = BPE(bpe_codes_prot, merges=-1, separator='')
+
+
+def load_file(file):
+    data = []
+    with open(file, 'r') as f:
+        lines = f.readlines()
+        for line in lines:
+            data.append(line.strip('\n'))
+        return data
+
+def load_vocab(vocab_file):
+    """Loads a vocabulary file into a dictionary."""
+    vocab = collections.OrderedDict()
+    with open(vocab_file, "r", encoding="utf-8") as reader:
+        tokens = reader.readlines()
+    for index, token in enumerate(tokens):
+        token = token.rstrip("\n")
+        vocab[token] = index
+    return vocab
+
+def seq2vec(protein, drug):
+    start_token = '[CLS]'
+    sep_token = '[SEP]'
+
+    prots = load_file(protein)
+    drugs = load_file(drug)
+    for p, d in zip(prots, drugs):
+        d = dbpe.process_line(d).split()
+        p = pbpe.process_line(p).split()
+        tokens = [start_token] + d + [sep_token] + p + [sep_token]
+        print(len(p))
+
+
+if __name__ == '__main__':
+    seq = '../data/test/test_protein_seq'
+    simle = '../data/train/train_smile'
+    vocab = '../config/vocab_mol.txt'
+    seq2vec(seq, simle)
\ No newline at end of file

utils/define_vocab.py

 import pandas as pd
+import numpy as np
 
-
-sub_csv = pd.read_csv('../ESPF/subword_units_map_chembl.csv')
+sub_csv = pd.read_csv('../config/subword_units_map_chembl.csv')
 idx2word_d = sub_csv['index'].values
+
+sub_csv = pd.read_csv('../config/subword_units_map_uniprot.csv')
+idx2word_p = sub_csv['index'].values
 # words2idx_d = dict(zip(idx2word_d, range(0, len(idx2word_d))))
 
-with open('../ESPF/vocab76.to', 'r') as f:
-    prot_vocab = f.readlines()
+spqcial_tokens = np.array(['[PAD]', '[MASK]', '[CLS]', '[SEP]', '[UNK]', '[unused1]', '[unused2]', '[unused3]', '[unused4]', '[unused5]'])
+
+all_tokens = np.concatenate((spqcial_tokens, idx2word_p, idx2word_d))
+
+save = '../config/vocab_mol.txt'
 
-with open('../ESPF/vocab.txt', 'w') as f:
-    for prot in prot_vocab:
-        f.write(prot.strip() + '\n')
-with open('../ESPF/vocab.txt', 'a') as f:
-    for drug in idx2word_d:
-        f.write(drug + '\n')
+with open(save, 'w') as f:
+    for token in all_tokens:
+        f.write(str(token) + '\n')