Added convincing results

logs/logs_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_random_best_model_n0/logs_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_random_best_model_n0/metrics.csv

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logs/logs_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_random_best_model_n1/logs_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_random_best_model_n1/metrics.csv

@@ -0,0 +1,46 @@
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logs/logs_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_random_best_model_n2/logs_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_random_best_model_n2/metrics.csv

@@ -0,0 +1,52 @@
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logs/logs_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_random_random_cv_model_fold0/logs_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_random_random_cv_model_fold0/metrics.csv

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logs/logs_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_random_random_cv_model_fold1/logs_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_random_random_cv_model_fold1/metrics.csv

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logs/logs_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_random_random_cv_model_fold3/logs_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_random_random_cv_model_fold3/metrics.csv

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logs/logs_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_random_random_cv_model_fold4/logs_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_random_random_cv_model_fold4/metrics.csv

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logs/logs_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_tanimoto_best_model_n1/logs_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_tanimoto_best_model_n1/metrics.csv

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logs/logs_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_tanimoto_best_model_n2/logs_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_tanimoto_best_model_n2/metrics.csv

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logs/logs_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_tanimoto_tanimoto_cv_model_fold0/logs_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_tanimoto_tanimoto_cv_model_fold0/metrics.csv

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logs/logs_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_tanimoto_tanimoto_cv_model_fold1/logs_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_tanimoto_tanimoto_cv_model_fold1/metrics.csv

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logs/logs_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_tanimoto_tanimoto_cv_model_fold3/logs_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_tanimoto_tanimoto_cv_model_fold3/metrics.csv

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logs/logs_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_tanimoto_tanimoto_cv_model_fold4/logs_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_tanimoto_tanimoto_cv_model_fold4/metrics.csv

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logs/logs_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_uniprot_uniprot_cv_model_fold0/logs_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_uniprot_uniprot_cv_model_fold0/metrics.csv

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logs/logs_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_uniprot_uniprot_cv_model_fold1/logs_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_uniprot_uniprot_cv_model_fold1/metrics.csv

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logs/logs_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_uniprot_uniprot_cv_model_fold2/logs_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_uniprot_uniprot_cv_model_fold2/metrics.csv

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logs/logs_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_uniprot_uniprot_cv_model_fold3/logs_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_uniprot_uniprot_cv_model_fold3/metrics.csv

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logs/logs_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_uniprot_uniprot_cv_model_fold4/logs_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_uniprot_uniprot_cv_model_fold4/metrics.csv

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+0.5832179188728333,0.8354430198669434,0.8785046935081482,0.9038461446762085,0.8545454740524292,0.8681818246841431,13,83,,,,,,,,,,,,,,
+,,,,,,13,83,0.5066381692886353,0.8221288323402405,0.8382165431976318,0.7686915993690491,0.9215686321258545,0.8948677778244019,,,,,,,,
+0.581275999546051,0.8101266026496887,0.8598130941390991,0.8846153616905212,0.8363636136054993,0.8674242496490479,14,89,,,,,,,,,,,,,,
+,,,,,,14,89,0.4889335632324219,0.8277310729026794,0.8425096273422241,0.775943398475647,0.9215686321258545,0.9021491408348083,,,,,,,,
+0.5882042050361633,0.7848101258277893,0.8440366983413696,0.8518518805503845,0.8363636136054993,0.8492424488067627,15,95,,,,,,,,,,,,,,
+,,,,,,15,95,0.46999117732048035,0.8347339034080505,0.8479381203651428,0.7852028608322144,0.9215686321258545,0.9134241342544556,,,,,,,,
+,,,,,,,99,,,,,,,6.270709747290424e-05,,,,,,,
+,,,,,,16,99,,,,,,,,0.44202303886413574,,,,,,
+0.5878583192825317,0.8101266026496887,0.8623853325843811,0.8703703880310059,0.8545454740524292,0.8522727489471436,16,101,,,,,,,,,,,,,,
+,,,,,,16,101,0.4567168951034546,0.8333333134651184,0.8468468189239502,0.7833333611488342,0.9215686321258545,0.9211919903755188,,,,,,,,
+0.5877148509025574,0.8101266026496887,0.8623853325843811,0.8703703880310059,0.8545454740524292,0.8522727489471436,17,107,,,,,,,,,,,,,,
+,,,,,,17,107,0.4553963840007782,0.8361344337463379,0.8490322828292847,0.7870813608169556,0.9215686321258545,0.9224553108215332,,,,,,,,
+0.5877050161361694,0.8101266026496887,0.8623853325843811,0.8703703880310059,0.8545454740524292,0.8522727489471436,18,113,,,,,,,,,,,,,,
+,,,,,,18,113,0.45466941595077515,0.8347339034080505,0.8479381203651428,0.7852028608322144,0.9215686321258545,0.9234203696250916,,,,,,,,
+0.5877034068107605,0.8101266026496887,0.8623853325843811,0.8703703880310059,0.8545454740524292,0.8522727489471436,19,119,,,,,,,,,,,,,,
+,,,,,,19,119,0.4554412066936493,0.8361344337463379,0.8486416339874268,0.7884615659713745,0.918767511844635,0.9228240251541138,,,,,,,,
+0.5877031683921814,0.8101266026496887,0.8623853325843811,0.8703703880310059,0.8545454740524292,0.8522727489471436,20,125,,,,,,,,,,,,,,
+,,,,,,20,125,0.45448336005210876,0.8361344337463379,0.8486416339874268,0.7884615659713745,0.918767511844635,0.9244953393936157,,,,,,,,
+0.5877031683921814,0.8101266026496887,0.8623853325843811,0.8703703880310059,0.8545454740524292,0.8522727489471436,21,126,,,,,,,,,,,,,,
+,,,,,,21,126,,,,,,,,,0.6851583123207092,0.658823549747467,0.6947368383407593,0.6734693646430969,0.717391312122345,0.5852842926979065

protac_degradation_predictor/optuna_utils.py

@@ -129,6 +129,7 @@ def pytorch_model_objective(
     use_smote = trial.suggest_categorical('use_smote', [True, False])
     apply_scaling = True # trial.suggest_categorical('apply_scaling', [True, False])
     dropout = trial.suggest_float('dropout', *dropout_options)
+    use_batch_norm = trial.suggest_categorical('use_batch_norm', [True, False])
 
     # Start the CV over the folds
     X = train_val_df.copy().drop(columns=active_label)
@@ -169,6 +170,7 @@ def pytorch_model_objective(
             batch_size=batch_size,
             learning_rate=learning_rate,
             dropout=dropout,
+            use_batch_norm=use_batch_norm,
             max_epochs=max_epochs,
             smote_k_neighbors=smote_k_neighbors,
             apply_scaling=apply_scaling,
@@ -256,7 +258,7 @@ def hyperparameter_tuning_and_training(
     pl.seed_everything(42)
 
     # Define the search space
-    hidden_dim_options = [32, 64, 128, 256, 512]
+    hidden_dim_options = [16, 32, 64, 128, 256] #, 512]
     batch_size_options = [128, 128] # [4, 8, 16, 32, 64, 128]
     learning_rate_options = (1e-6, 1e-3) # min and max values for loguniform distribution
     smote_k_neighbors_options = list(range(3, 16))
@@ -393,7 +395,16 @@ def hyperparameter_tuning_and_training(
     # Ablation study: disable embeddings at a time
     ablation_report = []
     dfs_stats = get_dataframe_stats(train_val_df, test_df=test_df, active_label=active_label)
-    for disabled_embeddings in [['e3'], ['poi'], ['cell'], ['smiles'], ['e3', 'cell'], ['poi', 'e3', 'cell']]:
+    disabled_embeddings_combinations = [
+        ['e3'],
+        ['poi'],
+        ['cell'],
+        ['smiles'],
+        ['e3', 'cell'],
+        ['poi', 'e3'],
+        ['poi', 'e3', 'cell'],
+    ]
+    for disabled_embeddings in disabled_embeddings_combinations:
         logging.info('-' * 100)
         logging.info(f'Ablation study with disabled embeddings: {disabled_embeddings}')
         logging.info('-' * 100)

protac_degradation_predictor/pytorch_models.py

@@ -37,6 +37,7 @@ class PROTAC_Predictor(nn.Module):
         cell_emb_dim: int = config.cell_embedding_size,
         dropout: float = 0.2,
         join_embeddings: Literal['beginning', 'concat', 'sum'] = 'sum',
+        use_batch_norm: bool = False,
         disabled_embeddings: list = [],
     ):
         """ Initialize the PROTAC model.
@@ -58,6 +59,7 @@ class PROTAC_Predictor(nn.Module):
         self.smiles_emb_dim = smiles_emb_dim
         self.hidden_dim = hidden_dim
         self.join_embeddings = join_embeddings
+        self.use_batch_norm = use_batch_norm
         self.disabled_embeddings = disabled_embeddings
         # Set our init args as class attributes
         self.__dict__.update(locals())
@@ -103,6 +105,7 @@ class PROTAC_Predictor(nn.Module):
         self.fc2 = nn.Linear(hidden_dim, hidden_dim)
         self.fc3 = nn.Linear(hidden_dim, 1)
 
+        self.bnorm = nn.BatchNorm1d(hidden_dim)
         self.dropout = nn.Dropout(p=dropout)
 
     
@@ -137,7 +140,8 @@ class PROTAC_Predictor(nn.Module):
                     x = torch.sum(embeddings, dim=1)
                 else:
                     x = embeddings[0]
-        x = self.dropout(F.relu(self.fc1(x)))
+        x = F.relu(self.fc1(x))
+        x = self.bnorm(x) if self.use_batch_norm else self.self.dropout(x)
         x = self.fc3(x)
         return x
 
@@ -154,6 +158,7 @@ class PROTAC_Model(pl.LightningModule):
         batch_size: int = 128,
         learning_rate: float = 1e-3,
         dropout: float = 0.2,
+        use_batch_norm: bool = False,
         join_embeddings: Literal['beginning', 'concat', 'sum'] = 'sum',
         train_dataset: PROTAC_Dataset = None,
         val_dataset: PROTAC_Dataset = None,
@@ -211,6 +216,7 @@ class PROTAC_Model(pl.LightningModule):
             cell_emb_dim=cell_emb_dim,
             dropout=dropout,
             join_embeddings=join_embeddings,
+            use_batch_norm=use_batch_norm,
             disabled_embeddings=disabled_embeddings,
         )
 
@@ -394,6 +400,7 @@ class PROTAC_Model(pl.LightningModule):
                 logging.warning("Scalers not found in checkpoint. Consider re-fitting scalers if necessary.")
 
 
+# TODO: Use some sort of **kwargs to pass all the parameters to the model...
 def train_model(
         protein2embedding: Dict,
         cell2embedding: Dict,
@@ -406,6 +413,7 @@ def train_model(
         learning_rate: float = 2e-5,
         dropout: float = 0.2,
         max_epochs: int = 50,
+        use_batch_norm: bool = False,
         smiles_emb_dim: int = config.fingerprint_size,
         poi_emb_dim: int = config.protein_embedding_size,
         e3_emb_dim: int = config.protein_embedding_size,
@@ -539,6 +547,7 @@ def train_model(
         batch_size=batch_size,
         join_embeddings=join_embeddings,
         dropout=dropout,
+        use_batch_norm=use_batch_norm,
         learning_rate=learning_rate,
         apply_scaling=apply_scaling,
         train_dataset=train_ds,

reports/ablation_report_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_random.csv

@@ -0,0 +1,7 @@
+train_loss,train_loss_step,train_loss_epoch,train_acc,train_acc_epoch,train_f1_score,train_f1_score_epoch,train_precision,train_precision_epoch,train_recall,train_recall_epoch,train_roc_auc,train_roc_auc_epoch,test_loss,test_acc,test_f1_score,test_precision,test_recall,test_roc_auc,disabled_embeddings,model_type,train_len,train_active_perc,train_inactive_perc,train_avg_tanimoto_dist,test_len,test_active_perc,test_inactive_perc,test_avg_tanimoto_dist,num_leaking_uniprot_train_test,num_leaking_smiles_train_test,perc_leaking_uniprot_train_test,perc_leaking_smiles_train_test,split_type
+0.6953334212303162,0.6953334212303162,0.6953334212303162,0.484375,0.484375,0.0,0.0,0.0,0.0,0.0,0.0,0.5139296054840088,0.5139296054840088,0.6909260153770447,0.5348837375640869,0.0,0.0,0.0,0.5467391610145569,disabled e3,Pytorch,771,0.5149156939040207,0.48508430609597925,0.3768059369269877,86,0.46511627906976744,0.5348837209302325,0.38114673659326254,34,44,0.8326848249027238,0.10246433203631647,random
+0.6972618699073792,0.6972618699073792,0.6972618699073792,0.46875,0.46875,0.0,0.0,0.0,0.0,0.0,0.0,0.4676470458507538,0.4676470458507538,0.6910226345062256,0.5348837375640869,0.0,0.0,0.0,0.5277173519134521,disabled poi,Pytorch,771,0.5149156939040207,0.48508430609597925,0.3768059369269877,86,0.46511627906976744,0.5348837209302325,0.38114673659326254,34,44,0.8326848249027238,0.10246433203631647,random
+0.6932122111320496,0.6932122111320496,0.6932122111320496,0.5546875,0.5546875,0.688524603843689,0.688524603843689,0.5431034564971924,0.5431034564971924,0.9402984976768494,0.9402984976768494,0.4653780460357666,0.4653780460357666,0.6960012316703796,0.5348837375640869,0.0,0.0,0.0,0.42418479919433594,disabled cell,Pytorch,771,0.5149156939040207,0.48508430609597925,0.3768059369269877,86,0.46511627906976744,0.5348837209302325,0.38114673659326254,34,44,0.8326848249027238,0.10246433203631647,random
+0.6925806999206543,0.6925806999206543,0.6925806999206543,0.5234375,0.5234375,0.0,0.0,0.0,0.0,0.0,0.0,0.4981648921966553,0.4981648921966553,0.6906061172485352,0.5348837375640869,0.0,0.0,0.0,0.5176630616188049,disabled smiles,Pytorch,771,0.5149156939040207,0.48508430609597925,0.3768059369269877,86,0.46511627906976744,0.5348837209302325,0.38114673659326254,34,44,0.8326848249027238,0.10246433203631647,random
+0.6859574317932129,0.6859574317932129,0.6859574317932129,0.5546875,0.5546875,0.033898305147886276,0.033898305147886276,0.3333333432674408,0.3333333432674408,0.01785714365541935,0.01785714365541935,0.5659722089767456,0.5659722089767456,0.6912890672683716,0.5348837375640869,0.0,0.0,0.0,0.3991847634315491,disabled e3 cell,Pytorch,771,0.5149156939040207,0.48508430609597925,0.3768059369269877,86,0.46511627906976744,0.5348837209302325,0.38114673659326254,34,44,0.8326848249027238,0.10246433203631647,random
+0.6942660808563232,0.6942660808563232,0.6942660808563232,0.4765625,0.4765625,0.6127167344093323,0.6127167344093323,0.49074074625968933,0.49074074625968933,0.8153846263885498,0.8153846263885498,0.4676434397697449,0.4676434397697449,0.6945915818214417,0.5348837375640869,0.0,0.0,0.0,0.448097825050354,disabled poi e3 cell,Pytorch,771,0.5149156939040207,0.48508430609597925,0.3768059369269877,86,0.46511627906976744,0.5348837209302325,0.38114673659326254,34,44,0.8326848249027238,0.10246433203631647,random

reports/ablation_report_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_tanimoto.csv

@@ -0,0 +1,7 @@
+train_loss,train_loss_step,train_loss_epoch,train_acc,train_acc_epoch,train_f1_score,train_f1_score_epoch,train_precision,train_precision_epoch,train_recall,train_recall_epoch,train_roc_auc,train_roc_auc_epoch,test_loss,test_acc,test_f1_score,test_precision,test_recall,test_roc_auc,disabled_embeddings,model_type,train_len,train_active_perc,train_inactive_perc,train_avg_tanimoto_dist,test_len,test_active_perc,test_inactive_perc,test_avg_tanimoto_dist,num_leaking_uniprot_train_test,num_leaking_smiles_train_test,perc_leaking_uniprot_train_test,perc_leaking_smiles_train_test,split_type
+0.3706200420856476,0.3924199342727661,0.3706200420856476,0.8612499833106995,0.8612499833106995,0.8634686470031738,0.8634686470031738,0.8498789072036743,0.8498789072036743,0.8774999976158142,0.8774999976158142,0.9321374893188477,0.9321374893188477,0.5499706864356995,0.7176470756530762,0.5862069129943848,0.8095238208770752,0.45945945382118225,0.7905404567718506,disabled e3,Pytorch,772,0.5181347150259067,0.48186528497409326,0.37254018872057115,85,0.43529411764705883,0.5647058823529412,0.4199408355934975,22,0,0.5699481865284974,0.0,tanimoto
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+0.34396126866340637,0.3502621650695801,0.34396126866340637,0.8650000095367432,0.8650000095367432,0.8711217045783997,0.8711217045783997,0.8333333134651184,0.8333333134651184,0.9125000238418579,0.9125000238418579,0.942243754863739,0.942243754863739,0.580200731754303,0.7176470756530762,0.6666666865348816,0.6857143044471741,0.6486486196517944,0.7798423171043396,disabled cell,Pytorch,772,0.5181347150259067,0.48186528497409326,0.37254018872057115,85,0.43529411764705883,0.5647058823529412,0.4199408355934975,22,0,0.5699481865284974,0.0,tanimoto
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reports/ablation_report_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_uniprot.csv

@@ -0,0 +1,7 @@
+train_loss,train_loss_step,train_loss_epoch,train_acc,train_acc_epoch,train_f1_score,train_f1_score_epoch,train_precision,train_precision_epoch,train_recall,train_recall_epoch,train_roc_auc,train_roc_auc_epoch,test_loss,test_acc,test_f1_score,test_precision,test_recall,test_roc_auc,disabled_embeddings,model_type,train_len,train_active_perc,train_inactive_perc,train_avg_tanimoto_dist,test_len,test_active_perc,test_inactive_perc,test_avg_tanimoto_dist,num_leaking_uniprot_train_test,num_leaking_smiles_train_test,perc_leaking_uniprot_train_test,perc_leaking_smiles_train_test,split_type
+0.5558249950408936,0.5920203924179077,0.5558249950408936,0.8312020301818848,0.8312020301818848,0.8417266011238098,0.8417266011238098,0.7923250794410706,0.7923250794410706,0.8976982235908508,0.8976982235908508,0.900458574295044,0.900458574295044,0.6788899898529053,0.5176470875740051,0.5684210658073425,0.5510203838348389,0.5869565010070801,0.5958751440048218,disabled e3,Pytorch,772,0.5064766839378239,0.49352331606217614,0.3753049487934892,85,0.5411764705882353,0.4588235294117647,0.39483030881358294,0,6,0.0,0.011658031088082901,uniprot
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reports/cv_report_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_random.csv

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reports/cv_report_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_tanimoto.csv

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reports/cv_report_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_uniprot.csv

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reports/hparam_report_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_random.csv

@@ -0,0 +1,2 @@
+hidden_dim,learning_rate,smote_k_neighbors,use_smote,dropout,use_batch_norm,split_type
+32,1.3783237455007196e-06,7,True,0.331261142176991,False,random

reports/hparam_report_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_tanimoto.csv

@@ -0,0 +1,2 @@
+hidden_dim,learning_rate,smote_k_neighbors,use_smote,dropout,split_type
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reports/hparam_report_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_uniprot.csv

@@ -0,0 +1,2 @@
+hidden_dim,learning_rate,smote_k_neighbors,use_smote,dropout,split_type
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reports/majority_vote_report_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_random.csv

@@ -0,0 +1,3 @@
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reports/majority_vote_report_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_tanimoto.csv

@@ -0,0 +1,3 @@
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reports/majority_vote_report_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_uniprot.csv

@@ -0,0 +1,3 @@
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reports/report_ablation_Active_Dmax_0.6_pDC50_6.0_test_split_0.1.csv

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reports/report_ablation_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_e3_ligase.csv

@@ -1,7 +0,0 @@
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reports/report_ablation_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_random.csv

@@ -1,7 +0,0 @@
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reports/report_ablation_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_tanimoto.csv

@@ -1,7 +0,0 @@
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reports/report_ablation_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_uniprot.csv

@@ -1,7 +0,0 @@
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reports/report_cv_train_Active_Dmax_0.6_pDC50_6.0_test_split_0.1.csv

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reports/report_cv_train_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_e3_ligase.csv

@@ -1,6 +0,0 @@
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reports/report_cv_train_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_random.csv

@@ -1,6 +0,0 @@
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reports/report_cv_train_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_tanimoto.csv

@@ -1,6 +0,0 @@
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reports/report_cv_train_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_uniprot.csv

@@ -1,6 +0,0 @@
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reports/report_hparam_Active_Dmax_0.6_pDC50_6.0_test_split_0.1.csv

@@ -1,5 +0,0 @@
-hidden_dim,batch_size,learning_rate,join_embeddings,smote_k_neighbors,use_smote,apply_scaling,dropout,split_type
-768,8,1.843233973932415e-05,concat,10,False,True,0.11257777663560328,random
-768,16,5.265532327544801e-05,concat,13,True,True,0.1427004529020413,e3_ligase
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-512,32,1.0549183286920568e-05,sum,6,False,True,0.2629572558965121,uniprot

reports/report_hparams_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_e3_ligase.csv

@@ -1,2 +0,0 @@
-hidden_dim,batch_size,learning_rate,join_embeddings,smote_k_neighbors,use_smote,apply_scaling,dropout,split_type
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