diff --git a/chebai/preprocessing/bin/smiles_token/tokens.txt b/chebai/preprocessing/bin/smiles_token/tokens.txt
index c5553958..18e0d8d6 100644
--- a/chebai/preprocessing/bin/smiles_token/tokens.txt
+++ b/chebai/preprocessing/bin/smiles_token/tokens.txt
@@ -4373,3 +4373,5 @@ b
 [CaH2]
 [NH3]
 [OH2]
+[TlH2+]
+[SbH6+3]
diff --git a/chebai/result/classification.py b/chebai/result/classification.py
index 725be248..d3c04233 100644
--- a/chebai/result/classification.py
+++ b/chebai/result/classification.py
@@ -7,15 +7,18 @@
 import torch
 from torch import Tensor
 from torchmetrics.classification import (
-    MultilabelF1Score,
-    MultilabelPrecision,
-    MultilabelRecall,
-    MultilabelAUROC,
-    BinaryF1Score,
     BinaryAUROC,
     BinaryAveragePrecision,
+    BinaryF1Score,
+    BinaryRecall,
+    MultilabelAUROC,
     MultilabelAveragePrecision,
+    MultilabelF1Score,
+    MultilabelPrecision,
+    MultilabelRecall,
+    MultilabelSpecificity,
 )
+from torchmetrics.functional import specificity
 
 from chebai.callbacks.epoch_metrics import BalancedAccuracy, MacroF1
 
@@ -130,13 +133,39 @@ def metrics_classification_multilabel(
     f1_micro = MacroF1(preds.shape[1]).to(device=device)
     my_auc_roc = MultilabelAUROC(preds.shape[1]).to(device=device)
     my_av_prec = MultilabelAveragePrecision(preds.shape[1]).to(device=device)
+    my_macro_specificity = MultilabelSpecificity(preds.shape[1], average="macro").to(
+        device=device
+    )
+    my_micro_specificity = MultilabelSpecificity(preds.shape[1], average="micro").to(
+        device=device
+    )
+    my_macro_sensitivity = MultilabelRecall(preds.shape[1], average="macro").to(
+        device=device
+    )
+    my_micro_sensitivity = MultilabelRecall(preds.shape[1], average="micro").to(
+        device=device
+    )
 
     macro_f1 = my_f1_macro(preds, labels).cpu().numpy()
     micro_f1 = f1_micro(preds, labels).cpu().numpy()
     auc_roc = my_auc_roc(preds, labels).cpu().numpy()
     prc_auc = my_av_prec(preds, labels).cpu().numpy()
-
-    return auc_roc, macro_f1, micro_f1, bal_acc, prc_auc
+    specificity_macro = my_macro_specificity(preds, labels).cpu().numpy()
+    specificity_micro = my_micro_specificity(preds, labels).cpu().numpy()
+    sensitivity_macro = my_macro_sensitivity(preds, labels).cpu().numpy()
+    sensitivity_micro = my_micro_sensitivity(preds, labels).cpu().numpy()
+
+    return (
+        auc_roc,
+        macro_f1,
+        micro_f1,
+        bal_acc,
+        prc_auc,
+        sensitivity_macro,
+        sensitivity_micro,
+        specificity_macro,
+        specificity_micro,
+    )
 
 
 def metrics_classification_binary(
@@ -151,6 +180,7 @@ def metrics_classification_binary(
     my_f1 = BinaryF1Score().to(device=device)
     my_av_prec = BinaryAveragePrecision().to(device=device)
     my_bal_acc = BalancedAccuracy(preds.shape[1]).to(device=device)
+    my_sensitivity = BinaryRecall().to(device=device)
 
     bal_acc = my_bal_acc(preds, labels).cpu().numpy()
     auc_roc = my_auc_roc(preds, labels).cpu().numpy()
@@ -158,5 +188,7 @@ def metrics_classification_binary(
     # auc_roc = my_auc_roc.compute().numpy()
     f1_score = my_f1(preds, labels).cpu().numpy()
     prc_auc = my_av_prec(preds, labels).cpu().numpy()
+    sensitivity = my_sensitivity(preds, labels).cpu().numpy()
+    specificity_result = specificity(preds, labels, task="binary").cpu().numpy()
 
-    return auc_roc, f1_score, bal_acc, prc_auc
+    return auc_roc, f1_score, bal_acc, prc_auc, sensitivity, specificity_result