diff --git a/lnp_ml/featurization/smiles.py b/lnp_ml/featurization/smiles.py
index 434c08d..a826a4a 100644
--- a/lnp_ml/featurization/smiles.py
+++ b/lnp_ml/featurization/smiles.py
@@ -1,5 +1,7 @@
 """SMILES 分子特征提取器"""
 
+import logging
+import warnings
 from dataclasses import dataclass, field
 from typing import List, Optional, Dict
 import numpy as np
@@ -15,6 +17,9 @@ import torch
 from chemprop.utils import load_checkpoint
 from chemprop.features import mol2graph
 
+_chemprop_logger = logging.getLogger("chemprop.load_checkpoint")
+_chemprop_logger.setLevel(logging.WARNING)
+
 
 @dataclass
 class RDKitFeaturizer:
@@ -97,16 +102,16 @@ class MPNNFeaturizer:
         device = torch.device(self.device)
         paths = self.ensemble_paths or [self.checkpoint_path]
         
-        for path in paths:
-            model = load_checkpoint(path, device=device)
-            model.eval()
-            # 提取 MPNEncoder（D-MPNN 核心部分）
-            encoder = model.encoder.encoder[0]
-            # 冻结参数
-            for param in encoder.parameters():
-                param.requires_grad = False
-            self._encoders.append(encoder)
-            self._hidden_size = encoder.hidden_size
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore", category=FutureWarning)
+            for path in paths:
+                model = load_checkpoint(path, device=device, logger=_chemprop_logger)
+                model.eval()
+                encoder = model.encoder.encoder[0]
+                for param in encoder.parameters():
+                    param.requires_grad = False
+                self._encoders.append(encoder)
+                self._hidden_size = encoder.hidden_size
         
         self._initialized = True
     
diff --git a/lnp_ml/modeling/pretrain.py b/lnp_ml/modeling/pretrain.py
index 52dd3f1..9a23288 100644
--- a/lnp_ml/modeling/pretrain.py
+++ b/lnp_ml/modeling/pretrain.py
@@ -158,7 +158,7 @@ def pretrain(
     model = model.to(device)
     optimizer = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=weight_decay)
     scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
-        optimizer, mode="min", factor=0.5, patience=5, verbose=True
+        optimizer, mode="min", factor=0.5, patience=5
     )
     early_stopping = EarlyStopping(patience=patience)