diff --git a/Makefile b/Makefile
index 160eb95..3cab00a 100644
--- a/Makefile
+++ b/Makefile
@@ -68,6 +68,11 @@ clean_data: requirements
 data: requirements
 	$(PYTHON_INTERPRETER) scripts/process_data.py
 
+## Process dataset for final training (interim -> processed/final, train:val=9:1, no test)
+.PHONY: data_final
+data_final: requirements
+	$(PYTHON_INTERPRETER) scripts/process_data_final.py
+
 ## Process external data for pretrain (external -> processed)
 .PHONY: data_pretrain
 data_pretrain: requirements
@@ -128,6 +133,16 @@ train: requirements
 finetune: requirements
 	$(PYTHON_INTERPRETER) -m lnp_ml.modeling.train --init-from-pretrain models/pretrain_delivery.pt $(FREEZE_FLAG) $(MPNN_FLAG) $(DEVICE_FLAG)
 
+## Final training using all data (train:val=9:1, no test set), with pretrained weights
+.PHONY: train_final
+train_final: requirements
+	$(PYTHON_INTERPRETER) -m lnp_ml.modeling.train \
+		--train-path data/processed/final/train.parquet \
+		--val-path data/processed/final/val.parquet \
+		--output-dir models/final \
+		--init-from-pretrain models/pretrain_delivery.pt \
+		$(FREEZE_FLAG) $(MPNN_FLAG) $(DEVICE_FLAG)
+
 ## Finetune with cross-validation on internal data (5-fold, amine-based split) with pretrained weights
 .PHONY: finetune_cv
 finetune_cv: requirements
diff --git a/data/processed/final/feature_columns.txt b/data/processed/final/feature_columns.txt
new file mode 100644
index 0000000..793bab9
--- /dev/null
+++ b/data/processed/final/feature_columns.txt
@@ -0,0 +1,89 @@
+# Feature columns configuration (final training)
+
+# SMILES
+smiles
+
+# comp token [5]
+Cationic_Lipid_to_mRNA_weight_ratio
+Cationic_Lipid_Mol_Ratio
+Phospholipid_Mol_Ratio
+Cholesterol_Mol_Ratio
+PEG_Lipid_Mol_Ratio
+
+# phys token [12]
+Purity_Pure
+Purity_Crude
+Mix_type_Microfluidic
+Mix_type_Pipetting
+Cargo_type_mRNA
+Cargo_type_pDNA
+Cargo_type_siRNA
+Target_or_delivered_gene_FFL
+Target_or_delivered_gene_Peptide_barcode
+Target_or_delivered_gene_hEPO
+Target_or_delivered_gene_FVII
+Target_or_delivered_gene_GFP
+
+# help token [4]
+Helper_lipid_ID_DOPE
+Helper_lipid_ID_DOTAP
+Helper_lipid_ID_DSPC
+Helper_lipid_ID_MDOA
+
+# exp token [32]
+Model_type_A549
+Model_type_BDMC
+Model_type_BMDM
+Model_type_HBEC_ALI
+Model_type_HEK293T
+Model_type_HeLa
+Model_type_IGROV1
+Model_type_Mouse
+Model_type_RAW264p7
+Delivery_target_body
+Delivery_target_dendritic_cell
+Delivery_target_generic_cell
+Delivery_target_liver
+Delivery_target_lung
+Delivery_target_lung_epithelium
+Delivery_target_macrophage
+Delivery_target_muscle
+Delivery_target_spleen
+Route_of_administration_in_vitro
+Route_of_administration_intramuscular
+Route_of_administration_intratracheal
+Route_of_administration_intravenous
+Batch_or_individual_or_barcoded_Barcoded
+Batch_or_individual_or_barcoded_Individual
+Value_name_log_luminescence
+Value_name_luminescence
+Value_name_FFL_silencing
+Value_name_Peptide_abundance
+Value_name_hEPO
+Value_name_FVII_silencing
+Value_name_GFP_delivery
+Value_name_Discretized_luminescence
+
+# Targets
+## Regression
+size
+quantified_delivery
+## PDI classification
+PDI_0_0to0_2
+PDI_0_2to0_3
+PDI_0_3to0_4
+PDI_0_4to0_5
+## EE classification
+Encapsulation_Efficiency_EE<50
+Encapsulation_Efficiency_50<=EE<80
+Encapsulation_Efficiency_80<EE<=100
+## Toxic
+toxic
+## Biodistribution
+Biodistribution_lymph_nodes
+Biodistribution_heart
+Biodistribution_liver
+Biodistribution_spleen
+Biodistribution_lung
+Biodistribution_kidney
+Biodistribution_muscle
diff --git a/data/processed/final/train.parquet b/data/processed/final/train.parquet
new file mode 100644
index 0000000..1123c72
Binary files /dev/null and b/data/processed/final/train.parquet differ
diff --git a/data/processed/final/val.parquet b/data/processed/final/val.parquet
new file mode 100644
index 0000000..25d4d41
Binary files /dev/null and b/data/processed/final/val.parquet differ
diff --git a/models/final/history.json b/models/final/history.json
new file mode 100644
index 0000000..8234202
--- /dev/null
+++ b/models/final/history.json
@@ -0,0 +1,720 @@
+{
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+}
\ No newline at end of file
diff --git a/models/final/loss_curves.png b/models/final/loss_curves.png
new file mode 100644
index 0000000..7a76a26
Binary files /dev/null and b/models/final/loss_curves.png differ
diff --git a/models/final/model.pt b/models/final/model.pt
new file mode 100644
index 0000000..0626353
Binary files /dev/null and b/models/final/model.pt differ
diff --git a/scripts/process_data_final.py b/scripts/process_data_final.py
new file mode 100644
index 0000000..6d71d32
--- /dev/null
+++ b/scripts/process_data_final.py
@@ -0,0 +1,153 @@
+"""最终训练数据处理脚本：train:val = 9:1，无测试集"""
+
+from pathlib import Path
+
+import pandas as pd
+import typer
+from loguru import logger
+
+from lnp_ml.config import INTERIM_DATA_DIR, PROCESSED_DATA_DIR
+from lnp_ml.dataset import (
+    process_dataframe,
+    SMILES_COL,
+    COMP_COLS,
+    HELP_COLS,
+    TARGET_REGRESSION,
+    TARGET_CLASSIFICATION_PDI,
+    TARGET_CLASSIFICATION_EE,
+    TARGET_TOXIC,
+    TARGET_BIODIST,
+    get_phys_cols,
+    get_exp_cols,
+)
+
+app = typer.Typer()
+
+
+@app.command()
+def main(
+    input_path: Path = INTERIM_DATA_DIR / "internal_corrected.csv",
+    output_dir: Path = PROCESSED_DATA_DIR / "final",
+    train_ratio: float = 0.9,
+    seed: int = 42,
+):
+    """
+    处理原始数据并划分训练/验证集（无测试集）。
+    
+    用于最终训练，使用所有数据。
+    
+    输出文件:
+        - final/train.parquet: 训练集 (90%)
+        - final/val.parquet: 验证集 (10%)
+        - final/feature_columns.txt: 特征列名配置
+    """
+    logger.info(f"Loading data from {input_path}")
+    df = pd.read_csv(input_path)
+    logger.info(f"Loaded {len(df)} samples")
+    
+    # 处理数据
+    logger.info("Processing dataframe...")
+    df = process_dataframe(df)
+    
+    # 定义要保留的列
+    phys_cols = get_phys_cols()
+    exp_cols = get_exp_cols()
+    
+    keep_cols = (
+        [SMILES_COL]
+        + COMP_COLS
+        + phys_cols
+        + HELP_COLS
+        + exp_cols
+        + TARGET_REGRESSION
+        + TARGET_CLASSIFICATION_PDI
+        + TARGET_CLASSIFICATION_EE
+        + [TARGET_TOXIC]
+        + TARGET_BIODIST
+    )
+    
+    # 只保留存在的列
+    keep_cols = [c for c in keep_cols if c in df.columns]
+    df = df[keep_cols]
+    
+    # 随机打乱并划分
+    logger.info(f"Splitting dataset (train:val = {train_ratio}:{1-train_ratio:.1f})...")
+    df = df.sample(frac=1, random_state=seed).reset_index(drop=True)
+    
+    n = len(df)
+    n_train = int(n * train_ratio)
+    
+    train_df = df.iloc[:n_train]
+    val_df = df.iloc[n_train:]
+    
+    logger.info(f"Train: {len(train_df)}, Val: {len(val_df)}")
+    
+    # 保存
+    output_dir.mkdir(parents=True, exist_ok=True)
+    
+    train_path = output_dir / "train.parquet"
+    val_path = output_dir / "val.parquet"
+    
+    train_df.to_parquet(train_path, index=False)
+    val_df.to_parquet(val_path, index=False)
+    
+    logger.success(f"Saved train to {train_path}")
+    logger.success(f"Saved val to {val_path}")
+    
+    # 保存列名配置
+    config_path = output_dir / "feature_columns.txt"
+    with open(config_path, "w") as f:
+        f.write("# Feature columns configuration (final training)\n\n")
+        f.write(f"# SMILES\n{SMILES_COL}\n\n")
+        f.write(f"# comp token [{len(COMP_COLS)}]\n")
+        f.write("\n".join(COMP_COLS) + "\n\n")
+        f.write(f"# phys token [{len(phys_cols)}]\n")
+        f.write("\n".join(phys_cols) + "\n\n")
+        f.write(f"# help token [{len(HELP_COLS)}]\n")
+        f.write("\n".join(HELP_COLS) + "\n\n")
+        f.write(f"# exp token [{len(exp_cols)}]\n")
+        f.write("\n".join(exp_cols) + "\n\n")
+        f.write("# Targets\n")
+        f.write("## Regression\n")
+        f.write("\n".join(TARGET_REGRESSION) + "\n")
+        f.write("## PDI classification\n")
+        f.write("\n".join(TARGET_CLASSIFICATION_PDI) + "\n")
+        f.write("## EE classification\n")
+        f.write("\n".join(TARGET_CLASSIFICATION_EE) + "\n")
+        f.write("## Toxic\n")
+        f.write(f"{TARGET_TOXIC}\n")
+        f.write("## Biodistribution\n")
+        f.write("\n".join(TARGET_BIODIST) + "\n")
+    
+    logger.success(f"Saved feature config to {config_path}")
+    
+    # 打印统计信息
+    logger.info("\n=== Dataset Statistics ===")
+    logger.info(f"Total samples: {n}")
+    logger.info(f"SMILES unique: {df[SMILES_COL].nunique()}")
+    
+    # 缺失值统计
+    logger.info("\nMissing values in targets:")
+    for col in TARGET_REGRESSION + [TARGET_TOXIC]:
+        if col in df.columns:
+            missing = df[col].isna().sum()
+            logger.info(f"  {col}: {missing} ({100*missing/n:.1f}%)")
+    
+    # PDI 分布
+    if all(c in df.columns for c in TARGET_CLASSIFICATION_PDI):
+        pdi_sum = df[TARGET_CLASSIFICATION_PDI].sum()
+        logger.info(f"\nPDI distribution:")
+        for col, count in pdi_sum.items():
+            logger.info(f"  {col}: {int(count)}")
+    
+    # EE 分布
+    if all(c in df.columns for c in TARGET_CLASSIFICATION_EE):
+        ee_sum = df[TARGET_CLASSIFICATION_EE].sum()
+        logger.info(f"\nEE distribution:")
+        for col, count in ee_sum.items():
+            logger.info(f"  {col}: {int(count)}")
+
+
+if __name__ == "__main__":
+    app()
+