重构分子属性分析流程并更新目录结构

1. 目录结构调整：
   - 创建scripts目录统一存放分析脚本
   - 保持数据文件在原有目录结构中
   - 生成的CSV文件和PNG图表文件也放在scripts目录下

2. 功能改进：
   - 更新calculate_qed_values.py脚本，添加对参考分子SDF文件的处理
   - 修改analyze_qed_mw_distribution.py脚本，统一使用SDF文件stem名称作为参考分子标识符
   - 改进Vina得分提取逻辑，支持从SDF文件中提取所有构象的得分
   - 完善KDE分布图绘制，确保参考分子在所有图表中显示统一的名称

3. 文档更新：
   - 更新README.md中的目录结构说明
   - 更新命令行和API使用示例
   - 添加详细的使用说明和示例

4. 示例代码：
   - 更新example_api_usage.py以适应新的目录结构和API调用方式

scripts/qed.py

@@ -0,0 +1,201 @@
+#!/usr/bin/env python
+# -*- encoding: utf-8 -*-
+'''
+@file               :qed_calculator.py
+@Description        :QED calculator with joblib parallel support
+@Date               :2025/08/04
+@Author             :lyzeng
+'''
+
+from rdkit import Chem
+from rdkit.Chem import QED
+import pandas as pd
+from typing import List, Union, Tuple, Optional
+import joblib
+from joblib import Parallel, delayed
+import logging
+
+# 设置日志
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+
+def calculate_single_qed(smiles: str) -> Optional[Tuple[str, float]]:
+    """
+    Calculate QED value for a single molecule.
+    
+    Args:
+        smiles (str): SMILES representation of the molecule
+        
+    Returns:
+        tuple: (smiles, qed_value) or None if calculation fails
+    """
+    try:
+        mol = Chem.MolFromSmiles(smiles)
+        if mol is not None:
+            qed_value = QED.qed(mol)
+            return (smiles, qed_value)
+    except Exception as e:
+        logger.warning(f"Failed to calculate QED for {smiles}: {e}")
+        pass
+    return None
+
+
+def parallel_qed_calculation(
+    smiles_list: List[str], 
+    n_jobs: int = -1,
+    batch_size: Union[int, str] = "auto",
+    backend: str = "loky"
+) -> pd.DataFrame:
+    """
+    Calculate QED values for a list of SMILES in parallel using joblib.
+    
+    Args:
+        smiles_list (List[str]): List of SMILES strings
+        n_jobs (int): Number of parallel jobs. -1 means using all processors
+        batch_size (int or str): Batch size for parallel processing
+        backend (str): Joblib backend to use ('loky', 'threading', 'multiprocessing')
+        
+    Returns:
+        pd.DataFrame: DataFrame with 'smiles' and 'qed' columns
+    """
+    logger.info(f"Calculating QED values for {len(smiles_list)} molecules...")
+    
+    # 并行计算QED值
+    results = Parallel(
+        n_jobs=n_jobs,
+        batch_size=batch_size,
+        backend=backend
+    )(delayed(calculate_single_qed)(smiles) for smiles in smiles_list)
+    
+    # 过滤掉None结果
+    valid_results = [r for r in results if r is not None]
+    
+    if not valid_results:
+        logger.warning("No valid QED values calculated")
+        return pd.DataFrame(columns=['smiles', 'qed'])
+    
+    # 分离SMILES和QED值
+    smiles_values, qed_values = zip(*valid_results)
+    
+    # 创建DataFrame
+    df = pd.DataFrame({
+        'smiles': smiles_values,
+        'qed': qed_values
+    })
+    
+    logger.info(f"Successfully calculated QED values for {len(df)} molecules")
+    return df
+
+
+def calculate_qed_series(
+    smiles_series: Union[List[str], pd.Series],
+    n_jobs: int = -1,
+    batch_size: Union[int, str] = "auto",
+    backend: str = "loky"
+) -> pd.Series:
+    """
+    Calculate QED values for a pandas Series or list of SMILES and return as Series.
+    
+    Args:
+        smiles_series: Series or list of SMILES strings
+        n_jobs (int): Number of parallel jobs
+        batch_size (int or str): Batch size for parallel processing
+        backend (str): Joblib backend to use
+        
+    Returns:
+        pd.Series: Series of QED values with the same index as input (if Series)
+    """
+    if isinstance(smiles_series, pd.Series):
+        smiles_list = smiles_series.tolist()
+        original_index = smiles_series.index
+    else:
+        smiles_list = smiles_series
+        original_index = None
+    
+    # 计算QED值
+    results = Parallel(
+        n_jobs=n_jobs,
+        batch_size=batch_size,
+        backend=backend
+    )(delayed(calculate_single_qed)(smiles) for smiles in smiles_list)
+    
+    # 提取QED值（失败的计算返回None）
+    qed_values = [r[1] if r is not None else None for r in results]
+    
+    # 创建Series
+    if original_index is not None:
+        return pd.Series(qed_values, index=original_index, name='qed')
+    else:
+        return pd.Series(qed_values, name='qed')
+
+
+class QEDCalculator:
+    """
+    A class for calculating QED values with support for parallel processing and caching.
+    """
+    
+    def __init__(self, n_jobs: int = -1, batch_size: Union[int, str] = "auto"):
+        """
+        Initialize the QEDCalculator.
+        
+        Args:
+            n_jobs (int): Number of parallel jobs. -1 means using all processors
+            batch_size (int or str): Batch size for parallel processing
+        """
+        self.n_jobs = n_jobs
+        self.batch_size = batch_size
+        self.backend = "loky"
+        
+    def calculate(self, smiles_list: List[str]) -> pd.DataFrame:
+        """
+        Calculate QED values for a list of SMILES.
+        
+        Args:
+            smiles_list (List[str]): List of SMILES strings
+            
+        Returns:
+            pd.DataFrame: DataFrame with 'smiles' and 'qed' columns
+        """
+        return parallel_qed_calculation(
+            smiles_list, 
+            n_jobs=self.n_jobs, 
+            batch_size=self.batch_size,
+            backend=self.backend
+        )
+    
+    def calculate_series(self, smiles_series: Union[List[str], pd.Series]) -> pd.Series:
+        """
+        Calculate QED values for a pandas Series or list of SMILES and return as Series.
+        
+        Args:
+            smiles_series: Series or list of SMILES strings
+            
+        Returns:
+            pd.Series: Series of QED values
+        """
+        return calculate_qed_series(
+            smiles_series,
+            n_jobs=self.n_jobs,
+            batch_size=self.batch_size,
+            backend=self.backend
+        )
+    
+"""
+usage
+
+from utils.qed_calculator import parallel_qed_calculation, QEDCalculator
+
+# 方式1：直接使用函数
+smiles_list = ['CCO', 'CCN', 'CCC']
+qed_df = parallel_qed_calculation(smiles_list, n_jobs=-1)
+
+# 方式2：使用类
+calculator = QEDCalculator(n_jobs=-1)
+qed_df = calculator.calculate(smiles_list)
+
+# 方式3：处理pandas Series
+import pandas as pd
+smiles_series = pd.Series(['CCO', 'CCN', 'CCC'], name='smiles')
+qed_series = calculator.calculate_series(smiles_series)
+"""