476 lines
16 KiB
Markdown
476 lines
16 KiB
Markdown
# MacrolactoneDB 12-20元环验证方案设计
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## 1. 验证目标与范围
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### 1.1 目标
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- 验证 `macro_lactone_toolkit` 对 MacrolactoneDB 12-20元环大环内酯的识别准确性
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- 验证环编号正确性（位置1=羰基碳，位置2=酯键氧）
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- 验证侧链断裂功能（仅针对标准大环内酯）
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- 使用同位素标记技术标记裂解位置，便于后续拼接
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### 1.2 范围
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- **数据来源**: `/data/MacrolactoneDB/ring12_20/temp.csv` (11,037分子)
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- **抽样策略**: 分层随机抽样10% (~1,100分子)，按环大小12-20均匀分布
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- **处理对象**: 仅处理 `classification="standard_macrolactone"` 的分子
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- **输出**: 可视化图片 + SQLite数据库 + 汇总统计
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## 2. 数据库设计 (SQLModel)
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### 2.1 模型定义
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```python
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from typing import Optional, List
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from sqlmodel import SQLModel, Field, Relationship
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from datetime import datetime
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from enum import Enum
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class ClassificationType(str, Enum):
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STANDARD = "standard_macrolactone"
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NON_STANDARD = "non_standard_macrocycle"
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NOT_MACROLACTONE = "not_macrolactone"
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class ProcessingStatus(str, Enum):
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PENDING = "pending"
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SUCCESS = "success"
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FAILED = "failed"
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SKIPPED = "skipped"
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# ==================== 主分子表 ====================
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class ParentMolecule(SQLModel, table=True):
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"""原始分子信息表"""
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__tablename__ = "parent_molecules"
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id: Optional[int] = Field(default=None, primary_key=True)
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# 原始数据
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source_id: str = Field(index=True) # 来自CSV的IDs字段
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molecule_name: Optional[str] = None # molecule_pref_name
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smiles: str = Field(index=True)
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# 分类结果
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classification: ClassificationType = Field(index=True)
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ring_size: Optional[int] = Field(default=None, index=True)
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primary_reason_code: Optional[str] = None
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primary_reason_message: Optional[str] = None
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# 处理状态
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processing_status: ProcessingStatus = Field(default=ProcessingStatus.PENDING)
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error_message: Optional[str] = None
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# 元数据
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created_at: datetime = Field(default_factory=datetime.utcnow)
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processed_at: Optional[datetime] = None
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# 关系
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fragments: List["SideChainFragment"] = Relationship(back_populates="parent")
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numbering: Optional["RingNumbering"] = Relationship(back_populates="parent")
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# ==================== 环编号表 ====================
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class RingNumbering(SQLModel, table=True):
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"""环编号详细信息"""
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__tablename__ = "ring_numberings"
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id: Optional[int] = Field(default=None, primary_key=True)
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parent_id: int = Field(foreign_key="parent_molecules.id", unique=True)
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# 环基本信息
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ring_size: int
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carbonyl_carbon_idx: int # 位置1
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ester_oxygen_idx: int # 位置2
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# 原子映射 (JSON存储)
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position_to_atom: str # JSON: {"1": 5, "2": 10, ...}
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atom_to_position: str # JSON: {"5": 1, "10": 2, ...}
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# 关系
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parent: Optional[ParentMolecule] = Relationship(back_populates="numbering")
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# ==================== 侧链片段表 ====================
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class SideChainFragment(SQLModel, table=True):
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"""裂解产生的侧链片段"""
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__tablename__ = "side_chain_fragments"
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id: Optional[int] = Field(default=None, primary_key=True)
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parent_id: int = Field(foreign_key="parent_molecules.id", index=True)
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# 片段标识
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fragment_id: str = Field(index=True) # {source_id}_frag_{n}
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# 裂解位置信息
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cleavage_position: int = Field(index=True) # 环上的断裂位置
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attachment_atom_idx: int # 母环上的连接原子索引
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attachment_atom_symbol: str # C, O, N等
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# SMILES (关键：同位素标记用于标识裂解位置)
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fragment_smiles_labeled: str # 带同位素标记，如 [5*]CCO
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fragment_smiles_plain: str # 无标记，如 *CCO
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# 同位素标记值 (用于后续拼接)
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dummy_isotope: int # 裂解位置的编号，用于重建连接关系
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# 物理化学性质
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atom_count: int
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heavy_atom_count: int
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molecular_weight: float
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# 连接信息 (用于后续拼接)
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original_bond_type: str # SINGLE, DOUBLE, AROMATIC等
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# 关系
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parent: Optional[ParentMolecule] = Relationship(back_populates="fragments")
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# ==================== 验证结果表 ====================
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class ValidationResult(SQLModel, table=True):
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"""人工验证结果记录"""
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__tablename__ = "validation_results"
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id: Optional[int] = Field(default=None, primary_key=True)
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parent_id: int = Field(foreign_key="parent_molecules.id")
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# 验证字段
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numbering_correct: Optional[bool] = None # 编号是否正确
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cleavage_correct: Optional[bool] = None # 裂解位置是否正确
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classification_correct: Optional[bool] = None # 分类是否正确
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# 备注
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notes: Optional[str] = None
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validated_by: Optional[str] = None
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validated_at: Optional[datetime] = None
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```
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### 2.2 数据库初始化
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```python
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from sqlmodel import create_engine, Session, SQLModel
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from contextlib import contextmanager
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# SQLite文件数据库
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DATABASE_URL = "sqlite:///./validation_output/fragments.db"
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engine = create_engine(DATABASE_URL, echo=False)
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@contextmanager
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def get_session():
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with Session(engine) as session:
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yield session
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def init_database():
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"""创建所有表"""
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SQLModel.metadata.create_all(engine)
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```
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## 3. 同位素标记方案 (借鉴Molassembler)
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### 3.1 标记策略
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```python
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def build_fragment_smiles_with_isotope(
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mol: Chem.Mol,
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side_chain_atoms: list[int],
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side_chain_start_idx: int,
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ring_atom_idx: int,
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cleavage_position: int, # 使用位置编号作为同位素值
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) -> str:
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"""
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构建带同位素标记的片段SMILES
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关键：用cleavage_position作为dummy原子的同位素值
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这样后续拼接时能精确知道片段来自哪个位置
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"""
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# 创建可编辑分子
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emol = Chem.EditableMol(Chem.Mol(mol))
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# 添加dummy原子替代连接点
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dummy_atom = Chem.Atom(0) # 原子序数0 = dummy
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dummy_atom.SetIsotope(cleavage_position) # 【关键】用位置编号标记
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dummy_idx = emol.AddAtom(dummy_atom)
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# 获取原始键类型
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bond = mol.GetBondBetweenAtoms(ring_atom_idx, side_chain_start_idx)
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bond_type = bond.GetBondType()
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# 添加dummy原子与侧链起始原子的键
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emol.AddBond(dummy_idx, side_chain_start_idx, bond_type)
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# 只保留dummy原子和侧链原子
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atoms_to_keep = set([dummy_idx] + list(side_chain_atoms))
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# 标记要删除的原子
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for atom_idx in range(mol.GetNumAtoms()):
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if atom_idx not in atoms_to_keep:
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emol.RemoveAtom(atom_idx)
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fragment = emol.GetMol()
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Chem.SanitizeMol(fragment)
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return Chem.MolToSmiles(fragment)
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```
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### 3.2 标记示例
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| 裂解位置 | 原始结构 | 标记后SMILES | 说明 |
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|---------|---------|-------------|------|
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| 5 | `...C5(CCO)...` | `[5*]CCO` | dummy同位素=5，表示位置5的侧链 |
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| 12 | `...C12(CCCO)...` | `[12*]CCCO` | dummy同位素=12，表示位置12的侧链 |
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### 3.3 拼接时使用标记
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```python
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# 后续拼接时，可以通过同位素值找到对应的裂解位置
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def find_attachment_position(fragment_smiles: str) -> int:
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"""从片段SMILES中提取裂解位置"""
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mol = Chem.MolFromSmiles(fragment_smiles)
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for atom in mol.GetAtoms():
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if atom.GetAtomicNum() == 0 and atom.GetIsotope() > 0:
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return atom.GetIsotope() # 返回位置编号
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return 0
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```
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## 4. 输出目录结构
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```
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validation_output/
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├── README.md # 目录结构说明
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├── fragments.db # SQLite数据库
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├── summary.csv # 主汇总表 (所有分子)
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├── summary_statistics.json # 统计信息
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│
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├── ring_size_12/ # 12元环
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├── ring_size_13/ # 13元环
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├── ring_size_14/ # 14元环
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├── ring_size_15/ # 15元环
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├── ring_size_16/ # 16元环
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├── ring_size_17/ # 17元环
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├── ring_size_18/ # 18元环
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├── ring_size_19/ # 19元环
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└── ring_size_20/ # 20元环
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│
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├── molecules.csv # 该环大小的所有分子
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│
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├── standard/ # 标准大环内酯
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│ ├── numbered/ # 带编号的高亮环图
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│ │ ├── {source_id}_numbered.png
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│ │ └── ...
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│ │
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│ └── sidechains/ # 侧链片段图
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│ └── {source_id}/
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│ ├── {source_id}_frag_0_pos{pos}.png # 位置信息在文件名
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│ ├── {source_id}_frag_1_pos{pos}.png
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│ └── ...
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│
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├── non_standard/ # 非标准大环
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│ └── original/
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│ ├── {source_id}_original.png
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│ └── ...
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│
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└── rejected/ # 被拒绝的分子
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└── original/
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├── {source_id}_original.png
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└── ...
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```
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## 5. CSV字段设计
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### 5.1 summary.csv
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| 字段名 | 类型 | 说明 |
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|--------|------|------|
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| `id` | int | 数据库主键 |
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| `source_id` | str | 原始IDs字段 |
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| `molecule_name` | str | 分子名称 |
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| `smiles` | str | 原始SMILES |
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| `classification` | str | standard/non_standard/not_macrolactone |
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| `ring_size` | int | 检测到的环大小 |
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| `primary_reason_code` | str | 分类原因代码 |
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| `primary_reason_message` | str | 分类原因描述 |
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| `processing_status` | str | pending/success/failed/skipped |
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| `error_message` | str | 错误信息 |
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| `num_sidechains` | int | 侧链数量 |
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| `cleavage_positions` | str | JSON数组 [5, 8, 12] |
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| `numbered_image_path` | str | 编号图相对路径 |
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| `processed_at` | datetime | 处理时间 |
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### 5.2 fragments.csv (每个分子的侧链详情)
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| 字段名 | 类型 | 说明 |
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|--------|------|------|
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| `fragment_id` | str | 唯一标识 |
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| `source_id` | str | 母分子ID |
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| `cleavage_position` | int | 环上断裂位置 |
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| `attachment_atom_idx` | int | 连接原子索引 |
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| `attachment_atom_symbol` | str | 连接原子类型 |
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| `fragment_smiles_labeled` | str | 带同位素标记的SMILES |
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| `fragment_smiles_plain` | str | 无标记SMILES |
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| `dummy_isotope` | int | 标记值=裂解位置 |
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| `atom_count` | int | 原子数 |
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| `molecular_weight` | float | 分子量 |
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| `original_bond_type` | str | 原始键类型 |
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| `image_path` | str | 片段图路径 |
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## 6. 验证脚本架构
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```python
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#!/usr/bin/env python3
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"""
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MacrolactoneDB 12-20元环验证脚本
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使用: pixi run python scripts/validate_macrolactone_db.py
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"""
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import pandas as pd
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from sqlmodel import Session
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from macro_lactone_toolkit import MacroLactoneAnalyzer, MacrolactoneFragmenter
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from macro_lactone_toolkit.visualization import save_numbered_molecule_png
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class MacrolactoneValidator:
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def __init__(self, sample_ratio=0.1, output_dir="validation_output"):
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self.analyzer = MacroLactoneAnalyzer()
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self.fragmenter = MacrolactoneFragmenter()
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self.sample_ratio = sample_ratio
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self.output_dir = Path(output_dir)
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def run(self, input_csv: str):
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# 1. 加载数据
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df = pd.read_csv(input_csv)
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# 2. 分层抽样
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sampled = self._stratified_sample(df)
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# 3. 初始化数据库
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init_database()
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# 4. 处理每个分子
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for _, row in sampled.iterrows():
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self._process_molecule(row)
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# 5. 生成汇总
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self._generate_summary()
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def _stratified_sample(self, df: pd.DataFrame) -> pd.DataFrame:
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"""按环大小分层抽样"""
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# 先分类所有分子
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df['classification'] = df['smiles'].apply(
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lambda s: self.analyzer.classify_macrocycle(s).classification
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)
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df['ring_size'] = df['smiles'].apply(
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lambda s: self.analyzer.classify_macrocycle(s).ring_size
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)
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# 按ring_size分层，每层抽10%
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sampled = df.groupby('ring_size').apply(
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lambda x: x.sample(frac=self.sample_ratio, random_state=42)
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).reset_index(drop=True)
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return sampled
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def _process_molecule(self, row: pd.Series):
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"""处理单个分子"""
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source_id = row['IDs']
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smiles = row['smiles']
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classification = row['classification']
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ring_size = row['ring_size']
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# 保存到ParentMolecule表
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parent = ParentMolecule(
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source_id=source_id,
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molecule_name=row.get('molecule_pref_name'),
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smiles=smiles,
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classification=classification,
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ring_size=ring_size,
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)
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if classification != ClassificationType.STANDARD:
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parent.processing_status = ProcessingStatus.SKIPPED
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self._save_image(smiles, ring_size, source_id, classification)
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return
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# 处理标准大环内酯
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try:
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# 环编号
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numbering = self.fragmenter.number_molecule(smiles)
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self._save_numbering_to_db(parent.id, numbering)
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# 生成编号图
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self._save_numbered_image(smiles, ring_size, source_id)
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# 侧链断裂
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result = self.fragmenter.fragment_molecule(smiles, parent_id=source_id)
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self._save_fragments_to_db(parent.id, result)
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self._save_fragment_images(result, source_id)
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parent.processing_status = ProcessingStatus.SUCCESS
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parent.num_sidechains = len(result.fragments)
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parent.cleavage_positions = json.dumps([f.cleavage_position for f in result.fragments])
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except Exception as e:
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parent.processing_status = ProcessingStatus.FAILED
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parent.error_message = str(e)
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parent.processed_at = datetime.utcnow()
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with get_session() as session:
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session.add(parent)
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session.commit()
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```
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## 7. 执行命令
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```bash
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# 进入项目目录
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cd /Users/lingyuzeng/project/macro-lactone-sidechain-profiler/macro_split
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# 激活pixi环境
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pixi shell
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# 运行验证脚本
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python scripts/validate_macrolactone_db.py \
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--input data/MacrolactoneDB/ring12_20/temp.csv \
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--output validation_output \
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--sample-ratio 0.1
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# 查看数据库
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sqlite3 validation_output/fragments.db ".tables"
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sqlite3 validation_output/fragments.db "SELECT * FROM parent_molecules LIMIT 5;"
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```
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## 8. 人工检查清单
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### 8.1 编号正确性检查
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- [ ] 位置1是否为内酯羰基碳（C=O）
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- [ ] 位置2是否为酯键氧（-O-）
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- [ ] 编号是否沿统一方向连续
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- [ ] 桥环/非标准环是否被正确跳过
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### 8.2 裂解正确性检查
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- [ ] 位置1和2是否有侧链（应该没有，是内酯本身）
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- [ ] 位置3-N的侧链是否正确识别
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- [ ] dummy原子是否正确标记裂解位置
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- [ ] 键型是否保持（单键/双键）
|
||
|
||
### 8.3 分类准确性检查
|
||
- [ ] 标准大环内酯是否被正确识别
|
||
- [ ] 非标准大环是否被正确分类并跳过
|
||
- [ ] 非大环内酯是否被拒绝
|
||
|
||
## 9. 后续拼接接口设计
|
||
|
||
```python
|
||
def get_fragment_by_position(db_path: str, source_id: str, position: int) -> Optional[SideChainFragment]:
|
||
"""通过位置获取片段，用于后续拼接"""
|
||
engine = create_engine(f"sqlite:///{db_path}")
|
||
with Session(engine) as session:
|
||
statement = select(SideChainFragment).where(
|
||
SideChainFragment.parent_id == source_id,
|
||
SideChainFragment.cleavage_position == position
|
||
)
|
||
return session.exec(statement).first()
|
||
|
||
def get_all_positions_for_parent(db_path: str, source_id: str) -> List[int]:
|
||
"""获取某分子的所有裂解位置"""
|
||
engine = create_engine(f"sqlite:///{db_path}")
|
||
with Session(engine) as session:
|
||
statement = select(SideChainFragment.cleavage_position).where(
|
||
SideChainFragment.parent_id == source_id
|
||
)
|
||
return [r[0] for r in session.exec(statement).all()]
|
||
```
|