datamol

Datamol 化学信息学技能

概述

Datamol 是一个 Python 库，为 RDKit 提供轻量、符合 Python 风格的分子化学信息学抽象层。通过合理的默认设置、高效的并行处理和现代化的 I/O 功能，简化复杂的分子操作。所有分子对象都是原生的 rdkit.Chem.Mol 实例，可确保与 RDKit 生态系统完全兼容。

主要功能：

• 分子格式转换（SMILES、SELFIES、InChI）
• 结构标准化和清理
• 分子描述符和指纹
• 3D 构象生成与分析
• 聚类和多样性选择
• 骨架和片段分析
• 化学反应应用
• 可视化和对齐
• 支持并行处理的批量处理
• 通过 fsspec 支持云存储

安装和设置

指导用户安装 datamol：

# Via conda/mamba (recommended)
conda install -c conda-forge datamol

# Via pip
pip install datamol

导入约定：

import datamol as dm

核心工作流

1. 分子基本操作

从 SMILES 创建分子：

import datamol as dm

# Single molecule
mol = dm.to_mol("CCO")  # Ethanol

# From list of SMILES
smiles_list = ["CCO", "c1ccccc1", "CC(=O)O"]
mols = [dm.to_mol(smi) for smi in smiles_list]

# Error handling
mol = dm.to_mol("invalid_smiles")  # Returns None
if mol is None:
    print("Failed to parse SMILES")

将分子转换为 SMILES：

# Canonical SMILES
smiles = dm.to_smiles(mol)

# Isomeric SMILES (includes stereochemistry)
smiles = dm.to_smiles(mol, isomeric=True)

# Other formats
inchi = dm.to_inchi(mol)
inchikey = dm.to_inchikey(mol)
selfies = dm.to_selfies(mol)

标准化和清理（始终建议用于用户提供的分子）：

# Sanitize molecule
mol = dm.sanitize_mol(mol)

# Full standardization (recommended for datasets)
mol = dm.standardize_mol(
    mol,
    disconnect_metals=True,
    normalize=True,
    reionize=True
)

# For SMILES strings directly
clean_smiles = dm.standardize_smiles(smiles)

2. 读取和写入分子文件

完整的 I/O 文档请参阅 references/io_module.md。

读取文件：

# SDF files (most common in chemistry)
df = dm.read_sdf("compounds.sdf", mol_column='mol')

# SMILES files
df = dm.read_smi("molecules.smi", smiles_column='smiles', mol_column='mol')

# CSV with SMILES column
df = dm.read_csv("data.csv", smiles_column="SMILES", mol_column="mol")

# Excel files
df = dm.read_excel("compounds.xlsx", sheet_name=0, mol_column="mol")

# Universal reader (auto-detects format)
df = dm.open_df("file.sdf")  # Works with .sdf, .csv, .xlsx, .parquet, .json

写入文件：

# Save as SDF
dm.to_sdf(mols, "output.sdf")
# Or from DataFrame
dm.to_sdf(df, "output.sdf", mol_column="mol")

# Save as SMILES file
dm.to_smi(mols, "output.smi")

# Excel with rendered molecule images
dm.to_xlsx(df, "output.xlsx", mol_columns=["mol"])

远程文件支持（S3、GCS、HTTP）：

# Read from cloud storage
df = dm.read_sdf("s3://bucket/compounds.sdf")
df = dm.read_csv("https://example.com/data.csv")

# Write to cloud storage
dm.to_sdf(mols, "s3://bucket/output.sdf")

3. 分子描述符和属性

详细的描述符文档请参阅 references/descriptors_viz.md。

计算单个分子的描述符：

# Get standard descriptor set
descriptors = dm.descriptors.compute_many_descriptors(mol)
# Returns: {'mw': 46.07, 'logp': -0.03, 'hbd': 1, 'hba': 1,
#           'tpsa': 20.23, 'n_aromatic_atoms': 0, ...}

批量计算描述符（推荐用于数据集）：

# Compute for all molecules in parallel
desc_df = dm.descriptors.batch_compute_many_descriptors(
    mols,
    n_jobs=-1,      # Use all CPU cores
    progress=True   # Show progress bar
)

特定描述符：

# Aromaticity
n_aromatic = dm.descriptors.n_aromatic_atoms(mol)
aromatic_ratio = dm.descriptors.n_aromatic_atoms_proportion(mol)

# Stereochemistry
n_stereo = dm.descriptors.n_stereo_centers(mol)
n_unspec = dm.descriptors.n_stereo_centers_unspecified(mol)

# Flexibility
n_rigid = dm.descriptors.n_rigid_bonds(mol)

药物相似性筛选（Lipinski 五法则）：

# Filter compounds
def is_druglike(mol):
    desc = dm.descriptors.compute_many_descriptors(mol)
    return (
        desc['mw'] <= 500 and
        desc['logp'] <= 5 and
        desc['hbd'] <= 5 and
        desc['hba'] <= 10
    )

druglike_mols = [mol for mol in mols if is_druglike(mol)]

4. 分子指纹和相似性

生成指纹：

# ECFP (Extended Connectivity Fingerprint, default)
fp = dm.to_fp(mol, fp_type='ecfp', radius=2, n_bits=2048)

# Other fingerprint types
fp_maccs = dm.to_fp(mol, fp_type='maccs')
fp_topological = dm.to_fp(mol, fp_type='topological')
fp_atompair = dm.to_fp(mol, fp_type='atompair')

相似性计算：

# Pairwise distances within a set
distance_matrix = dm.pdist(mols, n_jobs=-1)

# Distances between two sets
distances = dm.cdist(query_mols, library_mols, n_jobs=-1)

# Find most similar molecules
from scipy.spatial.distance import squareform
dist_matrix = squareform(dm.pdist(mols))
# Lower distance = higher similarity (Tanimoto distance = 1 - Tanimoto similarity)

5. 聚类和多样性选择

聚类详情请参阅 references/core_api.md。

Butina 聚类：

# Cluster molecules by structural similarity
clusters = dm.cluster_mols(
    mols,
    cutoff=0.2,    # Tanimoto distance threshold (0=identical, 1=completely different)
    n_jobs=-1      # Parallel processing
)

# Each cluster is a list of molecule indices
for i, cluster in enumerate(clusters):
    print(f"Cluster {i}: {len(cluster)} molecules")
    cluster_mols = [mols[idx] for idx in cluster]

重要提示：Butina 聚类会构建完整的距离矩阵，适用于约 1000 个分子，不适用于 10,000 个以上的分子。

多样性选择：

# Pick diverse subset
diverse_mols = dm.pick_diverse(
    mols,
    npick=100  # Select 100 diverse molecules
)

# Pick cluster centroids
centroids = dm.pick_centroids(
    mols,
    npick=50   # Select 50 representative molecules
)

6. 骨架分析

完整的骨架文档请参阅 references/fragments_scaffolds.md。

提取 Murcko 骨架：

# Get Bemis-Murcko scaffold (core structure)
scaffold = dm.to_scaffold_murcko(mol)
scaffold_smiles = dm.to_smiles(scaffold)

基于骨架的分析：

# Group compounds by scaffold
from collections import Counter

scaffolds = [dm.to_scaffold_murcko(mol) for mol in mols]
scaffold_smiles = [dm.to_smiles(s) for s in scaffolds]

# Count scaffold frequency
scaffold_counts = Counter(scaffold_smiles)
most_common = scaffold_counts.most_common(10)

# Create scaffold-to-molecules mapping
scaffold_groups = {}
for mol, scaf_smi in zip(mols, scaffold_smiles):
    if scaf_smi not in scaffold_groups:
        scaffold_groups[scaf_smi] = []
    scaffold_groups[scaf_smi].append(mol)

基于骨架划分训练集和测试集（用于机器学习）：

# Ensure train and test sets have different scaffolds
scaffold_to_mols = {}
for mol, scaf in zip(mols, scaffold_smiles):
    if scaf not in scaffold_to_mols:
        scaffold_to_mols[scaf] = []
    scaffold_to_mols[scaf].append(mol)

# Split scaffolds into train/test
import random
scaffolds = list(scaffold_to_mols.keys())
random.shuffle(scaffolds)
split_idx = int(0.8 * len(scaffolds))
train_scaffolds = scaffolds[:split_idx]
test_scaffolds = scaffolds[split_idx:]

# Get molecules for each split
train_mols = [mol for scaf in train_scaffolds for mol in scaffold_to_mols[scaf]]
test_mols = [mol for scaf in test_scaffolds for mol in scaffold_to_mols[scaf]]

7. 分子片段化

片段化详情请参阅 references/fragments_scaffolds.md。

BRICS 片段化（16 种键类型）：

# Fragment molecule
fragments = dm.fragment.brics(mol)
# Returns: set of fragment SMILES with attachment points like '[1*]CCN'

RECAP 片段化（11 种键类型）：

fragments = dm.fragment.recap(mol)

片段分析：

# Find common fragments across compound library
from collections import Counter

all_fragments = []
for mol in mols:
    frags = dm.fragment.brics(mol)
    all_fragments.extend(frags)

fragment_counts = Counter(all_fragments)
common_frags = fragment_counts.most_common(20)

# Fragment-based scoring
def fragment_score(mol, reference_fragments):
    mol_frags = dm.fragment.brics(mol)
    overlap = mol_frags.intersection(reference_fragments)
    return len(overlap) / len(mol_frags) if mol_frags else 0

8. 3D 构象生成

详细的构象文档请参阅 references/conformers_module.md。

生成构象：

# Generate 3D conformers
mol_3d = dm.conformers.generate(
    mol,
    n_confs=50,           # Number to generate (auto if None)
    rms_cutoff=0.5,       # Filter similar conformers (Ångströms)
    minimize_energy=True,  # Minimize with UFF force field
    method='ETKDGv3'      # Embedding method (recommended)
)

# Access conformers
n_conformers = mol_3d.GetNumConformers()
conf = mol_3d.GetConformer(0)  # Get first conformer
positions = conf.GetPositions()  # Nx3 array of atom coordinates

构象聚类：

# Cluster conformers by RMSD
clusters = dm.conformers.cluster(
    mol_3d,
    rms_cutoff=1.0,
    centroids=False
)

# Get representative conformers
centroids = dm.conformers.return_centroids(mol_3d, clusters)

SASA 计算：

# Calculate solvent accessible surface area
sasa_values = dm.conformers.sasa(mol_3d, n_jobs=-1)

# Access SASA from conformer properties
conf = mol_3d.GetConformer(0)
sasa = conf.GetDoubleProp('rdkit_free_sasa')

9. 可视化

可视化文档请参阅 references/descriptors_viz.md。

基本分子网格：

# Visualize molecules
dm.viz.to_image(
    mols[:20],
    legends=[dm.to_smiles(m) for m in mols[:20]],
    n_cols=5,
    mol_size=(300, 300)
)

# Save to file
dm.viz.to_image(mols, outfile="molecules.png")

# SVG for publications
dm.viz.to_image(mols, outfile="molecules.svg", use_svg=True)

对齐可视化（用于 SAR 分析）：

# Align molecules by common substructure
dm.viz.to_image(
    similar_mols,
    align=True,  # Enable MCS alignment
    legends=activity_labels,
    n_cols=4
)

高亮子结构：

# Highlight specific atoms and bonds
dm.viz.to_image(
    mol,
    highlight_atom=[0, 1, 2, 3],  # Atom indices
    highlight_bond=[0, 1, 2]      # Bond indices
)

构象可视化：

# Display multiple conformers
dm.viz.conformers(
    mol_3d,
    n_confs=10,
    align_conf=True,
    n_cols=3
)

10. 化学反应

反应文档请参阅 references/reactions_data.md。

应用反应：

from rdkit.Chem import rdChemReactions

# Define reaction from SMARTS
rxn_smarts = '[C:1](=[O:2])[OH:3]>>[C:1](=[O:2])[Cl:3]'
rxn = rdChemReactions.ReactionFromSmarts(rxn_smarts)

# Apply to molecule
reactant = dm.to_mol("CC(=O)O")  # Acetic acid
product = dm.reactions.apply_reaction(
    rxn,
    (reactant,),
    sanitize=True
)

# Convert to SMILES
product_smiles = dm.to_smiles(product)

批量应用反应：

# Apply reaction to library
products = []
for mol in reactant_mols:
    try:
        prod = dm.reactions.apply_reaction(rxn, (mol,))
        if prod is not None:
            products.append(prod)
    except Exception as e:
        print(f"Reaction failed: {e}")

并行处理

Datamol 为许多操作内置了并行处理功能。使用 n_jobs 参数：

• n_jobs=1：顺序执行（不进行并行处理）
• n_jobs=-1：使用所有可用的 CPU 核心
• n_jobs=4：使用 4 个核心

支持并行处理的函数：

• dm.read_sdf(..., n_jobs=-1)
• dm.descriptors.batch_compute_many_descriptors(..., n_jobs=-1)
• dm.cluster_mols(..., n_jobs=-1)
• dm.pdist(..., n_jobs=-1)
• dm.conformers.sasa(..., n_jobs=-1)

进度条：许多批量操作支持 progress=True 参数。

常见工作流和模式

完整流程：数据加载 → 筛选 → 分析

import datamol as dm
import pandas as pd

# 1. Load molecules
df = dm.read_sdf("compounds.sdf")

# 2. Standardize
df['mol'] = df['mol'].apply(lambda m: dm.standardize_mol(m) if m else None)
df = df[df['mol'].notna()]  # Remove failed molecules

# 3. Compute descriptors
desc_df = dm.descriptors.batch_compute_many_descriptors(
    df['mol'].tolist(),
    n_jobs=-1,
    progress=True
)

# 4. Filter by drug-likeness
druglike = (
    (desc_df['mw'] <= 500) &
    (desc_df['logp'] <= 5) &
    (desc_df['hbd'] <= 5) &
    (desc_df['hba'] <= 10)
)
filtered_df = df[druglike]

# 5. Cluster and select diverse subset
diverse_mols = dm.pick_diverse(
    filtered_df['mol'].tolist(),
    npick=100
)

# 6. Visualize results
dm.viz.to_image(
    diverse_mols,
    legends=[dm.to_smiles(m) for m in diverse_mols],
    outfile="diverse_compounds.png",
    n_cols=10
)

构效关系（SAR）分析

# Group by scaffold
scaffolds = [dm.to_scaffold_murcko(mol) for mol in mols]
scaffold_smiles = [dm.to_smiles(s) for s in scaffolds]

# Create DataFrame with activities
sar_df = pd.DataFrame({
    'mol': mols,
    'scaffold': scaffold_smiles,
    'activity': activities  # User-provided activity data
})

# Analyze each scaffold series
for scaffold, group in sar_df.groupby('scaffold'):
    if len(group) >= 3:  # Need multiple examples
        print(f"\nScaffold: {scaffold}")
        print(f"Count: {len(group)}")
        print(f"Activity range: {group['activity'].min():.2f} - {group['activity'].max():.2f}")

        # Visualize with activities as legends
        dm.viz.to_image(
            group['mol'].tolist(),
            legends=[f"Activity: {act:.2f}" for act in group['activity']],
            align=True  # Align by common substructure
        )

虚拟筛选工作流

# 1. Generate fingerprints for query and library
query_fps = [dm.to_fp(mol) for mol in query_actives]
library_fps = [dm.to_fp(mol) for mol in library_mols]

# 2. Calculate similarities
from scipy.spatial.distance import cdist
import numpy as np

distances = dm.cdist(query_actives, library_mols, n_jobs=-1)

# 3. Find closest matches (min distance to any query)
min_distances = distances.min(axis=0)
similarities = 1 - min_distances  # Convert distance to similarity

# 4. Rank and select top hits
top_indices = np.argsort(similarities)[::-1][:100]  # Top 100
top_hits = [library_mols[i] for i in top_indices]
top_scores = [similarities[i] for i in top_indices]

# 5. Visualize hits
dm.viz.to_image(
    top_hits[:20],
    legends=[f"Sim: {score:.3f}" for score in top_scores[:20]],
    outfile="screening_hits.png"
)

参考文档

如需详细的 API 文档，请查阅以下参考文件：

• references/core_api.md：核心命名空间函数（转换、标准化、指纹、聚类）
• references/io_module.md：文件输入/输出操作（读取/写入 SDF、CSV、Excel 和远程文件）
• references/conformers_module.md：3D 构象生成、聚类、SASA 计算
• references/descriptors_viz.md：分子描述符和可视化函数
• references/fragments_scaffolds.md：骨架提取、BRICS/RECAP 片段化
• references/reactions_data.md：化学反应和玩具数据集

最佳实践

1. 始终标准化来自外部来源的分子：

   mol = dm.standardize_mol(mol, disconnect_metals=True, normalize=True, reionize=True)
   

2. 检查分子解析后的 None 值：

   mol = dm.to_mol(smiles)
   if mol is None:
       # Handle invalid SMILES
   

3. 对大型数据集使用并行处理：

   result = dm.operation(..., n_jobs=-1, progress=True)
   

4. 利用 fsspec 处理云存储：

   df = dm.read_sdf("s3://bucket/compounds.sdf")
   

5. 根据相似性选择合适的指纹：

   • ECFP（Morgan）：通用，适用于结构相似性
   • MACCS：速度快，特征空间较小
   • 原子对：考虑原子对及其距离

6. 考虑规模限制：

   • Butina 聚类：约 1,000 个分子（完整距离矩阵）
   • 对于更大的数据集：使用多样性选择或层次方法

7. 用于机器学习的骨架拆分：确保按骨架正确划分训练集/测试集

8. 可视化 SAR 系列时对齐分子

错误处理

# Safe molecule creation
def safe_to_mol(smiles):
    try:
        mol = dm.to_mol(smiles)
        if mol is not None:
            mol = dm.standardize_mol(mol)
        return mol
    except Exception as e:
        print(f"Failed to process {smiles}: {e}")
        return None

# Safe batch processing
valid_mols = []
for smiles in smiles_list:
    mol = safe_to_mol(smiles)
    if mol is not None:
        valid_mols.append(mol)

与机器学习集成

# Feature generation
X = np.array([dm.to_fp(mol) for mol in mols])

# Or descriptors
desc_df = dm.descriptors.batch_compute_many_descriptors(mols, n_jobs=-1)
X = desc_df.values

# Train model
from sklearn.ensemble import RandomForestRegressor
model = RandomForestRegressor()
model.fit(X, y_target)

# Predict
predictions = model.predict(X_test)

故障排除

问题：分子解析失败

• 解决方案：先使用 dm.standardize_smiles()，或尝试 dm.fix_mol()

问题：聚类时发生内存错误

• 解决方案：对于较大的数据集，使用 dm.pick_diverse() 代替完整聚类

问题：构象生成速度慢

• 解决方案：减小 n_confs 或增大 rms_cutoff，以生成更少的构象

问题：远程文件访问失败

• 解决方案：确保已安装 fsspec 和相应的云服务提供商库（s3fs、gcsfs 等）

其他资源

• Datamol 文档：https://docs.datamol.io/
• RDKit 文档：https://www.rdkit.org/docs/
• GitHub 仓库：https://github.com/datamol-io/datamol