chem-data-extractor（化学数据提取器）

Chemistry Data Extractor | 化学数据提取器

Extract structured chemical characterization data from chemistry supplementary materials and return in strict JSON format. 从化学论文补充材料中提取结构化表征数据，以严格JSON格式返回。

Supported Data Fields

• compound_name: Full IUPAC or common name (including stereochemistry if given)
• structure_image_description: Brief description of the molecular structure
• physical_state: e.g., "white solid", "colorless oil"
• mass_obtained: in mg
• yield_percent: as number only
• melting_point_range: in °C, as string like "126.6–127.3"
• rf_value: Rf value and solvent system
• optical_rotation: [α]D²⁵ value, concentration, solvent
• hplc_conditions: column, mobile phase, flow rate, wavelength, retention times (major/minor), ee%
• nmr_1H: frequency, solvent, chemical shifts with multiplicity and coupling constants
• nmr_13C: frequency, solvent, chemical shifts with notes (e.g., d, JCF)
• nmr_19F: frequency, solvent, chemical shift
• hrms_data: ion type, calculated m/z, found m/z, formula
• racemic_sample_note: if mentioned

Workflow

Step 1: Ask User for Extraction Mode

When user asks to extract chemistry data, first ask:

Do you want to extract data for: 1. A specific compound (provide compound ID like "3i" or "1a") 2. All compounds in a single document 3. Batch process multiple PDF files (creates folder for each)

Mode 1: Batch Process Multiple PDFs

For processing multiple PDF files at once. Creates a separate folder for each PDF with extracted compounds.

Usage

python scripts/batch_extract.py \
    /path/to/pdf_folder \
    -o ./output_folder

Options

• -o, --output: Output base directory (default: ./chem_extract_output)
• --keep-md: Keep intermediate markdown files (default: cleanup after extraction)
• --skip-existing: Skip PDFs that already have output folders

Output Structure

output_folder/
├── batch_summary.json          # Overall summary of all processed PDFs
├── paper1/
│   ├── compounds.json          # All extracted compounds
│   └── summary.json            # Brief summary with compound list
├── paper2/
│   ├── compounds.json
│   └── summary.json
└── ...

Example: Batch Process

# Process all PDFs in a folder
python scripts/batch_extract.py ./pdfs/ -o ./extracted_data

# Process single PDF
python scripts/batch_extract.py ./article.pdf -o ./results

# Keep intermediate files, skip existing
python scripts/batch_extract.py ./pdfs/ --keep-md --skip-existing

Mode 2: Single Document Processing

For processing a single document (Markdown or after PDF conversion).

Step 2: Prepare Input File

If the input is a PDF file:

1. Use mineru-pdf-converter skill to convert PDF to Markdown first
2. Use the generated full.md file as input

If the input is already a Markdown file, use it directly.

Step 3: Extract Data

Use the extraction script to parse the data:

# Extract a specific compound
python scripts/extract_chem_data.py \
    /path/to/full.md -c COMPOUND_ID --compact

# Extract all compounds
python scripts/extract_chem_data.py \
    /path/to/full.md --compact

Options:

• -c, --compound: Extract specific compound by ID (e.g., "3i", "1a")
• --compact: Remove null/empty fields from output
• -o, --output: Save output to file instead of stdout

Step 4: Return Results

Output ONLY valid JSON without any extra text, unless the user specifically asks for explanations.

Examples

Example 1: Batch Process Multiple PDFs

# Process all PDFs in a directory
python scripts/batch_extract.py ./supplementary_pdfs/ -o ./extracted_compounds

# Output structure:
# ./extracted_compounds/
# ├── batch_summary.json
# ├── paper1/
# │   ├── compounds.json
# │   └── summary.json
# └── paper2/
#     ├── compounds.json
#     └── summary.json

Example 2: Extract Single Compound from Markdown

python scripts/extract_chem_data.py full.md -c 3i --compact

Output:

{
  "compound_name": "(R)-N-Benzoyl-4-iodobenzenesulfonimidoyl fluoride",
  "physical_state": "white solid",
  "mass_obtained": 33.2,
  "yield_percent": 85,
  "melting_point_range": "126.6–127.3",
  "rf_value": "0.37 (Pet/EtOAc, 5/1, v/v)",
  "optical_rotation": "[α]D25 = +10.3 (c = 0.75, CHCl3)",
  "hplc_conditions": {
    "column": "CHIRALCEL AY-RH",
    "mobile_phase": "n-hexane/2-propanol = 60/40",
    "flow_rate": "1.0 mL/min",
    "wavelength": "256 nm",
    "retention_times": {
      "major": "9.642 min",
      "minor": "12.955 min"
    },
    "ee_percent": 90
  },
  "nmr_1H": {
    "frequency": "400 MHz",
    "solvent": "CDCl3",
    "chemical_shifts": "δ 8.17–8.09 (m, 2H), 8.06–8.00 (m, 2H)..."
  },
  "nmr_13C": {
    "frequency": "100 MHz",
    "solvent": "CDCl3",
    "chemical_shifts": "δ 170.0, 139.2, 134.3 (d, JCF = 22.0 Hz)..."
  },
  "nmr_19F": {
    "frequency": "376 MHz",
    "solvent": "CDCl3",
    "chemical_shift": "δ 65.3 (s, 1F)"
  },
  "hrms_data": {
    "ion_type": "[M+Na]+",
    "calculated_mz": 411.9275,
    "found_mz": 411.9273,
    "formula": "C13H9FINNaO2S"
  }
}

Example 3: Extract All Compounds from Single File

python scripts/extract_chem_data.py full.md --compact -o all_compounds.json

Output is a JSON array containing all extracted compounds.

Common Compound ID Patterns

Compound IDs typically follow these patterns:

• Numbers: 1, 2, 3
• Numbers with letters: 1a, 3i, 5b
• Numbers with multiple letters: 1aa, 3ba
• Numbers with primes: 1', 2''

Tips

1. Compound identification: The script looks for section headers like (R)-Compound Name (3i) or # Compound Name (1a)
2. Data completeness: Not all fields may be present for every compound - missing fields will be null (or omitted with --compact)
3. Stereochemistry: The script preserves stereochemical descriptors like (R), (S), (±) in compound names
4. Multiple compounds: When extracting all compounds, the output is a JSON array sorted by appearance in document