MolParser

MolParser is a toolkit for working with E-SMILES (extended SMILES) in OCSR and Markush workflows. The notation follows the formulation introduced in the MolParser paper.

Path	Role
utils/	MolParser utils (substitute known abbreviations, and render E-SMILES to structure)
skills/molparser-extended-smiles/	E-SMILES skills (concise rules and examples for LLM / OCSR agents)

Installation

pip install -r requirements.txt

Run the examples below from the repository root so that from utils import ... resolves correctly.

E-SMILES overview

E-SMILES combines a base SMILES with an optional extension:

SMILES<sep>EXTENSION

Common extension records:

• <a>0:R[1]</a> — atom-indexed substituent or Markush placeholder
• <r>0:R[1]</r> — ring-indexed substituent (regio-uncertain attachment)
• <c>9:B</c> — abstract-ring or superatom placeholder
• <a>0:<dum></a> — explicit dummy attachment point
• |Sg:n| — structural repeating unit (SRU) marker

Full specification: [skills/molparser-extended-smiles/extended-smiles-spec.md](skills/molparser-extended-smiles/extended-smiles-spec.md)

Quick start

Post-process E-SMILES

Normalize an E-SMILES string and substitute known abbreviations. In this example, CF3 is read from utils/abbrevs_example.csv, attached to the dummy atom *, and folded into ordinary SMILES. No unresolved Markush group remains, so markush is False.

from utils import postprocess_caption

raw = "*c1ccccc1<sep><a>0:CF3</a>"
result = postprocess_caption(raw)

# caption: original input string
# smi: normalized RDKit SMILES after substituting known abbreviations
# esmi: normalized E-SMILES after substitution and index repair
# cxsmiles: CXSMILES generated from the normalized E-SMILES
# markush: True if unresolved Markush labels remain
# sru: True if a structural repeating unit marker was detected
# groups: unresolved E-SMILES extension records kept after normalization
for key in ("caption", "smi", "esmi", "cxsmiles", "markush", "sru", "groups"):
    print(f"{key}: {result[key]}")

Expected output:

caption: *c1ccccc1<sep><a>0:CF3</a>
smi: FC(F)(F)c1ccccc1
esmi: FC(F)(F)c1ccccc1<sep>
cxsmiles: FC(F)(F)c1ccccc1
markush: False
sru: False
groups:

Render E-SMILES

Render the E-SMILES as SVG and save it locally:

from pathlib import Path
from utils import draw

svg_text = draw("*C(O)c1cc(C(=O)N(*)*)cc(-c2*ccc*2)c1<sep><a>0:CF3</a><a>9:R[3]</a><a>10:R[2]</a><a>14:X</a><a>18:Y</a><r>1:R[1]?1-3</r>", output_format="svg")

svg_path = Path("molecule.svg")
svg_path.write_text(svg_text, encoding="utf-8")

molecule.svg is a local render artifact.

To obtain a PNG from that SVG (requires cairosvg from requirements.txt):

import cairosvg

png_path = Path("molecule.png")
cairosvg.svg2png(url=str(svg_path), write_to=str(png_path))

LLM / OCSR workflow

Skill context

Load these files for the agent:

• skills/molparser-extended-smiles/SKILL.md
• skills/molparser-extended-smiles/extended-smiles-spec.md
• skills/molparser-extended-smiles/figure-index.md

Expected model output

1. Base SMILES
2. E-SMILES in SMILES<sep>EXTENSION format
3. Markush status
4. Unsupported or ambiguous chemistry

Validate and normalize

python skills/molparser-extended-smiles/validate_esmiles.py "<your_esmiles>"

Then normalize and render with postprocess_caption and draw.

Related resources

• Uni-Parser — agent-oriented scientific document parsing with the latest MolParser. Demo
• MolParser — end-to-end molecular recognition. Demo
• MolDetv2 weights — lightweight molecule detector. Demo