Rdkit Repr

RDKit Molecular Featurization

This skill provides practical command patterns for RDKit descriptor and fingerprint extraction

using the standardized CLI wrapper: <skill_path>/scripts/rdkit_helper.py.

Key behaviors (important for Agents):

• The script prints environment detection (Python/RDKit/NumPy/Pandas) by default.
• Bad/illegal SMILES are skipped and logged to *.skipped.csv (no crash).
• Each run ends by printing absolute output paths like:
• [RESULT] desc_csv=/abs/path.csv
• [RESULT] fp_npy=/abs/path.npy
• [RESULT] fp_csv=/abs/path.csv

Quick Start

Check CLI help:

uv run <skill_path>/scripts/rdkit_helper.py --help

Check subcommand help:

uv run <skill_path>/scripts/rdkit_helper.py desc --help
uv run <skill_path>/scripts/rdkit_helper.py fp --help
uv run <skill_path>/scripts/rdkit_helper.py list-desc --help

Disable environment printing (optional):

uv run <skill_path>/scripts/rdkit_helper.py --no-env desc --smiles "CCO" --output out.csv

Core Tasks

1) Compute physicochemical descriptors → .csv

Single SMILES (default preset: physchem, 25 descriptors):

uv run <skill_path>/scripts/rdkit_helper.py desc \
    --smiles "CCO" \
    --output /tmp/CCO.desc.csv

From CSV (default SMILES column is smiles):

uv run <skill_path>/scripts/rdkit_helper.py desc \
    --file data.csv \
    --smiles-col smiles \
    --output data.desc.csv

From SMI:

uv run <skill_path>/scripts/rdkit_helper.py desc \
    --file molecules.smi \
    --output molecules.desc.csv

Choose a descriptor preset:

# Lipinski drug-likeness (6 descriptors: MolWt, MolLogP, NumHDonors, ...)
uv run <skill_path>/scripts/rdkit_helper.py desc \
    --file data.csv --preset lipinski --output data.lipinski.csv

# Extended physicochemical (25 descriptors, default)
uv run <skill_path>/scripts/rdkit_helper.py desc \
    --file data.csv --preset physchem --output data.physchem.csv

# Topological / graph indices (56 descriptors: BalabanJ, BertzCT, Chi*, PEOE_VSA*, ...)
uv run <skill_path>/scripts/rdkit_helper.py desc \
    --file data.csv --preset topological --output data.topo.csv

# All RDKit descriptors (~200 descriptors)
uv run <skill_path>/scripts/rdkit_helper.py desc \
    --file data.csv --preset all --output data.all_desc.csv

Select specific descriptors (overrides --preset):

uv run <skill_path>/scripts/rdkit_helper.py desc \
    --file data.csv \
    --descriptors "MolWt,MolLogP,TPSA,NumHDonors,NumHAcceptors" \
    --output data.custom.csv

Suppress merging back original CSV columns (output only smiles + descriptors):

uv run <skill_path>/scripts/rdkit_helper.py desc \
    --file data.csv --preset physchem --no-merge --output data.desc_only.csv

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2) Compute molecular fingerprints → .npy or .csv

Available fingerprint types:

| Type | Description | Default bits |

| --------------- | ---------------------------------------------------------- | ------------ |

| morgan2 | Morgan circular FP radius 2 (ECFP4-like), bit vector | 2048 |

| morgan3 | Morgan circular FP radius 3 (ECFP6-like), bit vector | 2048 |

| morgan2_count | Morgan radius-2 count vector | 2048 |

| rdkit | RDKit path-based FP, bit vector | 2048 |

| maccs | MACCS 167 structural keys (bit vector, --nbits ignored) | 167 |

| topological | Topological torsion FP (count vector, hashed to --nbits) | 2048 |

| atompair | Atom-pair FP (count vector, hashed to --nbits) | 2048 |

| layered | Layered substructure FP, bit vector | 2048 |

| pattern | SMARTS pattern FP, bit vector | 2048 |

Single SMILES, output as NumPy array (.npy):

uv run <skill_path>/scripts/rdkit_helper.py fp \
    --smiles "CCO" \
    --type morgan2 \
    --output /tmp/CCO.morgan2.npy

From CSV, Morgan ECFP4 (2048 bits):

uv run <skill_path>/scripts/rdkit_helper.py fp \
    --file data.csv \
    --smiles-col smiles \
    --type morgan2 \
    --nbits 2048 \
    --output data.morgan2.npy

From SMI, MACCS keys (always 167 bits):

uv run <skill_path>/scripts/rdkit_helper.py fp \
    --file molecules.smi \
    --type maccs \
    --output molecules.maccs.npy

Output as CSV (smiles + bit_0 … bit_N-1 columns):

uv run <skill_path>/scripts/rdkit_helper.py fp \
    --file data.csv \
    --type rdkit \
    --nbits 1024 \
    --format csv \
    --output data.rdkfp.csv

Atom-pair fingerprint, 4096 bits:

uv run <skill_path>/scripts/rdkit_helper.py fp \
    --file data.csv \
    --type atompair \
    --nbits 4096 \
    --output data.atompair.npy

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3) List available descriptors

List all descriptors and built-in presets:

uv run <skill_path>/scripts/rdkit_helper.py list-desc

List descriptors in a specific preset group:

uv run <skill_path>/scripts/rdkit_helper.py list-desc --group lipinski
uv run <skill_path>/scripts/rdkit_helper.py list-desc --group physchem
uv run <skill_path>/scripts/rdkit_helper.py list-desc --group topological
uv run <skill_path>/scripts/rdkit_helper.py list-desc --group all

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Descriptor Presets Reference

| Preset | Count | Typical Use |

| ------------- | ----- | ---------------------------------------------------------------------- |

| lipinski | 6 | Quick drug-likeness screening (Ro5 filter) |

| physchem | 25 | General ML features: MW, logP, TPSA, ring counts, charge stats, … |

| topological | 56 | Graph/topology indices: Balaban J, Kappa, Chi, PEOE_VSA, EState_VSA, … |

| all | ~200 | Full RDKit descriptor set (includes fragment counts, MQN, etc.) |

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Output Format Notes

desc output (CSV):

• Columns: smiles, then one column per descriptor.
• When --file is a .csv and --no-merge is not set, original CSV columns are appended.
• Rows only contain valid SMILES (invalid ones are logged to *.skipped.csv).

fp output:

• .npy (default): NumPy array of shape (N_valid, nbits), dtype uint8 (bit) or int32 (count).
• .csv: smiles column followed by bit_0 … bit_{nbits-1} columns.
• MACCS keys always produce 167 bits regardless of --nbits.

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Agent Checklist

When using this skill for users:

• Confirm input format:
• .csv requires a SMILES column (default smiles)
• .smi uses the first token of each line as SMILES
• Quote SMILES containing special shell characters (brackets/parentheses):
• Example: --smiles "C@@H(F)Cl"
• For CSV workflows, verify column names:
• desc: --smiles-col
• fp: --smiles-col
• Choose the right preset or fingerprint type for the downstream task:
• Drug screening / Ro5: --preset lipinski
• General ML featurization: --preset physchem or --type morgan2
• Structural similarity search: --type morgan2 or --type rdkit
• Substructure matching: --type maccs or --type pattern
• Watch for skipped SMILES:
• Check *.skipped.csv and decide whether to fix or permanently drop them
• Always capture absolute output paths:
• Look for [RESULT] ...=/abs/path in stdout
• If debugging is needed, enable full traceback:
• RDKIT_HELPER_TRACE=1 uv run <skill_path>/scripts/rdkit_helper.py ...

References

• RDKit documentation: https://www.rdkit.org/docs/
• RDKit descriptor list: https://www.rdkit.org/docs/GettingStartedInPython.html#list-of-available-descriptors
• RDKit fingerprint guide: https://www.rdkit.org/docs/GettingStartedInPython.html#fingerprinting-and-molecular-similarity