Unimol

Skill jinzhezenggroup/computational-chemistry-agent-skills/molecular-representation/unimol

A standardized CLI wrapper for Uni-Mol molecular ML workflows that handles representation extraction (embeddings), model training (regression/classification), and property prediction with built-in RDKit SMILES validation. USE WHEN you need to generate molecular embeddings, train machine learning models for chemical properties, or run predictions on SMILES datasets (.csv/.smi) using the Uni-Mol framework.From its SKILL.md

Install

npx -y skills add jinzhezenggroup/computational-chemistry-agent-skills --skill unimol

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What its file declares

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The file declares its own license as LGPL-3.0-or-later. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

SKILL.md

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Uni-Mol

This skill provides practical command patterns for Uni-Mol molecular representation / training / prediction using the standardized CLI wrapper: <skill_path>/scripts/unimol_helper.py.

Key behaviors (important for Agents):

The script prints environment detection (Python/Torch/CUDA) by default.
Bad/illegal SMILES are skipped and logged to *.skipped.csv (no crash).
Each run ends by printing absolute output paths like:
- [RESULT] repr_npy=/abs/path.npy
- [RESULT] model_dir=/abs/model_dir
- [RESULT] pred_csv=/abs/pred.csv

Quick Start

Check CLI help:

uv run python <skill_path>/scripts/unimol_helper.py --help

Check subcommand help:

uv run python <skill_path>/scripts/unimol_helper.py repr --help
uv run python <skill_path>/scripts/unimol_helper.py train --help
uv run python <skill_path>/scripts/unimol_helper.py predict --help

Disable environment printing (optional):

uv run python <skill_path>/scripts/unimol_helper.py --no-env repr --smiles "CCO" --output out.npy

Core Tasks

1) Extract molecular representations (embedding) to .npy

Single SMILES:

uv run python <skill_path>/scripts/unimol_helper.py repr \
    --smiles "CCO" \
    --output /tmp/ccO.repr.npy

From CSV (default SMILES column is smiles):

uv run python <skill_path>/scripts/unimol_helper.py repr \
    --file data.csv \
    --smiles-col smiles \
    --output data.repr.npy

From SMI:

uv run python <skill_path>/scripts/unimol_helper.py repr \
    --file molecules.smi \
    --output molecules.repr.npy

Force CPU / GPU:

# Force CPU
uv run python <skill_path>/scripts/unimol_helper.py repr --smiles "CCO" --no-gpu --output out.npy

# Force GPU (will warn & fall back if CUDA is unavailable)
uv run python <skill_path>/scripts/unimol_helper.py repr --smiles "CCO" --use-gpu --output out.npy

2) Train a property model (classification / regression / multilabel_*)

Regression training (CSV must contain smiles and target columns):

uv run python <skill_path>/scripts/unimol_helper.py train \
    --task regression \
    --input train.csv \
    --smiles-col smiles \
    --target-col target \
    --epochs 50 \
    --output ./model_reg

Classification training:

uv run python <skill_path>/scripts/unimol_helper.py train \
    --task classification \
    --input train.csv \
    --smiles-col smiles \
    --target-col target \
    --epochs 50 \
    --output ./model_cls

Multilabel regression training (explicit multi-target columns):

uv run python <skill_path>/scripts/unimol_helper.py train \
    --task multilabel_regression \
    --input train.csv \
    --smiles-col smiles \
    --target-cols target_0,target_1,target_2 \
    --epochs 50 \
    --output ./model_mreg

Multilabel classification training:

uv run python <skill_path>/scripts/unimol_helper.py train \
    --task multilabel_classification \
    --input train.csv \
    --smiles-col smiles \
    --target-cols y_cls_0,y_cls_1,y_cls_2 \
    --epochs 50 \
    --output ./model_mcls

Target recognition for training:

Single-task (classification / regression): use --target-col (default target).
Multilabel tasks: prefer --target-cols (comma-separated).
If --target-cols is omitted for multilabel tasks, the helper auto-detects columns named target or prefixed with target_ (case-insensitive).

Force CPU:

uv run python <skill_path>/scripts/unimol_helper.py train \
    --task regression \
    --input train.csv \
    --epochs 50 \
    --output ./model_cpu \
    --no-cuda

3) Predict properties to .csv

Predict from CSV:

uv run python <skill_path>/scripts/unimol_helper.py predict \
    --model ./model_reg \
    --input test.csv \
    --smiles-col smiles \
    --output pred.csv

Predict from SMI:

uv run python <skill_path>/scripts/unimol_helper.py predict \
    --model ./model_reg \
    --input test.smi \
    --output pred.csv

Notes:

Output CSV contains the input rows (for valid SMILES) plus pred / pred_* columns.
If there are bad SMILES, they are skipped and saved to pred.csv.skipped.csv (or your --error-log path).

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 characters (brackets/parentheses):
- Example: --smiles "[C]([H])([H])[H]"
For CSV workflows, verify column names:
- repr: --smiles-col
- train: --smiles-col and --target-col / --target-cols
- predict: --smiles-col
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:
- UNIMOL_HELPER_TRACE=1 uv run python <skill_path>/scripts/unimol_helper.py ...

References

Uni-Mol project: https://github.com/fanxiaoyu0/Uni-Mol
RDKit: https://www.rdkit.org/

What ships with it: 1 file

21.8 KB alongside SKILL.md, 1 of them executable

scripts/

unimol_helper.pyruns21.8 KB