unimol

name	unimol
description	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.
compatibility	Requires uv. Dependencies (unimol-tools, rdkit, etc.) are handled automatically via inline script metadata in unimol_helper.py.
license	LGPL-3.0-or-later
metadata	{"author":"luzitian","version":"1.0","repository":"https://github.com/deepmodeling/Uni-Mol"}

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:

1. Confirm input format:
   • .csv requires a SMILES column (default smiles)
   • .smi uses the first token of each line as SMILES
2. Quote SMILES containing special characters (brackets/parentheses):
   • Example: --smiles "[C]([H])([H])[H]"
3. For CSV workflows, verify column names:
   • repr: --smiles-col
   • train: --smiles-col and --target-col / --target-cols
   • predict: --smiles-col
4. Watch for skipped SMILES:
   • Check *.skipped.csv and decide whether to fix or permanently drop them
5. Always capture absolute output paths:
   • Look for [RESULT] ...=/abs/path in stdout
6. 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/