name: "cli-anything-unimol-tools"
description: >-
Interactive CLI for Uni-Mol molecular property prediction training and inference workflows.

Uni-Mol Tools - Molecular Property Prediction CLI#

Package: cli-anything-unimol-tools
Command: python3 -m cli_anything.unimol_tools

Description#

Interactive CLI for training and inference of molecular property prediction models using Uni-Mol Tools. Supports 5 task types: binary classification, regression, multiclass, multilabel classification, and multilabel regression.

Key Features#

Project Management: Organize experiments with named projects
5 Task Types: Classification, regression, multiclass, multilabel variants
Model Tracking: Automatic performance history and rankings
Smart Storage: Analyze usage and clean up underperformers
JSON API: Full automation support with --json flag

Common Commands#

Project Management#

# Create a new project
project create --name drug_discovery

# List all projects
project list

# Switch to a project
project switch --name drug_discovery

Training#

# Train a classification model
train --data-path train.csv --target-col active --task-type classification --epochs 10

# Train a regression model
train --data-path train.csv --target-col affinity --task-type regression --epochs 10

Model Management#

# List all trained models
models list

# Show model details and performance
models show --model-id <id>

# Rank models by performance
models rank

Storage & Cleanup#

# Analyze storage usage
storage analyze

# Automatic cleanup of poor performers
cleanup auto

# Manual cleanup with criteria
cleanup manual --max-models 10 --min-score 0.7

Prediction#

# Make predictions with a trained model
predict --model-id <id> --data-path test.csv

Data Format#

CSV files must contain:

SMILES column: Molecular structures in SMILES format
Target column(s): Values to predict (name specified via --target-col)

Example:

SMILES,target
CCO,1
CCCO,0
CC(C)O,1

Task Types#

classification: Binary classification (0/1)
regression: Continuous value prediction
multiclass: Multiple class classification
multilabel_classification: Multiple binary labels
multilabel_regression: Multiple continuous values

JSON Mode#

Add --json flag to any command for machine-readable output:

python3 -m cli_anything.unimol_tools --json models list

Output format:

{
  "status": "success",
  "data": [...],
  "message": "..."
}

Interactive Mode#

Launch without commands for interactive REPL:

python3 -m cli_anything.unimol_tools

Features:

Tab completion
Command history
Contextual help
Project state persistence

Test Data#

Example datasets available at:
https://github.com/545487677/CLI-Anything-unimol-tools/tree/main/unimol_tools/examples

Includes data for all 5 task types.

Requirements#

Python 3.8+
PyTorch 1.12+
Uni-Mol Tools backend
4GB+ RAM (8GB+ recommended for training)

Installation#

cd unimol_tools/agent-harness
pip install -e .

Documentation#

SOP: UNIMOL_TOOLS.md
Quick Start: docs/guides/02-QUICK-START.md
Full Documentation: docs/README.md

Testing#

cd docs/test
bash run_tests.sh --unit -v    # Unit tests (67 tests)
bash run_tests.sh --full -v    # Full test suite

Performance Tips#

Start with 10 epochs for initial experiments
Use smaller batch sizes if memory is limited
Monitor storage with storage analyze
Use models rank to identify best performers
Clean up regularly with cleanup auto

Troubleshooting#

CUDA errors: Reduce batch size or use CPU mode
CSV not recognized: Verify SMILES column exists
Low accuracy: Try more epochs or adjust learning rate
Storage full: Run cleanup auto to free space

Uni-Mol Tools: https://github.com/dptech-corp/Uni-Mol/tree/main/unimol_tools
Uni-Mol Paper: https://arxiv.org/abs/2209.11126
CLI-Anything: https://github.com/HKUDS/CLI-Anything