Train and run Uni-Mol Tools models to predict molecular properties from CSV data. Organize experiments into projects, compare model performance, rank results, and manage storage through interactive commands or machine-readable JSON output.
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Interactive CLI for Uni-Mol molecular property prediction training and inference workflows.
安装前可以查看交给 AI 代理的指令内容。
Package: cli-anything-unimol-tools
Command: python3 -m cli_anything.unimol_tools
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.
--json flag# Create a new project
project create --name drug_discovery
# List all projects
project list
# Switch to a project
project switch --name drug_discovery
# 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
# List all trained models
models list
# Show model details and performance
models show --model-id <id>
# Rank models by performance
models rank
# 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
# Make predictions with a trained model
predict --model-id <id> --data-path test.csv
CSV files must contain:
SMILES column: Molecular structures in SMILES format--target-col)Example:
SMILES,target
CCO,1
CCCO,0
CC(C)O,1
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": "..."
}
Launch without commands for interactive REPL:
python3 -m cli_anything.unimol_tools
Features:
Example datasets available at: https://github.com/545487677/CLI-Anything-unimol-tools/tree/main/unimol_tools/examples
Includes data for all 5 task types.
cd unimol_tools/agent-harness
pip install -e .
cd docs/test
bash run_tests.sh --unit -v # Unit tests (67 tests)
bash run_tests.sh --full -v # Full test suite
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