cli-anything-unimol-tools

You can automate molecular property prediction workflows by using an interactive command-line interface to train, evaluate, and manage models, which supports JSON output for seamless scripting and full workflow automation.

Cheminformatics model management supports various molecular prediction tasks, specifically handling classification, regression, and multilabel scenarios to accommodate diverse compound screening and analysis requirements.

Yes, you need PyTorch and the Uni-Mol backend installed, as these dependencies are strictly required for executing model operations and managing molecular property prediction tasks through the CLI.

You can track and compare multiple molecular prediction experiments by organizing them into named projects, which allows you to maintain performance history and identify top-performing models based on evaluation metrics.

The best way to organize compound screening models for reproducibility is managing multiple experiments as named projects via CLI, ensuring consistent tracking of performance history and automated model selection.