deepmodeling/Uni-Mol

Health v2 · maintenance only

Adoption not part of the score

1155 stars · 181 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Uni-Mol is a collection of 3D molecular representation learning frameworks and pretrained models for tasks like molecule property prediction, quantum chemical modeling, and protein-ligand docking. It includes easy-to-use Python wrappers (unimol-tools) and scalable pretrained models up to 1.1 billion parameters.

Use cases

• predict molecular properties from SMILES
• predict protein-ligand binding poses
• generate and optimize molecular conformations
• predict quantum chemical properties of molecules
• fine-tune a pretrained molecular model on my dataset
• embed molecules as 3D representations for ML

When to choose

• you need state-of-the-art 3D molecular property prediction or docking
• you want pretrained molecular models with fine-tuning support
• you work in drug discovery or computational chemistry with Python

When to avoid

• you need general-purpose NLP or vision models
• you lack GPU resources for large pretrained models
• you need a GUI-only workflow without Python

Facets

library · maturity active

machine-learning deep-learning llm-training deep-learning machine-learning bioinformatics chemistry python molecular-modeling pretrained-model molecular-representation-learning drug-discovery property-prediction protein-ligand-docking transformers linux docker

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markdown · JSON · MCP: product_card(name="deepmodeling/Uni-Mol")