Unimol_tools - AI Research Tools Tool

Overview

Unimol_tools is an open-source, research-oriented toolkit that streamlines molecule property prediction using the Uni-Mol framework. It provides high-level wrappers for loading and running Uni-Mol pre-trained models (hosted on Hugging Face), preparing molecular inputs with RDKit, and running downstream evaluation and fine-tuning workflows with PyTorch. The project is designed for researchers who want a compact, reproducible bridge between chemoinformatics preprocessing and transformer-style 3D molecular representation learning. According to the GitHub repository, Unimol_tools focuses on easy-to-use automation: model loading, batched inference, and common downstream tasks (property regression/classification). The codebase is permissively licensed under MIT and is intended for research and prototyping workflows where users want to leverage Uni-Mol pre-trained weights, integrate RDKit featurization, and run experiments or batch predictions without building the entire pipeline from scratch.

GitHub Statistics

• Stars: 26
• Forks: 6
• Contributors: 15
• License: MIT
• Primary Language: Python
• Last Updated: 2025-10-30T03:21:24Z
• Latest Release: v0.1.5

Repository activity indicates an active research project with recent development: 26 stars, 6 forks, and 15 contributors, and the last commit recorded on 2025-10-30 (per the GitHub repository). The MIT license encourages reuse and contribution. Contributor count and recent commits suggest steady maintenance and collaborative development, although the star count is modest, indicating a focused user base primarily from the research community rather than a large mainstream user community.

Installation

Install via pip:

pip install git+https://github.com/deepmodeling/unimol_tools.git

git clone https://github.com/deepmodeling/unimol_tools.git && cd unimol_tools && pip install -e .

Key Features

• Wrappers to load Uni-Mol pre-trained models hosted on Hugging Face for property prediction.
• PyTorch-based inference and fine-tuning utilities for regression and classification tasks.
• RDKit integration for molecular parsing, sanitization, and featurization pipelines.
• Auto-ML style evaluation scaffolding for model selection and metric-driven comparison.
• Batch prediction helpers and dataset loaders tailored to common benchmarking tasks.

Community

Community engagement is centered on the GitHub repository (26 stars, 6 forks, 15 contributors). The MIT license supports reuse; activity and contributor count show research-focused collaboration rather than broad commercial adoption.

Key Information

• Category: Research Tools
• Type: AI Research Tools Tool