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Unimol_tools - AI Research Tools Tool

Overview

Unimol_tools is an easy-to-use AutoML toolkit for molecular property prediction built around the Uni-Mol framework. It provides wrappers that integrate pre-trained Uni-Mol models (hosted on Hugging Face) with PyTorch and RDKit to streamline representation, inference, and downstream tasks.

Key Features

  • Auto-ML pipeline for molecular property prediction
  • Wrappers for molecular representation and downstream tasks
  • Integrates with Uni-Mol pre-trained models on Hugging Face
  • Supports PyTorch for model execution
  • Supports RDKit for chemoinformatics workflows
  • API and example scripts for inference and evaluation

Ideal Use Cases

  • Rapid prototyping of molecular property prediction models
  • Benchmarking Uni-Mol representations with custom datasets
  • Running inference with Hugging Face-hosted pre-trained models
  • Integrating chemoinformatics into ML research workflows
  • Preprocessing molecules for downstream machine-learning tasks

Getting Started

  • Clone the project's GitHub repository
  • Install required dependencies such as PyTorch and RDKit
  • Open the README to review installation and example usage
  • Download or reference Uni-Mol pre-trained models on Hugging Face
  • Run provided example inference or training scripts
  • Adapt wrappers to your dataset and evaluate predictions

Pricing

No pricing information provided in the supplied project context. Check the GitHub repository README and license for usage and commercial terms.

Limitations

  • Relies on external pre-trained models hosted on Hugging Face
  • Requires PyTorch and RDKit to run model and chemoinformatics code
  • Designed primarily for research use rather than production deployment

Key Information

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