A Python library for efficient computation of molecular fingerprints
Click HERE to see the Documentation.
- Description
- General Project Vision
- Library Description
- Installation
- Usage
- Technologies Used
- Contributing
- License
Molecular fingerprints are crucial in various scientific fields, including drug discovery, materials science, and chemical analysis. However, existing Python libraries for computing molecular fingerprints often lack performance, user-friendliness, and support for modern programming standards. This project aims to address these shortcomings by creating an efficient and accessible Python library for molecular fingerprint computation.
python3.9 |
python3.10 |
python3.11 |
python3.12 |
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Ubuntu - latest | ✅ | ✅ | ✅ | ✅ |
Windows - latest | ✅ | ✅ | ✅ | ✅ |
macOS - latest | only macOS 13 | ✅ | ✅ | ✅ |
You can install the library using pip:
pip install scikit-fingerprints
The primary goal of this project was to develop a Python library that simplifies the computation of widely-used molecular fingerprints, such as Morgan's fingerprint, MACCS fingerprint, and others. This library has the following key features:
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User-Friendly Interface: The library was designed to provide an intuitive interface, making it easy to integrate into machine learning workflows.
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Performance Optimization: We implemented molecular fingerprint computation algorithms using concurrent programming techniques to maximize performance. Large datasets of molecules are processed in parallel for improved efficiency.
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Compatibility: The library's interface was inspired by popular data science libraries like Scikit-Learn, ensuring compatibility and familiarity for users familiar with these tools.
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Extensibility: Users should be able to customize and extend the library to suit their specific needs.
- The library offers various functions that accept molecule descriptors (e.g., SMILES) and fingerprint parameters, returning the specified fingerprints.
- It's open-source and available for installation via pip.
- The library has been designed for ease of use, minimizing the need for extensive training.
- Compatibility with the standard Python ML stack, based on Scikit-Learn interfaces, has been a top priority.
Please read CONTRIBUTING.md and CODE_OF_CONDUCT.md for details on our code of conduct, and the process for submitting pull requests to us.
This project is licensed under the MIT License - see the LICENSE.md file for details.