scikit-fingerprints
Readme
A Python library for efficient computation of molecular fingerprints
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.
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:
User-Friendly Interface: The library was designed to provide an intuitive interface, making it easy to integrate into machine learning workflows.
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.
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.
Extensibility: Users should be able to customize and extend the library to suit their specific needs.
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.
FAQs
Library for effective molecular fingerprints calculation
We found that scikit-fingerprints demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 4 open source maintainers collaborating on the project.
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