Metadata-Version: 2.1
Name: scikit-fingerprints
Version: 1.1.2
Summary: Library for effective molecular fingerprints calculation
Home-page: https://github.com/Arch4ngel21/scikit-fingerprints
License: MIT
Author: Scikit-Fingerprints Development Team
Author-email: scikitfingerprints@gmail.com
Requires-Python: >=3.9,<4.0
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Requires-Dist: datasketch
Requires-Dist: e3fp
Requires-Dist: joblib
Requires-Dist: mhfp
Requires-Dist: numpy (>=1.20.0,<2.0.0)
Requires-Dist: pandas
Requires-Dist: rdkit
Requires-Dist: scikit-learn
Requires-Dist: scipy
Requires-Dist: tqdm
Project-URL: Repository, https://github.com/Arch4ngel21/scikit-fingerprints
Description-Content-Type: text/markdown

# scikit-fingerprints

A Python library for efficient computation of molecular fingerprints


## Table of Contents
- [Description](#description)
- [General Project Vision](#general-project-vision)
- [Library Description](#library-description)
- [Installation](#installation)
- [Usage](#usage)
- [Technologies Used](#technologies-used)
- [Contributing](#contributing)
- [License](#license)

---

## Description

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.

## Installation

You can install the library using pip:

```bash
pip install scikit-fingerprints
```

## General Project Vision
 
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.

## Library Description

- 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.

## Contributing

Please read [CONTRIBUTING.md](CONTRIBUTING.md) and [CODE_OF_CONDUCT.md](CODE_OF_CONDUCT.md) for details on our code of conduct, and the process for submitting pull requests to us.

## License

This project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details.


