Scikit-fingerprints: An Advanced Python Library for Efficient Molecular Fingerprint Computation and Integration with Machine Learning Pipelines
Practical Solutions and Value
Scikit-fingerprints is a Python package developed for computing molecular fingerprints in chemoinformatics, providing an interface compatible with scikit-learn for easy integration into machine learning workflows. The library offers over 30 types of molecular fingerprints, supports both 2D and 3D representations, and enables efficient parallel computation for processing large datasets.
Key features include:
- Integration with scikit-learn for easy incorporation into ML pipelines
- Optimized parallel computation for handling large datasets
- Support for over 30 fingerprint types, including 2D and 3D representations
- Extensive testing, security checks, and CI/CD practices for high code quality
Scikit-fingerprints simplifies molecular property prediction, fingerprint hyperparameter tuning, and tasks like virtual screening, showcasing near-ideal parallelism and efficient memory usage. Additionally, it supports users with varying programming expertise and is actively used in research for molecular property prediction and pesticide toxicity studies.
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