In this work, we present \textit{scikit-fingerprints}, a Python package for computation of molecular fingerprints for applications in chemoinformatics. Our library offers an industry-standard scikit-learn interface, allowing intuitive usage and easy integration with machine learning pipelines. It is also highly optimized, featuring parallel computation that enables efficient processing of large molecular datasets. Currently, \textit{scikit-fingerprints} stands as the most feature-rich library in the Python ecosystem, offering over 30 molecular fingerprints. Our library simplifies chemoinformatics tasks based on molecular fingerprints, including molecular property prediction and virtual screening. It is also flexible, highly efficient, and fully open source.

翻译：本文介绍了 \textit{scikit-fingerprints}，一个用于计算化学信息学中分子指纹的Python软件包。该库提供了业界标准的scikit-learn接口，支持直观使用并易于与机器学习流程集成。同时，它经过高度优化，具备并行计算能力，能够高效处理大规模分子数据集。目前，\textit{scikit-fingerprints} 是Python生态系统中功能最丰富的分子指纹库，提供超过30种分子指纹。本库简化了基于分子指纹的化学信息学任务，包括分子性质预测和虚拟筛选。该工具兼具灵活性、高效性，且完全开源。