发表机构
Faculty of Computer Science, AGH University of Krakow（克拉科夫AGH科技大学计算机科学学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究推出基于RDKit的scikit-fingerprints库，填补化学信息学框架与scikit-learn生态的兼容空白，整合多类功能，提升分子机器学习的原型开发、复现与部署效率。
AI 中文摘要
我们推出scikit-fingerprints，这是一个基于RDKit、完全兼容scikit-learn的Python综合库，用于分子机器学习。分子指纹及相关功能是化学信息学的核心工具，但广泛使用的开源框架并不兼容基于scikit-learn规范的更广泛Python机器学习生态系统。scikit-fingerprints填补了这一空白，将分子指纹、分子过滤器、相似性与距离度量、适用域估计、数据拆分策略等功能整合到单一的熟悉接口中。scikit-learn兼容性意味着，从原始SMILES字符串到可部署模型的整个化学信息学工作流，均可通过可组合的构建模块组装而成，且能复用周边生态系统的成熟工具。底层RDKit代码使其对自定义化学信息学用例而言既熟悉又可扩展。我们高度重视统一接口、易用性、计算效率、定制化与可扩展性，scikit-fingerprints可加快分子机器学习的原型开发速度，提升可复现性并简化部署流程。
英文摘要
We present scikit-fingerprints, a comprehensive, fully scikit-learn compatible library for molecular machine learning in Python, based on RDKit. Molecular fingerprints and related functionalities are workhorses of chemoinformatics, yet the widely used open-source frameworks are not compatible with the wider Python machine learning ecosystem based on scikit-learn conventions. scikit-fingerprints closes this gap, bringing molecular fingerprints, molecular filters, similarity and distance measures, applicability domain estimation, data splitting strategies, and more under a single, familiar interface. Scikit-learn compatibility means that an entire chemoinformatics workflow, from a raw SMILES string to a deployable model, can be assembled from composable building blocks and can reuse the mature tooling of the surrounding ecosystem. The underlying RDKit code makes it familiar and extensible for custom chemoinformatics use cases. We put a strong focus on unified interfaces, ease of use, computational efficiency, customization, and extensibility. scikit-fingerprints makes molecular machine learning faster to prototype, easier to reproduce, and simpler to deploy.
