让分子指纹与scikit-learn无缝对接，加速药物研发机器学习流程。
Scikit-fingerprints: Python library for scikit-learn compatible molecular fingerprints and chemoinformatics
- 基于RDKit构建，完全兼容scikit-learn接口，支持分子指纹生成
- 集成相似性度量、数据划分等工具，全流程可组合复用
- 适合药化研究者快速搭建可重复的分子机器学习模型
我们提出scikit-fingerprints，一个全面且完全兼容scikit-learn的Python分子机器学习库，基于RDKit实现。分子指纹及相关功能是化学信息学的核心，但现有开源框架与以scikit-learn为核心的Python机器学习生态不兼容。scikit-fingerprints填补了这一空白，将分子指纹、分子筛选、相似性/距离度量、适用域估计、数据划分策略等功能整合到统一、熟悉的接口中。兼容scikit-learn意味着从原始SMILES字符串到可部署模型的完整工作流，可由模块化组件构成，并复用成熟生态工具。底层使用RDKit，便于熟悉且可扩展至自定义化学信息学场景。本库强调统一接口、易用性、计算效率、可定制性和可扩展性，使分子机器学习更快速原型化、更易复现、更易部署。
原文摘要 · Abstract (English)
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.
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