Interaction fingerprints are vector representations that summarize the three-dimensional nature of interactions in molecular complexes, typically formed between a protein and a ligand. This kind of encoding has found many applications in drug-discovery projects, from structure-based virtual-screening to machine-learning. Here, we present ProLIF, a Python library designed to generate interaction fingerprints for molecular complexes extracted from molecular dynamics trajectories, experimental structures, and docking simulations. It can handle complexes formed of any combination of ligand, protein, DNA, or RNA molecules. The available interaction types can be fully reparametrized or extended by user-defined ones. Several tutorials that cover typical use-case scenarios are available, and the documentation is accompanied with code snippets showcasing the integration with other data-analysis libraries for a more seamless user-experience. The library can be freely installed from our GitHub repository ( https://github.com/chemosim-lab/ProLIF ).

中文翻译：

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ProLIF：将分子相互作用编码为指纹的库

相互作用指纹是矢量表示，总结了分子复合物中相互作用的三维性质，通常在蛋白质和配体之间形成。这种编码在药物发现项目中发现了许多应用，从基于结构的虚拟筛选到机器学习。在这里，我们展示了 ProLIF，这是一个 Python 库，旨在为从分子动力学轨迹、实验结构和对接模拟中提取的分子复合物生成相互作用指纹。它可以处理由配体、蛋白质、DNA 或 RNA 分子的任意组合形成的复合物。可用的交互类型可以完全重新参数化或由用户定义的类型扩展。提供了几个涵盖典型用例场景的教程，文档附有代码片段，展示了与其他数据分析库的集成，以获得更无缝的用户体验。该库可以从我们的 GitHub 存储库 (https://github.com/chemosim-lab/ProLIF) 免费安装。