The construction, management, and analysis of large in silico molecular libraries is critical in many areas of modern chemistry. Herein, we introduce the MOLecular LIibrary toolkit, “molli”, which is a Python 3 cheminformatics module that provides a streamlined interface for manipulating large in silico libraries. Three-dimensional, combinatorial molecule libraries can be expanded directly from two-dimensional chemical structure fragments stored in CDXML files with high stereochemical fidelity. Geometry optimization, property calculation, and conformer generation are executed by interfacing with widely used computational chemistry programs such as OpenBabel, RDKit, ORCA, NWChem, and xTB/CREST. Conformer-dependent grid-based feature calculators provide numerical representation and interface to robust three-dimensional visualization tools that provide comprehensive images to enhance human understanding of libraries with thousands of members. The package includes a command-line interface in addition to Python classes to streamline frequently used workflows. Parallel performance is benchmarked on various hardware platforms, and common workflows are demonstrated for different tasks ranging from optimized grid-based descriptor calculation on catalyst libraries to an NMR chemical shift prediction workflow from CDXML files.

中文翻译：

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molli：用于组合小分子库生成、操作和特征提取的通用 Python 工具包


大型计算机分子文库的构建、管理和分析在现代化学的许多领域都至关重要。在此，我们介绍了 MOLecular LIibrary 工具包“molli”，这是一个 Python 3 化学信息学模块，为操作大型计算机库提供了一个简化的界面。三维组合分子库可以直接从存储在 CDXML 文件中的二维化学结构片段扩展，具有高立体化学保真度。通过与广泛使用的计算化学程序（如 OpenBabel、RDKit、ORCA、NWChem 和 xTB/CREST）连接来执行几何优化、属性计算和构象异构体生成。基于构象异构体的基于网格的特征计算器为强大的三维可视化工具提供数字表示和接口，这些工具提供全面的图像，以增强人类对拥有数千个成员的文库的理解。除了 Python 类之外，该软件包还包括一个命令行界面，以简化常用的工作流程。在各种硬件平台上对并行性能进行了基准测试，并演示了不同任务的常见工作流程，从催化剂库上基于网格的优化描述符计算到来自 CDXML 文件的 NMR 化学位移预测工作流程。