In recent years computational methods for molecular modeling have become a prime focus of computational biology and cheminformatics. Many dedicated systems exist for modeling specific classes of molecules such as proteins or small drug-like ligands. These are often heavily tailored toward the automated generation of molecular structures based on some meta-input by the user and are not intended for expert-driven structure assembly. Dedicated manual or semi-automated assembly software tools exist for a variety of molecule classes but are limited in the scope of structures they can produce. In this work we present BuildAMol, a highly flexible and extendable, general-purpose fragment-based molecular assembly toolkit. Written in Python and featuring a well-documented, user-friendly API, BuildAMol empowers researchers with a framework for detailed manual or semi-automated construction of diverse molecular models. Unlike specialized software, BuildAMol caters to a broad range of applications. We demonstrate its versatility across various use cases, encompassing generating metal complexes or the modeling of dendrimers or integrated into a drug discovery pipeline. By providing a robust foundation for expert-driven model building, BuildAMol holds promise as a valuable tool for the continuous integration and advancement of powerful deep learning techniques. Scientific contribution BuildAMol introduces a cutting-edge framework for molecular modeling that seamlessly blends versatility with user-friendly accessibility. This innovative toolkit integrates modeling, modification, optimization, and visualization functions within a unified API, and facilitates collaboration with other cheminformatics libraries. BuildAMol, with its shallow learning curve, serves as a versatile tool for various molecular applications while also laying the groundwork for the development of specialized software tools, contributing to the progress of molecular research and innovation.

中文翻译：

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BuildAMol：用于基于片段的分子设计的多功能 Python 工具包


近年来，分子建模的计算方法已成为计算生物学和化学信息学的主要焦点。许多专用系统用于对特定类别的分子（例如蛋白质或小型药物样配体）进行建模。这些通常是针对基于用户的某些元输入自动生成分子结构而量身定制的，而不是用于专家驱动的结构组装。针对各种分子类别存在专用的手动或半自动组装软件工具，但它们可以产生的结构范围受到限制。在这项工作中，我们展示了 BuildAMol，一个高度灵活和可扩展的、通用的基于片段的分子组装工具包。 BuildAMol 用 Python 编写，具有文档齐全、用户友好的 API，为研究人员提供了一个详细的手动或半自动构建不同分子模型的框架。与专业软件不同，BuildAMol 能够满足广泛的应用需求。我们展示了它在各种用例中的多功能性，包括生成金属络合物或树状聚合物建模或集成到药物发现管道中。通过为专家驱动的模型构建提供坚实的基础，BuildAMol 有望成为持续集成和改进强大深度学习技术的宝贵工具。科学贡献 BuildAMol 引入了尖端的分子建模框架，将多功能性与用户友好的可访问性无缝地融合在一起。该创新工具包将建模、修改、优化和可视化功能集成在统一的 API 中，并促进与其他化学信息学库的协作。 BuildAMol 的学习曲线较浅，可作为各种分子应用的多功能工具，同时也为专业软件工具的开发奠定了基础，为分子研究和创新的进步做出了贡献。