Kleinschmidt Noah, Lemmin Thomas
Institute of Biochemistry and Molecular Medicine, University of Bern, Buehlstrasse 28, 3012, Bern, Switzerland.
J Cheminform. 2024 Aug 25;16(1):104. doi: 10.1186/s13321-024-00900-6.
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 contributionBuildAMol 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.
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。存在许多用于对特定类别的分子(如蛋白质或类药物小分子配体)进行建模的专用系统。这些系统通常是根据用户的一些元输入,针对分子结构的自动生成进行了大量定制,并非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,但它们能够生成的结构范围有限。在这项工作中,我们展示了BuildAMol,这是一个高度灵活且可扩展的、基于片段的通用分子组装工具包。BuildAMol用Python编写,具有文档完善、用户友好的应用程序编程接口(API),为研究人员提供了一个用于详细手动或半自动构建各种分子模型的框架。与专用软件不同,BuildAMol适用于广泛的应用。我们展示了它在各种用例中的通用性,包括生成金属配合物或树枝状大分子建模,或集成到药物发现流程中。通过为专家驱动的模型构建提供强大基础,BuildAMol有望成为强大的深度学习技术持续集成和发展的宝贵工具。
科学贡献
BuildAMol引入了一个前沿的分子建模框架,该框架将通用性与用户友好的可访问性无缝融合。这个创新的工具包在统一的API中集成了建模、修改、优化和可视化功能,并促进与其他化学信息学库的协作。BuildAMol学习曲线较浅,是各种分子应用的通用工具,同时也为专用软件工具的开发奠定了基础,有助于分子研究和创新的进展。