Liu Tairan, Naderi Misagh, Alvin Chris, Mukhopadhyay Supratik, Brylinski Michal
Department of Computer Science and Information Systems, Bradley University , Peoria, Illinois 61625, United States.
J Chem Inf Model. 2017 Apr 24;57(4):627-631. doi: 10.1021/acs.jcim.6b00596. Epub 2017 Apr 4.
Constructing high-quality libraries of molecular building blocks is essential for successful fragment-based drug discovery. In this communication, we describe eMolFrag, a new open-source software to decompose organic compounds into nonredundant fragments retaining molecular connectivity information. Given a collection of molecules, eMolFrag generates a set of unique fragments comprising larger moieties, bricks, and smaller linkers connecting bricks. These building blocks can subsequently be used to construct virtual screening libraries for targeted drug discovery. The robustness and computational performance of eMolFrag is assessed against the Directory of Useful Decoys, Enhanced database conducted in serial and parallel modes with up to 16 computing cores. Further, the application of eMolFrag in de novo drug design is illustrated using the adenosine receptor. eMolFrag is implemented in Python, and it is available as stand-alone software and a web server at www.brylinski.org/emolfrag and https://github.com/liutairan/eMolFrag .
构建高质量的分子构建块库对于基于片段的药物发现的成功至关重要。在本通讯中,我们描述了eMolFrag,这是一种新的开源软件,用于将有机化合物分解为保留分子连接性信息的非冗余片段。给定一组分子,eMolFrag会生成一组独特的片段,包括较大的部分、砖块以及连接砖块的较小连接体。这些构建块随后可用于构建用于靶向药物发现的虚拟筛选库。针对有用诱饵目录增强数据库,在串行和并行模式下使用多达16个计算核心评估了eMolFrag的稳健性和计算性能。此外,使用腺苷受体说明了eMolFrag在从头药物设计中的应用。eMolFrag用Python实现,可作为独立软件和网络服务器在www.brylinski.org/emolfrag和https://github.com/liutairan/eMolFrag上获取。