Wolf Antje, Shahid Mohammad, Kasam Vinod, Ziegler Wolfgang, Hofmann-Apitius Martin
Department of Bioinformatics, Fraunhofer-Institute for Algorithms and Scientific Computing, Schloss Birlinghoven, 53754 Sankt Augustin, Germany.
Curr Clin Pharmacol. 2010 Feb;5(1):37-46. doi: 10.2174/157488410790410560.
The first step in finding a "drug" is screening chemical compound databases against a protein target. In silico approaches like virtual screening by molecular docking are well established in modern drug discovery. As molecular databases of compounds and target structures are becoming larger and more and more computational screening approaches are available, there is an increased need in compute power and more complex workflows. In this regard, computational Grids are predestined and offer seamless compute and storage capacity. In recent projects related to pharmaceutical research, the high computational and data storage demands of large-scale in silico drug discovery approaches have been addressed by using Grid computing infrastructures, in both; pharmaceutical industry as well as academic research. Grid infrastructures are part of the so-called eScience paradigm, where a digital infrastructure supports collaborative processes by providing relevant resources and tools for data- and compute-intensive applications. Substantial computing resources, large data collections and services for data analysis are shared on the Grid infrastructure and can be mobilized on demand. This review gives an overview on the use of Grid computing for in silico drug discovery and tries to provide a vision of future development of more complex and integrated workflows on Grids, spanning from target identification and target validation via protein-structure and ligand dependent screenings to advanced mining of large scale in silico experiments.
寻找“药物”的第一步是针对蛋白质靶点筛选化合物数据库。在现代药物发现中，诸如通过分子对接进行虚拟筛选等计算机辅助方法已得到广泛应用。随着化合物和靶点结构的分子数据库越来越大，可用的计算筛选方法也越来越多，对计算能力和更复杂工作流程的需求日益增加。在这方面，计算网格是理想之选，可提供无缝的计算和存储能力。在近期与药物研究相关的项目中，制药行业和学术研究都通过使用网格计算基础设施，满足了大规模计算机辅助药物发现方法对高计算和数据存储的需求。网格基础设施是所谓的电子科学范式的一部分，在这种范式中，数字基础设施通过为数据密集型和计算密集型应用提供相关资源和工具来支持协作过程。大量的计算资源、大数据集以及数据分析服务在网格基础设施上共享，并可按需调用。本综述概述了网格计算在计算机辅助药物发现中的应用，并试图展望未来在网格上更复杂、更集成的工作流程的发展，涵盖从靶点识别和靶点验证到基于蛋白质结构和配体的筛选，再到大规模计算机辅助实验的高级挖掘。
