Hadoop MapReduce Framework to Implement Molecular Docking of Large-Scale Virtual Screening

Traditional virtual screening in the grid needs chemists to upload small molecule files and collect the results manually, which cannot implement docking and collection of results automatically. This caused heavy workload to chemists. In this paper, we took advantage of Hadoop platform in the massive data storage. We stored and managed small molecule files and docking results files using HDFS. In addition, MapReduce programming framework is used for parallel molecular docking to preliminarily process results files, in order to achieve the automation of the virtual screening molecular docking. The research of this thesis will be helpful to drug researcher by offering a massive data storage management system for large-scale virtual screening, and will also provide a reference for drug discovery in the cloud environment to promote the development of computational chemistry e-science.