On November 13, at 9:00, there will be another entry in the HPCViz seminar series, once again held in the visualization studio (room 4450, fourth floor of the D-building). Laeeq Ahmed will talk about Parallel Virtual Screening using MapReduce. It is a one hour lecture.
Drug discovery is the process of screening a large number of chemical libraries to find new medicines. Due to the huge size of chemical libraries, traditional screening is time-consuming and costly. With advancements in computer technology, Virtual screening is performed using machine learning techniques for filtering large collection of chemical structures. Support-vector-machine (SVM) is one of the most famous machine learning techniques for classification and regression analysis. In this work we developed a parallel version of SVM based virtual screening using iterative MapReduce programming model Spark, to further reduce the filtering time and thus the cost. I will first introduce Spark and its usage in the cluster environment and later discuss the case study of parallel SVM based virtual screening.