A GPU based virtual screening tool using SOM

Abstract

This paper attempts to introduce the applicability of low cost graphics processing unit alternatives to a virtual screening technique using a novel self-organising map (SOM) based technique. This method combines the unsupervised learning capability of the SOM with a subsequent supervised labelling of the trained SOM neurons for building the prediction model. This novel iteration-based SOM technique can label molecule as undefined classes which can reduce the false positives in the screening. For running large datasets, the serial implementation of the proposed algorithm is very time-consuming and cannot be completed in a stipulated time frame. This has been overcome by exploiting the parallelism present in finding the winner neuron and neuron weight updating steps. A tool named SOMSCREEN is developed based on the proposed parallelised method to make the drug discovery process faster. It is observed that, the proposed method offers reduced false positive rate than the Random Forest based work.