GPURFSCREEN: a GPU based virtual screening tool using random forest classifier

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Abstract

Background: In-silico methods are an integral part of modern drug discovery paradigm. Virtual screening, an in-silico method, is used to refine data models and reduce the chemical space on which wet lab experiments need to be performed. Virtual screening of a ligand data model requires large scale computations, making it a highly time consuming task. This process can be speeded up by implementing parallelized algorithms on a Graphical Processing Unit (GPU). Results: Random Forest is a robust classification algorithm that can be employed in the virtual screening. A ligand based virtual screening tool (GPURFSCREEN) that uses random forests on GPU systems has been proposed and evaluated in this paper. This tool produces optimized results at a lower execution time for large bioassay data sets. The quality of results produced by our tool on GPU is same as that on a regular serial environment. Conclusion: Considering the magnitude of data to be screened, the parallelized virtual screening has a significantly lower running time at high throughput. The proposed parallel tool outperforms its serial counterpart by successfully screening billions of molecules in training and prediction phases.

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Jayaraj, P. B., Ajay, M. K., Nufail, M., Gopakumar, G., & Jaleel, U. C. A. (2016). GPURFSCREEN: a GPU based virtual screening tool using random forest classifier. Journal of Cheminformatics, 8(1). https://doi.org/10.1186/s13321-016-0124-8

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