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

• Published in: Journal of cheminformatics Vol. 8; no. 1; pp. 12 - 10
• Main Authors: Jayaraj, P. B., Ajay, Mathias K., Nufail, M., Gopakumar, G., Jaleel, U. C. A.
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.03.2016; Springer Nature B.V
• Subjects: Algorithms; Bioassays; Chemistry; Chemistry and Materials Science; Computational Biology/Bioinformatics; Computer Applications in Chemistry; Documentation and Information in Chemistry; Software; Theoretical and Computational Chemistry; CUDA; Virtual screening; Ligand based drug discovery; drug discovery; Random forest classifier; GPU computing; In-silico drug discovery
• ISSN: 1758-2946; 1758-2946
• DOI: 10.1186/s13321-016-0124-8

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.