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10.1186/s13321-016-0124-8 http://scihub22266oqcxt.onion/10.1186/s13321-016-0124-8 C4772510!4772510!26933453     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Cheminform 2016 ; 8 (ä): ä Nephropedia Template TP {{tp|p=26933453|t=2016. GPURFSCREEN: a GPU based virtual screening tool using random forest classifier |pdf=|usr=}}{{26933453}} gab.com Text GPURFSCREEN: a GPU based virtual screening tool using random forest classifier JO: J Cheminform http://www.kidney.de/pget.php?&tw=1&pmid=26933453 #FOAvip 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. Twit Text FOAVip GPURFSCREEN: a GPU based virtual screening tool using random forest classifier ????J Cheminform http://www.kidney.de/pget.php?&tw=1&pmid=26933453 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26933453 #FOAvip English Wikipedia <ref>{{Cite pmid|26933453}}</ref> GPURFSCREEN: a GPU based virtual screening tool using random forest classifier #MMPMID26933453 Jayaraj PB; Ajay MK; Nufail M; Gopakumar G; Jaleel UCA J Cheminform 2016[]; 8 (ä): ä PMID26933453show ga 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. äGPURFSCREEN: a GPU based virtual screening tool using random forest cl(epmc).pdf DeepDyve Pubget Overpricing