GPURFSCREEN: a GPU based virtual screening tool using random forest classifier
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 co...
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| Autor*in: |
Jayaraj, P. B. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016 |
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| Schlagwörter: |
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| Anmerkung: |
© Jayaraj et al. 2016 |
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| Übergeordnetes Werk: |
Enthalten in: Journal of cheminformatics - London : BioMed Central, 2009, 8(2016), 1 vom: 01. März |
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| Übergeordnetes Werk: |
volume:8 ; year:2016 ; number:1 ; day:01 ; month:03 |
| Links: |
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| DOI / URN: |
10.1186/s13321-016-0124-8 |
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SPR031343554 |
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| 520 | |a 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. | ||
| 650 | 4 | |a drug discovery |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Virtual screening |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Ligand based drug discovery |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Random forest classifier |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a GPU computing |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a CUDA |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Ajay, Mathias K. |4 aut | |
| 700 | 1 | |a Nufail, M. |4 aut | |
| 700 | 1 | |a Gopakumar, G. |4 aut | |
| 700 | 1 | |a Jaleel, U. C. A. |4 aut | |
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10.1186/s13321-016-0124-8 doi (DE-627)SPR031343554 (SPR)s13321-016-0124-8-e DE-627 ger DE-627 rakwb eng Jayaraj, P. B. verfasserin aut GPURFSCREEN: a GPU based virtual screening tool using random forest classifier 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Jayaraj et al. 2016 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. drug discovery (dpeaa)DE-He213 Virtual screening (dpeaa)DE-He213 Ligand based drug discovery (dpeaa)DE-He213 Random forest classifier (dpeaa)DE-He213 GPU computing (dpeaa)DE-He213 CUDA (dpeaa)DE-He213 Ajay, Mathias K. aut Nufail, M. aut Gopakumar, G. aut Jaleel, U. C. A. aut Enthalten in Journal of cheminformatics London : BioMed Central, 2009 8(2016), 1 vom: 01. März (DE-627)594779219 (DE-600)2486539-4 1758-2946 nnns volume:8 year:2016 number:1 day:01 month:03 https://dx.doi.org/10.1186/s13321-016-0124-8 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2016 1 01 03 |
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10.1186/s13321-016-0124-8 doi (DE-627)SPR031343554 (SPR)s13321-016-0124-8-e DE-627 ger DE-627 rakwb eng Jayaraj, P. B. verfasserin aut GPURFSCREEN: a GPU based virtual screening tool using random forest classifier 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Jayaraj et al. 2016 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. drug discovery (dpeaa)DE-He213 Virtual screening (dpeaa)DE-He213 Ligand based drug discovery (dpeaa)DE-He213 Random forest classifier (dpeaa)DE-He213 GPU computing (dpeaa)DE-He213 CUDA (dpeaa)DE-He213 Ajay, Mathias K. aut Nufail, M. aut Gopakumar, G. aut Jaleel, U. C. A. aut Enthalten in Journal of cheminformatics London : BioMed Central, 2009 8(2016), 1 vom: 01. März (DE-627)594779219 (DE-600)2486539-4 1758-2946 nnns volume:8 year:2016 number:1 day:01 month:03 https://dx.doi.org/10.1186/s13321-016-0124-8 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2016 1 01 03 |
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10.1186/s13321-016-0124-8 doi (DE-627)SPR031343554 (SPR)s13321-016-0124-8-e DE-627 ger DE-627 rakwb eng Jayaraj, P. B. verfasserin aut GPURFSCREEN: a GPU based virtual screening tool using random forest classifier 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Jayaraj et al. 2016 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. drug discovery (dpeaa)DE-He213 Virtual screening (dpeaa)DE-He213 Ligand based drug discovery (dpeaa)DE-He213 Random forest classifier (dpeaa)DE-He213 GPU computing (dpeaa)DE-He213 CUDA (dpeaa)DE-He213 Ajay, Mathias K. aut Nufail, M. aut Gopakumar, G. aut Jaleel, U. C. A. aut Enthalten in Journal of cheminformatics London : BioMed Central, 2009 8(2016), 1 vom: 01. März (DE-627)594779219 (DE-600)2486539-4 1758-2946 nnns volume:8 year:2016 number:1 day:01 month:03 https://dx.doi.org/10.1186/s13321-016-0124-8 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2016 1 01 03 |
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10.1186/s13321-016-0124-8 doi (DE-627)SPR031343554 (SPR)s13321-016-0124-8-e DE-627 ger DE-627 rakwb eng Jayaraj, P. B. verfasserin aut GPURFSCREEN: a GPU based virtual screening tool using random forest classifier 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Jayaraj et al. 2016 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. drug discovery (dpeaa)DE-He213 Virtual screening (dpeaa)DE-He213 Ligand based drug discovery (dpeaa)DE-He213 Random forest classifier (dpeaa)DE-He213 GPU computing (dpeaa)DE-He213 CUDA (dpeaa)DE-He213 Ajay, Mathias K. aut Nufail, M. aut Gopakumar, G. aut Jaleel, U. C. A. aut Enthalten in Journal of cheminformatics London : BioMed Central, 2009 8(2016), 1 vom: 01. März (DE-627)594779219 (DE-600)2486539-4 1758-2946 nnns volume:8 year:2016 number:1 day:01 month:03 https://dx.doi.org/10.1186/s13321-016-0124-8 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2016 1 01 03 |
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10.1186/s13321-016-0124-8 doi (DE-627)SPR031343554 (SPR)s13321-016-0124-8-e DE-627 ger DE-627 rakwb eng Jayaraj, P. B. verfasserin aut GPURFSCREEN: a GPU based virtual screening tool using random forest classifier 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Jayaraj et al. 2016 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. drug discovery (dpeaa)DE-He213 Virtual screening (dpeaa)DE-He213 Ligand based drug discovery (dpeaa)DE-He213 Random forest classifier (dpeaa)DE-He213 GPU computing (dpeaa)DE-He213 CUDA (dpeaa)DE-He213 Ajay, Mathias K. aut Nufail, M. aut Gopakumar, G. aut Jaleel, U. C. A. aut Enthalten in Journal of cheminformatics London : BioMed Central, 2009 8(2016), 1 vom: 01. März (DE-627)594779219 (DE-600)2486539-4 1758-2946 nnns volume:8 year:2016 number:1 day:01 month:03 https://dx.doi.org/10.1186/s13321-016-0124-8 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2016 1 01 03 |
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GPURFSCREEN: a GPU based virtual screening tool using random forest classifier drug discovery (dpeaa)DE-He213 Virtual screening (dpeaa)DE-He213 Ligand based drug discovery (dpeaa)DE-He213 Random forest classifier (dpeaa)DE-He213 GPU computing (dpeaa)DE-He213 CUDA (dpeaa)DE-He213 |
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GPURFSCREEN: a GPU based virtual screening tool using random forest classifier |
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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. © Jayaraj et al. 2016 |
| abstractGer |
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. © Jayaraj et al. 2016 |
| abstract_unstemmed |
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. © Jayaraj et al. 2016 |
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7.3998165 |