Virtual-screening workflow tutorials and prospective results from the Teach-Discover-Treat competition 2014 against malaria [version 2; referees: 3 approved]

The first challenge in the 2014 competition launched by the Teach-Discover-Treat (TDT) initiative asked for the development of a tutorial for ligand-based virtual screening, based on data from a primary phenotypic high-throughput screen (HTS) against malaria. The resulting Workflows were applied to...
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Autor*in:	Sereina Riniker [verfasserIn] Gregory A. Landrum [verfasserIn] Floriane Montanari [verfasserIn] Santiago D. Villalba [verfasserIn] Julie Maier [verfasserIn] Johanna M. Jansen [verfasserIn] W. Patrick Walters [verfasserIn] Anang A. Shelat [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	Biomacromolecule-Ligand Interactions Theory & Simulation

Übergeordnetes Werk:	In: F1000Research - F1000 Research Ltd, 2013, 6(2018)
Übergeordnetes Werk:	volume:6 ; year:2018

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.12688/f1000research.11905.2

Katalog-ID:	DOAJ071418016

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spelling	10.12688/f1000research.11905.2 doi (DE-627)DOAJ071418016 (DE-599)DOAJc66b2c8483024b38a6ca2a0e04d919e4 DE-627 ger DE-627 rakwb eng Sereina Riniker verfasserin aut Virtual-screening workflow tutorials and prospective results from the Teach-Discover-Treat competition 2014 against malaria [version 2; referees: 3 approved] 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The first challenge in the 2014 competition launched by the Teach-Discover-Treat (TDT) initiative asked for the development of a tutorial for ligand-based virtual screening, based on data from a primary phenotypic high-throughput screen (HTS) against malaria. The resulting Workflows were applied to select compounds from a commercial database, and a subset of those were purchased and tested experimentally for anti-malaria activity. Here, we present the two most successful Workflows, both using machine-learning approaches, and report the results for the 114 compounds tested in the follow-up screen. Excluding the two known anti-malarials quinidine and amodiaquine and 31 compounds already present in the primary HTS, a high hit rate of 57% was found. Biomacromolecule-Ligand Interactions Theory & Simulation Medicine R Science Q Gregory A. Landrum verfasserin aut Floriane Montanari verfasserin aut Santiago D. Villalba verfasserin aut Julie Maier verfasserin aut Johanna M. Jansen verfasserin aut W. Patrick Walters verfasserin aut Anang A. Shelat verfasserin aut In F1000Research F1000 Research Ltd, 2013 6(2018) (DE-627)735133581 (DE-600)2699932-8 20461402 nnns volume:6 year:2018 https://doi.org/10.12688/f1000research.11905.2 kostenfrei https://doaj.org/article/c66b2c8483024b38a6ca2a0e04d919e4 kostenfrei https://f1000research.com/articles/6-1136/v2 kostenfrei https://doaj.org/toc/2046-1402 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 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_2014 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2018
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allfieldsSound	10.12688/f1000research.11905.2 doi (DE-627)DOAJ071418016 (DE-599)DOAJc66b2c8483024b38a6ca2a0e04d919e4 DE-627 ger DE-627 rakwb eng Sereina Riniker verfasserin aut Virtual-screening workflow tutorials and prospective results from the Teach-Discover-Treat competition 2014 against malaria [version 2; referees: 3 approved] 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The first challenge in the 2014 competition launched by the Teach-Discover-Treat (TDT) initiative asked for the development of a tutorial for ligand-based virtual screening, based on data from a primary phenotypic high-throughput screen (HTS) against malaria. The resulting Workflows were applied to select compounds from a commercial database, and a subset of those were purchased and tested experimentally for anti-malaria activity. Here, we present the two most successful Workflows, both using machine-learning approaches, and report the results for the 114 compounds tested in the follow-up screen. Excluding the two known anti-malarials quinidine and amodiaquine and 31 compounds already present in the primary HTS, a high hit rate of 57% was found. Biomacromolecule-Ligand Interactions Theory & Simulation Medicine R Science Q Gregory A. Landrum verfasserin aut Floriane Montanari verfasserin aut Santiago D. Villalba verfasserin aut Julie Maier verfasserin aut Johanna M. Jansen verfasserin aut W. Patrick Walters verfasserin aut Anang A. Shelat verfasserin aut In F1000Research F1000 Research Ltd, 2013 6(2018) (DE-627)735133581 (DE-600)2699932-8 20461402 nnns volume:6 year:2018 https://doi.org/10.12688/f1000research.11905.2 kostenfrei https://doaj.org/article/c66b2c8483024b38a6ca2a0e04d919e4 kostenfrei https://f1000research.com/articles/6-1136/v2 kostenfrei https://doaj.org/toc/2046-1402 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 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_2014 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2018
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author	Sereina Riniker
spellingShingle	Sereina Riniker misc Biomacromolecule-Ligand Interactions misc Theory & Simulation misc Medicine misc R misc Science misc Q Virtual-screening workflow tutorials and prospective results from the Teach-Discover-Treat competition 2014 against malaria [version 2; referees: 3 approved]
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topic_title	Virtual-screening workflow tutorials and prospective results from the Teach-Discover-Treat competition 2014 against malaria [version 2; referees: 3 approved] Biomacromolecule-Ligand Interactions Theory & Simulation
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title	Virtual-screening workflow tutorials and prospective results from the Teach-Discover-Treat competition 2014 against malaria [version 2; referees: 3 approved]
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title_full	Virtual-screening workflow tutorials and prospective results from the Teach-Discover-Treat competition 2014 against malaria [version 2; referees: 3 approved]
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author_browse	Sereina Riniker Gregory A. Landrum Floriane Montanari Santiago D. Villalba Julie Maier Johanna M. Jansen W. Patrick Walters Anang A. Shelat
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title_sort	virtual-screening workflow tutorials and prospective results from the teach-discover-treat competition 2014 against malaria [version 2; referees: 3 approved]
title_auth	Virtual-screening workflow tutorials and prospective results from the Teach-Discover-Treat competition 2014 against malaria [version 2; referees: 3 approved]
abstract	The first challenge in the 2014 competition launched by the Teach-Discover-Treat (TDT) initiative asked for the development of a tutorial for ligand-based virtual screening, based on data from a primary phenotypic high-throughput screen (HTS) against malaria. The resulting Workflows were applied to select compounds from a commercial database, and a subset of those were purchased and tested experimentally for anti-malaria activity. Here, we present the two most successful Workflows, both using machine-learning approaches, and report the results for the 114 compounds tested in the follow-up screen. Excluding the two known anti-malarials quinidine and amodiaquine and 31 compounds already present in the primary HTS, a high hit rate of 57% was found.
abstractGer	The first challenge in the 2014 competition launched by the Teach-Discover-Treat (TDT) initiative asked for the development of a tutorial for ligand-based virtual screening, based on data from a primary phenotypic high-throughput screen (HTS) against malaria. The resulting Workflows were applied to select compounds from a commercial database, and a subset of those were purchased and tested experimentally for anti-malaria activity. Here, we present the two most successful Workflows, both using machine-learning approaches, and report the results for the 114 compounds tested in the follow-up screen. Excluding the two known anti-malarials quinidine and amodiaquine and 31 compounds already present in the primary HTS, a high hit rate of 57% was found.
abstract_unstemmed	The first challenge in the 2014 competition launched by the Teach-Discover-Treat (TDT) initiative asked for the development of a tutorial for ligand-based virtual screening, based on data from a primary phenotypic high-throughput screen (HTS) against malaria. The resulting Workflows were applied to select compounds from a commercial database, and a subset of those were purchased and tested experimentally for anti-malaria activity. Here, we present the two most successful Workflows, both using machine-learning approaches, and report the results for the 114 compounds tested in the follow-up screen. Excluding the two known anti-malarials quinidine and amodiaquine and 31 compounds already present in the primary HTS, a high hit rate of 57% was found.
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title_short	Virtual-screening workflow tutorials and prospective results from the Teach-Discover-Treat competition 2014 against malaria [version 2; referees: 3 approved]
url	https://doi.org/10.12688/f1000research.11905.2 https://doaj.org/article/c66b2c8483024b38a6ca2a0e04d919e4 https://f1000research.com/articles/6-1136/v2 https://doaj.org/toc/2046-1402
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Virtual-screening workflow tutorials and prospective results from the Teach-Discover-Treat competition 2014 against malaria [version 2; referees: 3 approved]

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