istar: A Web Platform for Large-Scale Protein-Ligand Docking
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{"title"=>"Istar: A web platform for large-scale protein-ligand docking", "type"=>"journal", "authors"=>[{"first_name"=>"Hongjian", "last_name"=>"Li", "scopus_author_id"=>"48261247800"}, {"first_name"=>"Kwong Sak", "last_name"=>"Leung", "scopus_author_id"=>"55650989500"}, {"first_name"=>"Pedro J.", "last_name"=>"Ballester", "scopus_author_id"=>"8880287000"}, {"first_name"=>"Man Hon", "last_name"=>"Wong", "scopus_author_id"=>"23478640000"}], "year"=>2014, "source"=>"PLoS ONE", "identifiers"=>{"scopus"=>"2-s2.0-84894639153", "sgr"=>"84894639153", "issn"=>"19326203", "isbn"=>"1932-6203", "pmid"=>"24475049", "doi"=>"10.1371/journal.pone.0085678", "pui"=>"373030976"}, "id"=>"c6a54c4c-cba8-3e24-810a-91ecaa738f58", "abstract"=>"Protein-ligand docking is a key computational method in the design of starting points for the drug discovery process. We are motivated by the desire to automate large-scale docking using our popular docking engine idock and thus have developed a publicly-accessible web platform called istar. Without tedious software installation, users can submit jobs using our website. Our istar website supports 1) filtering ligands by desired molecular properties and previewing the number of ligands to dock, 2) monitoring job progress in real time, and 3) visualizing ligand conformations and outputting free energy and ligand efficiency predicted by idock, binding affinity predicted by RF-Score, putative hydrogen bonds, and supplier information for easy purchase, three useful features commonly lacked on other online docking platforms like DOCK Blaster or iScreen. We have collected 17,224,424 ligands from the All Clean subset of the ZINC database, and revamped our docking engine idock to version 2.0, further improving docking speed and accuracy, and integrating RF-Score as an alternative rescoring function. To compare idock 2.0 with the state-of-the-art AutoDock Vina 1.1.2, we have carried out a rescoring benchmark and a redocking benchmark on the 2,897 and 343 protein-ligand complexes of PDBbind v2012 refined set and CSAR NRC HiQ Set 24Sept2010 respectively, and an execution time benchmark on 12 diverse proteins and 3,000 ligands of different molecular weight. Results show that, under various scenarios, idock achieves comparable success rates while outperforming AutoDock Vina in terms of docking speed by at least 8.69 times and at most 37.51 times. When evaluated on the PDBbind v2012 core set, our istar platform combining with RF-Score manages to reproduce Pearson's correlation coefficient and Spearman's correlation coefficient of as high as 0.855 and 0.859 respectively between the experimental binding affinity and the predicted binding affinity of the docked conformation. istar is freely available at http://istar.cse.cuhk.edu.hk/idock.", "link"=>"http://www.mendeley.com/research/istar-web-platform-largescale-proteinligand-docking", "reader_count"=>73, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>3, "Researcher"=>15, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>27, "Student > Postgraduate"=>4, "Other"=>2, "Student > Master"=>9, "Student > Bachelor"=>4, "Lecturer > Senior Lecturer"=>1, "Professor"=>3}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Professor > Associate Professor"=>3, "Researcher"=>15, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>27, "Student > Postgraduate"=>4, "Other"=>2, "Student > Master"=>9, "Student > Bachelor"=>4, "Lecturer > Senior Lecturer"=>1, "Professor"=>3}, "reader_count_by_subject_area"=>{"Unspecified"=>7, "Environmental Science"=>1, "Biochemistry, Genetics and Molecular Biology"=>12, "Mathematics"=>1, "Agricultural and Biological Sciences"=>24, "Medicine and Dentistry"=>4, "Neuroscience"=>1, "Pharmacology, Toxicology and Pharmaceutical Science"=>1, "Physics and Astronomy"=>3, "Chemistry"=>11, "Computer Science"=>7, "Economics, Econometrics and Finance"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>4}, "Neuroscience"=>{"Neuroscience"=>1}, "Chemistry"=>{"Chemistry"=>11}, "Physics and Astronomy"=>{"Physics and Astronomy"=>3}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>24}, "Computer Science"=>{"Computer Science"=>7}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>12}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>7}, "Environmental Science"=>{"Environmental Science"=>1}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>1}}, "reader_count_by_country"=>{"Argentina"=>1, "Japan"=>2, "Malaysia"=>1, "Germany"=>1, "India"=>2}, "group_count"=>3}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1360593"], "description"=>"<p>Pearson's correlation coefficient , Spearman's correlation coefficient and standard deviation of the difference between predicted and experimental binding affinity on PDBbind v2007 core set ( = 195). The scoring functions are sorted in the descending order of . RF-Score, AutoDock Vina and idock rank 1st, 7th and 8th respectively in terms of Pearson's correlation coefficient . RF-Score, ID-Score, SVR-Score and X-Score are the only scoring functions whose training set do not overlap with the PDBbind v2007 core set. The statistics for AutoDock Vina and idock are reported in this study and the statistics for the other 19 scoring functions are collected from <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0085678#pone.0085678-Ballester1\" target=\"_blank\">[31]</a>, <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0085678#pone.0085678-Li2\" target=\"_blank\">[45]</a>, <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0085678#pone.0085678-Ballester4\" target=\"_blank\">[46]</a>, <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0085678#pone.0085678-Cheng1\" target=\"_blank\">[48]</a>.</p>", "links"=>[], "tags"=>["Biochemistry", "Biochemistry simulations", "Biomacromolecule-ligand interactions", "biophysics", "Biophysics simulations", "Computational biology", "Macromolecular structure analysis", "Macromolecular complex analysis", "protein structure", "Biochemical simulations", "Biophysic al simulations", "Computer applications", "Web-based applications", "Computing methods", "cloud computing", "software engineering", "Software tools", "21", "scoring", "functions", "pdbbind", "v2007"], "article_id"=>913248, "categories"=>["Biological Sciences", "Engineering"], "users"=>["Hongjian Li", "Kwong-Sak Leung", "Pedro J. Ballester", "Man-Hon Wong"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0085678.t001", "stats"=>{"downloads"=>3, "page_views"=>40, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_21_scoring_functions_on_PDBbind_v2007_core_set__195_/913248", "title"=>"Comparison of 21 scoring functions on PDBbind v2007 core set ( = 195).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-01-24 03:29:08"}
  • {"files"=>["https://ndownloader.figshare.com/files/1360591"], "description"=>"<p>The RF-Score was re-trained on PDBbind v2012 refined set ( = 2,897) for prospective prediction purpose. Values are in or unit.  = 0.815,  = 0.817,  = 0.75,  = 0.76.</p>", "links"=>[], "tags"=>["Biochemistry", "Biochemistry simulations", "Biomacromolecule-ligand interactions", "biophysics", "Biophysics simulations", "Computational biology", "Macromolecular structure analysis", "Macromolecular complex analysis", "protein structure", "Biochemical simulations", "Biophysic al simulations", "Computer applications", "Web-based applications", "Computing methods", "cloud computing", "software engineering", "Software tools", "highest", "rf-score", "docked", "conformations", "idock", "binding", "affinity", "pdbbind", "v2012", "redocking"], "article_id"=>913246, "categories"=>["Biological Sciences", "Engineering"], "users"=>["Hongjian Li", "Kwong-Sak Leung", "Pedro J. Ballester", "Man-Hon Wong"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0085678.g006", "stats"=>{"downloads"=>2, "page_views"=>42, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Scatter_plot_of_the_highest_RF_Score_of_the_9_docked_conformations_output_by_idock_against_the_experimental_binding_affinity_on_PDBbind_v2012_core_set__201_in_the_redocking_benchmark_/913246", "title"=>"Scatter plot of the highest RF-Score of the 9 docked conformations output by idock against the experimental binding affinity on PDBbind v2012 core set ( = 201) in the redocking benchmark.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-01-24 03:29:08"}
  • {"files"=>["https://ndownloader.figshare.com/files/1360588"], "description"=>"<p>Very often the of 2.0Å is regarded as the positive control for correct bound structure prediction. Out the 201 cases, there are 109 and 114 successful cases for idock and AutoDock Vina respectively. The average number of rotatable bonds of the ligand in successful cases are 7.52 and 7.30 respectively for idock and AutoDock Vina. The average number of rotatable bonds of the ligand in unsuccessful cases are 10.36 and 10.82 respectively for idock and AutoDock Vina. Docking a ligand with no greater than 10 rotatable bonds has a higher chance to succeed.</p>", "links"=>[], "tags"=>["Biochemistry", "Biochemistry simulations", "Biomacromolecule-ligand interactions", "biophysics", "Biophysics simulations", "Computational biology", "Macromolecular structure analysis", "Macromolecular complex analysis", "protein structure", "Biochemical simulations", "Biophysic al simulations", "Computer applications", "Web-based applications", "Computing methods", "cloud computing", "software engineering", "Software tools", "rotatable", "bonds", "ligand", "rates", "idock", "autodock", "vina", "benchmarked", "pdbbind", "v2012"], "article_id"=>913243, "categories"=>["Biological Sciences", "Engineering"], "users"=>["Hongjian Li", "Kwong-Sak Leung", "Pedro J. Ballester", "Man-Hon Wong"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0085678.g003", "stats"=>{"downloads"=>3, "page_views"=>37, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Impact_of_number_of_rotatable_bonds_of_the_ligand_on_the_success_rates_of_idock_and_AutoDock_Vina_benchmarked_on_PDBbind_v2012_core_set__201_/913243", "title"=>"Impact of number of rotatable bonds of the ligand on the success rates of idock and AutoDock Vina benchmarked on PDBbind v2012 core set ( = 201).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-01-24 03:29:08"}
  • {"files"=>["https://ndownloader.figshare.com/files/1360587"], "description"=>"<p>Values are in or unit. The three scoring functions are all trained on the PDBbind v2007 refined set ( = 1,300).  = 0.801,  = 0.795,  = 1.34,  = 1.34 for RF-Score,  = 0.595,  = 0.612,  = 1.79,  = 1.79 for Vina, and  = 0.597,  = 0.613,  = 1.79,  = 1.79 for idock.</p>", "links"=>[], "tags"=>["Biochemistry", "Biochemistry simulations", "Biomacromolecule-ligand interactions", "biophysics", "Biophysics simulations", "Computational biology", "Macromolecular structure analysis", "Macromolecular complex analysis", "protein structure", "Biochemical simulations", "Biophysic al simulations", "Computer applications", "Web-based applications", "Computing methods", "cloud computing", "software engineering", "Software tools", "correlations", "binding", "affinity", "autodock", "vina", "idock", "csar", "nrc", "hiq", "24sept2010"], "article_id"=>913242, "categories"=>["Biological Sciences", "Engineering"], "users"=>["Hongjian Li", "Kwong-Sak Leung", "Pedro J. Ballester", "Man-Hon Wong"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0085678.g002", "stats"=>{"downloads"=>2, "page_views"=>24, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Pairwise_correlations_of_experimental_binding_affinity_and_predicted_binding_affinity_by_RF_Score_AutoDock_Vina_and_idock_on_the_CSAR_NRC_HiQ_Set_24Sept2010__343_/913242", "title"=>"Pairwise correlations of experimental binding affinity and predicted binding affinity by RF-Score, AutoDock Vina and idock on the CSAR NRC HiQ Set 24Sept2010 ( = 343).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-01-24 03:29:08"}
  • {"files"=>["https://ndownloader.figshare.com/files/1360595"], "description"=>"<p>CPU time and elapsed time in hours of docking 3,000 clean ligands of 3 molecular weight sets against 12 diverse receptors by AutoDock Vina and idock. idock outperforms AutoDock Vina by at least 8.69 times and at most 37.51 times.</p>", "links"=>[], "tags"=>["Biochemistry", "Biochemistry simulations", "Biomacromolecule-ligand interactions", "biophysics", "Biophysics simulations", "Computational biology", "Macromolecular structure analysis", "Macromolecular complex analysis", "protein structure", "Biochemical simulations", "Biophysic al simulations", "Computer applications", "Web-based applications", "Computing methods", "cloud computing", "software engineering", "Software tools"], "article_id"=>913250, "categories"=>["Biological Sciences", "Engineering"], "users"=>["Hongjian Li", "Kwong-Sak Leung", "Pedro J. Ballester", "Man-Hon Wong"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0085678.t003", "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Docking_execution_time_/913250", "title"=>"Docking execution time.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-01-24 03:29:08"}
  • {"files"=>["https://ndownloader.figshare.com/files/1360590"], "description"=>"<p>Values are in or unit.  = 0.502,  = 0.530,  = 1.31,  = 1.32.</p>", "links"=>[], "tags"=>["Biochemistry", "Biochemistry simulations", "Biomacromolecule-ligand interactions", "biophysics", "Biophysics simulations", "Computational biology", "Macromolecular structure analysis", "Macromolecular complex analysis", "protein structure", "Biochemical simulations", "Biophysic al simulations", "Computer applications", "Web-based applications", "Computing methods", "cloud computing", "software engineering", "Software tools", "lowest", "idock", "docked", "conformations", "binding", "affinity", "pdbbind", "v2012", "redocking"], "article_id"=>913245, "categories"=>["Biological Sciences", "Engineering"], "users"=>["Hongjian Li", "Kwong-Sak Leung", "Pedro J. Ballester", "Man-Hon Wong"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0085678.g005", "stats"=>{"downloads"=>1, "page_views"=>17, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Scatter_plot_of_the_lowest_idock_score_of_the_9_docked_conformations_output_by_idock_against_the_experimental_binding_affinity_on_PDBbind_v2012_core_set__201_in_the_redocking_benchmark_/913245", "title"=>"Scatter plot of the lowest idock score of the 9 docked conformations output by idock against the experimental binding affinity on PDBbind v2012 core set ( = 201) in the redocking benchmark.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-01-24 03:29:08"}
  • {"files"=>["https://ndownloader.figshare.com/files/1360589"], "description"=>"<p>Out the 201 cases, there are 158, 31 and 12 cases in which there are 0, 1 and 2 metal ions respectively in the binding site. Very often the of 2.0Å is regarded as the positive control for correct bound structure prediction. For idock, the success rates are 0.58, 0.39 and 0.42 when there are 0, 1 and 2 metal ions respectively in the binding site. For AutoDock Vina, they are 0.60, 0.42 and 0.50 respectively. Docking a ligand with no metal ions in the binding site has a higher chance to succeed.</p>", "links"=>[], "tags"=>["Biochemistry", "Biochemistry simulations", "Biomacromolecule-ligand interactions", "biophysics", "Biophysics simulations", "Computational biology", "Macromolecular structure analysis", "Macromolecular complex analysis", "protein structure", "Biochemical simulations", "Biophysic al simulations", "Computer applications", "Web-based applications", "Computing methods", "cloud computing", "software engineering", "Software tools", "ions", "binding", "rates", "idock", "autodock", "vina", "benchmarked", "pdbbind", "v2012"], "article_id"=>913244, "categories"=>["Biological Sciences", "Engineering"], "users"=>["Hongjian Li", "Kwong-Sak Leung", "Pedro J. Ballester", "Man-Hon Wong"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0085678.g004", "stats"=>{"downloads"=>11, "page_views"=>63, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Impact_of_number_of_metal_ions_in_the_binding_site_on_the_success_rates_of_idock_and_AutoDock_Vina_benchmarked_on_PDBbind_v2012_core_set__201_/913244", "title"=>"Impact of number of metal ions in the binding site on the success rates of idock and AutoDock Vina benchmarked on PDBbind v2012 core set ( = 201).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-01-24 03:29:08"}
  • {"files"=>["https://ndownloader.figshare.com/files/1360586"], "description"=>"<p>Values are in or unit. The three scoring functions are all trained on the PDBbind v2007 refined set ( = 1,300).  = 0.765,  = 0.755,  = 1.26,  = .26 for RF-Score,  = 0.466,  = 0.464,  = 1.74,  = 1.74 for Vina, and  = 0.451,  = 0.453,  = 1.75,  = .75 for idock.</p>", "links"=>[], "tags"=>["Biochemistry", "Biochemistry simulations", "Biomacromolecule-ligand interactions", "biophysics", "Biophysics simulations", "Computational biology", "Macromolecular structure analysis", "Macromolecular complex analysis", "protein structure", "Biochemical simulations", "Biophysic al simulations", "Computer applications", "Web-based applications", "Computing methods", "cloud computing", "software engineering", "Software tools", "correlations", "binding", "affinity", "autodock", "vina", "idock", "pdbbind", "v2012", "refined"], "article_id"=>913241, "categories"=>["Biological Sciences", "Engineering"], "users"=>["Hongjian Li", "Kwong-Sak Leung", "Pedro J. Ballester", "Man-Hon Wong"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0085678.g001", "stats"=>{"downloads"=>2, "page_views"=>16, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Pairwise_correlations_of_experimental_binding_affinity_and_predicted_binding_affinity_by_RF_Score_AutoDock_Vina_and_idock_on_the_PDBbind_v2012_refined_set__2_897_/913241", "title"=>"Pairwise correlations of experimental binding affinity and predicted binding affinity by RF-Score, AutoDock Vina and idock on the PDBbind v2012 refined set ( = 2,897).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-01-24 03:29:08"}
  • {"files"=>["https://ndownloader.figshare.com/files/1360617", "https://ndownloader.figshare.com/files/1360618", "https://ndownloader.figshare.com/files/1360619", "https://ndownloader.figshare.com/files/1360620", "https://ndownloader.figshare.com/files/1360621", "https://ndownloader.figshare.com/files/1360622", "https://ndownloader.figshare.com/files/1360623", "https://ndownloader.figshare.com/files/1360624"], "description"=>"<div><p>Protein-ligand docking is a key computational method in the design of starting points for the drug discovery process. We are motivated by the desire to automate large-scale docking using our popular docking engine idock and thus have developed a publicly-accessible web platform called istar. Without tedious software installation, users can submit jobs using our website. Our istar website supports 1) filtering ligands by desired molecular properties and previewing the number of ligands to dock, 2) monitoring job progress in real time, and 3) visualizing ligand conformations and outputting free energy and ligand efficiency predicted by idock, binding affinity predicted by RF-Score, putative hydrogen bonds, and supplier information for easy purchase, three useful features commonly lacked on other online docking platforms like DOCK Blaster or iScreen. We have collected 17,224,424 ligands from the All Clean subset of the ZINC database, and revamped our docking engine idock to version 2.0, further improving docking speed and accuracy, and integrating RF-Score as an alternative rescoring function. To compare idock 2.0 with the state-of-the-art AutoDock Vina 1.1.2, we have carried out a rescoring benchmark and a redocking benchmark on the 2,897 and 343 protein-ligand complexes of PDBbind v2012 refined set and CSAR NRC HiQ Set 24Sept2010 respectively, and an execution time benchmark on 12 diverse proteins and 3,000 ligands of different molecular weight. Results show that, under various scenarios, idock achieves comparable success rates while outperforming AutoDock Vina in terms of docking speed by at least 8.69 times and at most 37.51 times. When evaluated on the PDBbind v2012 core set, our istar platform combining with RF-Score manages to reproduce Pearson's correlation coefficient and Spearman's correlation coefficient of as high as 0.855 and 0.859 respectively between the experimental binding affinity and the predicted binding affinity of the docked conformation. istar is freely available at <a href=\"http://istar.cse.cuhk.edu.hk/idock\" target=\"_blank\">http://istar.cse.cuhk.edu.hk/idock</a>.</p></div>", "links"=>[], "tags"=>["Biochemistry", "Biochemistry simulations", "Biomacromolecule-ligand interactions", "biophysics", "Biophysics simulations", "Computational biology", "Macromolecular structure analysis", "Macromolecular complex analysis", "protein structure", "Biochemical simulations", "Biophysic al simulations", "Computer applications", "Web-based applications", "Computing methods", "cloud computing", "software engineering", "Software tools", "large-scale", "protein-ligand"], "article_id"=>913252, "categories"=>["Biological Sciences", "Engineering"], "users"=>["Hongjian Li", "Kwong-Sak Leung", "Pedro J. Ballester", "Man-Hon Wong"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0085678.s001", "https://dx.doi.org/10.1371/journal.pone.0085678.s002", "https://dx.doi.org/10.1371/journal.pone.0085678.s003", "https://dx.doi.org/10.1371/journal.pone.0085678.s004", "https://dx.doi.org/10.1371/journal.pone.0085678.s005", "https://dx.doi.org/10.1371/journal.pone.0085678.s006", "https://dx.doi.org/10.1371/journal.pone.0085678.s007", "https://dx.doi.org/10.1371/journal.pone.0085678.s008"], "stats"=>{"downloads"=>21, "page_views"=>18, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_istar_A_Web_Platform_for_Large_Scale_Protein_Ligand_Docking_/913252", "title"=>"istar: A Web Platform for Large-Scale Protein-Ligand Docking", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2014-01-24 03:29:08"}
  • {"files"=>["https://ndownloader.figshare.com/files/1360594"], "description"=>"<p>Redocking success rates of idock and AuoDock Vina on the PDBbind v2012 refined set ( = 2,897), the PDBbind v2011 refined set ( = 2,455), and the CSAR NRC HiQ Set 24Sept2010 ( = 343) under various conditions regarding the (Root Mean Square Deviation) values between the crystal and docked conformations. By default, both programs output 9 predicted conformations per ligand. refers to the value between the crystal conformation and the th docked conformation, i.e. the one with the th highest predicted binding affinity, while refers to the value between the crystal conformation and the closest docked conformation, i.e. the one with the minimum value. . In conclusion, idock has a slightly higher conformation generation error than AutoDock Vina.</p>", "links"=>[], "tags"=>["Biochemistry", "Biochemistry simulations", "Biomacromolecule-ligand interactions", "biophysics", "Biophysics simulations", "Computational biology", "Macromolecular structure analysis", "Macromolecular complex analysis", "protein structure", "Biochemical simulations", "Biophysic al simulations", "Computer applications", "Web-based applications", "Computing methods", "cloud computing", "software engineering", "Software tools"], "article_id"=>913249, "categories"=>["Biological Sciences", "Engineering"], "users"=>["Hongjian Li", "Kwong-Sak Leung", "Pedro J. Ballester", "Man-Hon Wong"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0085678.t002", "stats"=>{"downloads"=>1, "page_views"=>16, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Redocking_success_rates_/913249", "title"=>"Redocking success rates.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-01-24 03:29:08"}
  • {"files"=>["https://ndownloader.figshare.com/files/1360592"], "description"=>"<p>The RF-Score was re-trained on PDBbind v2012 refined set ( = 2,897) for prospective prediction purpose. Values are in or unit.  = 0.855,  = 0.859,  = 0.73,  = 0.73.</p>", "links"=>[], "tags"=>["Biochemistry", "Biochemistry simulations", "Biomacromolecule-ligand interactions", "biophysics", "Biophysics simulations", "Computational biology", "Macromolecular structure analysis", "Macromolecular complex analysis", "protein structure", "Biochemical simulations", "Biophysic al simulations", "Computer applications", "Web-based applications", "Computing methods", "cloud computing", "software engineering", "Software tools", "rf-score", "docked", "conformation", "binding", "affinity", "pdbbind", "v2012", "redocking"], "article_id"=>913247, "categories"=>["Biological Sciences", "Engineering"], "users"=>["Hongjian Li", "Kwong-Sak Leung", "Pedro J. Ballester", "Man-Hon Wong"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0085678.g007", "stats"=>{"downloads"=>1, "page_views"=>18, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Scatter_plot_of_the_RF_Score_of_the_first_docked_conformation_against_the_experimental_binding_affinity_on_PDBbind_v2012_core_set__201_in_the_redocking_benchmark_/913247", "title"=>"Scatter plot of the RF-Score of the first docked conformation against the experimental binding affinity on PDBbind v2012 core set ( = 201) in the redocking benchmark.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-01-24 03:29:08"}

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