Combining Metabolite-Based Pharmacophores with Bayesian Machine Learning Models for Mycobacterium tuberculosis Drug Discovery
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{"title"=>"Combining metabolite-based pharmacophores with Bayesian machine learning models for mycobacterium tuberculosis drug discovery", "type"=>"journal", "authors"=>[{"first_name"=>"Sean", "last_name"=>"Ekins", "scopus_author_id"=>"7004648757"}, {"first_name"=>"Peter B.", "last_name"=>"Madrid", "scopus_author_id"=>"9841429500"}, {"first_name"=>"Malabika", "last_name"=>"Sarker", "scopus_author_id"=>"36646631400"}, {"first_name"=>"Shao Gang", "last_name"=>"Li", "scopus_author_id"=>"56555623100"}, {"first_name"=>"Nisha", "last_name"=>"Mittal", "scopus_author_id"=>"56381090200"}, {"first_name"=>"Pradeep", "last_name"=>"Kumar", "scopus_author_id"=>"55760856400"}, {"first_name"=>"Xin", "last_name"=>"Wang", "scopus_author_id"=>"55981076300"}, {"first_name"=>"Thomas P.", "last_name"=>"Stratton", "scopus_author_id"=>"57008837100"}, {"first_name"=>"Matthew", "last_name"=>"Zimmerman", "scopus_author_id"=>"56688142500"}, {"first_name"=>"Carolyn", "last_name"=>"Talcott", "scopus_author_id"=>"7003406044"}, {"first_name"=>"Pauline", "last_name"=>"Bourbon", "scopus_author_id"=>"57008868200"}, {"first_name"=>"Mike", "last_name"=>"Travers", "scopus_author_id"=>"57197222403"}, {"first_name"=>"Maneesh", "last_name"=>"Yadav", "scopus_author_id"=>"55923196600"}, {"first_name"=>"Joel S.", "last_name"=>"Freundlich", "scopus_author_id"=>"8919176600"}], "year"=>2015, "source"=>"PLoS ONE", "identifiers"=>{"scopus"=>"2-s2.0-84950336404", "sgr"=>"84950336404", "issn"=>"19326203", "doi"=>"10.1371/journal.pone.0141076", "pmid"=>"26517557", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)", "pui"=>"607277557"}, "id"=>"cae1544a-8ef5-3dc8-87f4-d4eb1de79951", "abstract"=>"Integrated computational approaches for Mycobacterium tuberculosis (Mtb) are useful to identify new molecules that could lead to future tuberculosis (TB) drugs. Our approach uses information derived from the TBCyc pathway and genome database, the Collaborative Drug Discovery TB database combined with 3D pharmacophores and dual event Bayesian models of whole-cell activity and lack of cytotoxicity. We have prioritized a large number of molecules that may act as mimics of substrates and metabolites in the TB metabolome. We computationally searched over 200,000 commercial molecules using 66 pharmacophores based on substrates and metabolites from Mtb and further filtering with Bayesian models. We ultimately tested 110 compounds in vitro that resulted in two compounds of interest, BAS 04912643 and BAS 00623753 (MIC of 2.5 and 5 μg/mL, respectively). These molecules were used as a starting point for hit-to-lead optimization. The most promising class proved to be the quinoxaline di-N-oxides, evidenced by transcriptional profiling to induce mRNA level perturbations most closely resembling known protonophores. One of these, SRI58 exhibited an MIC = 1.25 μg/mL versus Mtb and a CC50 in Vero cells of >40 μg/mL, while featuring fair Caco-2 A-B permeability (2.3 x 10-6 cm/s), kinetic solubility (125 μM at pH 7.4 in PBS) and mouse metabolic stability (63.6% remaining after 1 h incubation with mouse liver microsomes). Despite demonstration of how a combined bioinformatics/cheminformatics approach afforded a small molecule with promising in vitro profiles, we found that SRI58 did not exhibit quantifiable blood levels in mice.", "link"=>"http://www.mendeley.com/research/combining-metabolitebased-pharmacophores-bayesian-machine-learning-models-mycobacterium-tuberculosis", "reader_count"=>34, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>4, "Researcher"=>6, "Student > Ph. D. Student"=>4, "Student > Postgraduate"=>2, "Student > Master"=>13, "Other"=>1, "Student > Bachelor"=>2, "Lecturer"=>1, "Professor"=>1}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>4, "Researcher"=>6, "Student > Ph. D. Student"=>4, "Student > Postgraduate"=>2, "Student > Master"=>13, "Other"=>1, "Student > Bachelor"=>2, "Lecturer"=>1, "Professor"=>1}, "reader_count_by_subject_area"=>{"Biochemistry, Genetics and Molecular Biology"=>9, "Agricultural and Biological Sciences"=>7, "Medicine and Dentistry"=>1, "Pharmacology, Toxicology and Pharmaceutical Science"=>2, "Chemistry"=>7, "Psychology"=>1, "Computer Science"=>6, "Immunology and Microbiology"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Chemistry"=>{"Chemistry"=>7}, "Psychology"=>{"Psychology"=>1}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>7}, "Computer Science"=>{"Computer Science"=>6}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>9}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>2}}, "reader_count_by_country"=>{"United States"=>2, "Brazil"=>1, "Mexico"=>1}, "group_count"=>0}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/2391878"], "description"=>"<p>Synthetic routes to the A) arylamide and B) quinoxaline di-<i>N</i>-oxide families.</p>", "links"=>[], "tags"=>["mic", "compound", "substrate", "3 D pharmacophores", "metabolite", "approach", "Collaborative Drug Discovery TB database", "pbs", "SRI 58", "mRNA level perturbations", "cc", "1 h incubation", "mouse liver microsomes", "mtb", "exhibit quantifiable blood levels", "event Bayesian models", "Bayesian Machine Learning Models", "bas", "Mycobacterium tuberculosis Drug Discovery Integrated", "molecule"], "article_id"=>1590144, "categories"=>["Uncategorised"], "users"=>["Sean Ekins", "Peter B. Madrid", "Malabika Sarker", "Shao-Gang Li", "Nisha Mittal", "Pradeep Kumar", "Xin Wang", "Thomas P. Stratton", "Matthew Zimmerman", "Carolyn talcott", "Pauline Bourbon", "Mike Travers", "Maneesh Yadav", "Joel S. Freundlich"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0141076.g002", "stats"=>{"downloads"=>0, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Synthetic_routes_to_the_A_arylamide_and_B_quinoxaline_di_N_oxide_families_/1590144", "title"=>"Synthetic routes to the A) arylamide and B) quinoxaline di-<i>N</i>-oxide families.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-10-30 02:47:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/2391889", "https://ndownloader.figshare.com/files/2391890", "https://ndownloader.figshare.com/files/2391891", "https://ndownloader.figshare.com/files/2391892", "https://ndownloader.figshare.com/files/2391893", "https://ndownloader.figshare.com/files/2391894", "https://ndownloader.figshare.com/files/2391895", "https://ndownloader.figshare.com/files/2391896", "https://ndownloader.figshare.com/files/2391897", "https://ndownloader.figshare.com/files/2391898"], "description"=>"<div><p>Integrated computational approaches for <i>Mycobacterium tuberculosis</i> (<i>Mtb</i>) are useful to identify new molecules that could lead to future tuberculosis (TB) drugs. Our approach uses information derived from the TBCyc pathway and genome database, the Collaborative Drug Discovery TB database combined with 3D pharmacophores and dual event Bayesian models of whole-cell activity and lack of cytotoxicity. We have prioritized a large number of molecules that may act as mimics of substrates and metabolites in the TB metabolome. We computationally searched over 200,000 commercial molecules using 66 pharmacophores based on substrates and metabolites from <i>Mtb</i> and further filtering with Bayesian models. We ultimately tested 110 compounds <i>in vitro</i> that resulted in two compounds of interest, BAS 04912643 and BAS 00623753 (MIC of 2.5 and 5 μg/mL, respectively). These molecules were used as a starting point for hit-to-lead optimization. The most promising class proved to be the quinoxaline di<i>-N</i>-oxides, evidenced by transcriptional profiling to induce mRNA level perturbations most closely resembling known protonophores. One of these, SRI58 exhibited an MIC = 1.25 μg/mL versus <i>Mtb</i> and a CC<sub>50</sub> in Vero cells of >40 μg/mL, while featuring fair Caco-2 A-B permeability (2.3 x 10<sup>−6</sup> cm/s), kinetic solubility (125 μM at pH 7.4 in PBS) and mouse metabolic stability (63.6% remaining after 1 h incubation with mouse liver microsomes). Despite demonstration of how a combined bioinformatics/cheminformatics approach afforded a small molecule with promising <i>in vitro</i> profiles, we found that SRI58 did not exhibit quantifiable blood levels in mice.</p></div>", "links"=>[], "tags"=>["mic", "compound", "substrate", "3 D pharmacophores", "metabolite", "approach", "Collaborative Drug Discovery TB database", "pbs", "SRI 58", "mRNA level perturbations", "cc", "1 h incubation", "mouse liver microsomes", "mtb", "exhibit quantifiable blood levels", "event Bayesian models", "Bayesian Machine Learning Models", "bas", "Mycobacterium tuberculosis Drug Discovery Integrated", "molecule"], "article_id"=>1590152, "categories"=>["Uncategorised"], "users"=>["Sean Ekins", "Peter B. Madrid", "Malabika Sarker", "Shao-Gang Li", "Nisha Mittal", "Pradeep Kumar", "Xin Wang", "Thomas P. Stratton", "Matthew Zimmerman", "Carolyn talcott", "Pauline Bourbon", "Mike Travers", "Maneesh Yadav", "Joel S. Freundlich"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0141076.s001", "https://dx.doi.org/10.1371/journal.pone.0141076.s002", "https://dx.doi.org/10.1371/journal.pone.0141076.s003", "https://dx.doi.org/10.1371/journal.pone.0141076.s004", "https://dx.doi.org/10.1371/journal.pone.0141076.s005", "https://dx.doi.org/10.1371/journal.pone.0141076.s006", "https://dx.doi.org/10.1371/journal.pone.0141076.s007", "https://dx.doi.org/10.1371/journal.pone.0141076.s008", "https://dx.doi.org/10.1371/journal.pone.0141076.s009", "https://dx.doi.org/10.1371/journal.pone.0141076.s010"], "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Combining_Metabolite_Based_Pharmacophores_with_Bayesian_Machine_Learning_Models_for_Mycobacterium_tuberculosis_Drug_Discovery/1590152", "title"=>"Combining Metabolite-Based Pharmacophores with Bayesian Machine Learning Models for <i>Mycobacterium tuberculosis</i> Drug Discovery", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2015-10-30 02:47:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/2391885"], "description"=>"<p><sup>a</sup> RIF = rifampicin; EMB = ethambutol; INH = isoniazid; PAS = <i>p-</i>aminosalicyclic acid; KAN = kanamycin; SM = streptomycin; CAP = capreomycin</p><p>Activity of SRI50 against wild type and clinical MDR-TB strains.</p>", "links"=>[], "tags"=>["mic", "compound", "substrate", "3 D pharmacophores", "metabolite", "approach", "Collaborative Drug Discovery TB database", "pbs", "SRI 58", "mRNA level perturbations", "cc", "1 h incubation", "mouse liver microsomes", "mtb", "exhibit quantifiable blood levels", "event Bayesian models", "Bayesian Machine Learning Models", "bas", "Mycobacterium tuberculosis Drug Discovery Integrated", "molecule"], "article_id"=>1590150, "categories"=>["Uncategorised"], "users"=>["Sean Ekins", "Peter B. Madrid", "Malabika Sarker", "Shao-Gang Li", "Nisha Mittal", "Pradeep Kumar", "Xin Wang", "Thomas P. Stratton", "Matthew Zimmerman", "Carolyn talcott", "Pauline Bourbon", "Mike Travers", "Maneesh Yadav", "Joel S. Freundlich"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0141076.t004", "stats"=>{"downloads"=>0, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Activity_of_SRI50_against_wild_type_and_clinical_MDR_TB_strains_/1590150", "title"=>"Activity of SRI50 against wild type and clinical MDR-TB strains.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-10-30 02:47:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/2391882"], "description"=>"<p>Molecule structures are in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0141076#pone.0141076.s001\" target=\"_blank\">S1 Data</a>.</p>", "links"=>[], "tags"=>["mic", "compound", "substrate", "3 D pharmacophores", "metabolite", "approach", "Collaborative Drug Discovery TB database", "pbs", "SRI 58", "mRNA level perturbations", "cc", "1 h incubation", "mouse liver microsomes", "mtb", "exhibit quantifiable blood levels", "event Bayesian models", "Bayesian Machine Learning Models", "bas", "Mycobacterium tuberculosis Drug Discovery Integrated", "molecule"], "article_id"=>1590147, "categories"=>["Uncategorised"], "users"=>["Sean Ekins", "Peter B. Madrid", "Malabika Sarker", "Shao-Gang Li", "Nisha Mittal", "Pradeep Kumar", "Xin Wang", "Thomas P. Stratton", "Matthew Zimmerman", "Carolyn talcott", "Pauline Bourbon", "Mike Travers", "Maneesh Yadav", "Joel S. Freundlich"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0141076.t001", "stats"=>{"downloads"=>0, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Mtb_growth_inhibitory_activities_of_BAS_00623753_and_a_small_set_of_analogs_/1590147", "title"=>"<i>Mtb</i> growth inhibitory activities of BAS 00623753 and a small set of analogs.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-10-30 02:47:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/2391881"], "description"=>"<p>100 SRI54 most induced and repressed genes (top-bottom) are clustered with responses to other treatments (left-right). The top dendrogram indicates relatedness of the <i>Mtb</i> perturbations based on gene clusters. Red indicates increase, blue indicates decrease and white no change in expression versus DMSO treatment. <i>Amp</i>, ampicillin; <i>EMB</i>, ethambutol; <i>TLM</i>, thiolactomycin; <i>INH</i>, isoniazid; <i>ETH</i>, ethionamide; <i>5-Cl-PZA</i>, 5-chloropyrazinamide, <i>CPZ</i>, chlorpromazine; <i>CCCP</i>, carbonyl cyanide 3-chlorophenylhydrazone; <i>GSNO</i>, S-nitrosoglutathione; <i>DNP</i>, 2,4-dinitrophenol.</p>", "links"=>[], "tags"=>["mic", "compound", "substrate", "3 D pharmacophores", "metabolite", "approach", "Collaborative Drug Discovery TB database", "pbs", "SRI 58", "mRNA level perturbations", "cc", "1 h incubation", "mouse liver microsomes", "mtb", "exhibit quantifiable blood levels", "event Bayesian models", "Bayesian Machine Learning Models", "bas", "Mycobacterium tuberculosis Drug Discovery Integrated", "molecule"], "article_id"=>1590146, "categories"=>["Uncategorised"], "users"=>["Sean Ekins", "Peter B. Madrid", "Malabika Sarker", "Shao-Gang Li", "Nisha Mittal", "Pradeep Kumar", "Xin Wang", "Thomas P. Stratton", "Matthew Zimmerman", "Carolyn talcott", "Pauline Bourbon", "Mike Travers", "Maneesh Yadav", "Joel S. Freundlich"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0141076.g004", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Mtb_transcriptional_response_to_SRI54_as_compared_to_other_small_molecule_antituberculars_and_environmental_stresses_/1590146", "title"=>"<i>Mtb</i> transcriptional response to SRI54 as compared to other small molecule antituberculars and environmental stresses.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-10-30 02:47:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/2391884"], "description"=>"<p>For microsomal stability, verapamil was used as a high-metabolism control (0.24% remaining with NADPH) and warfarin was a low-metabolism control (85% remaining with NADPH). The kinetic solubility limit was the highest concentration with no detectable precipitate. For Caco-2 cell permeability, compounds at a concentration of 10 μM were incubated for 2 h. P<sub>app</sub> = apparent permeability coefficient. All compounds showed poor recovery due to either low solubility or non-specific binding. Ranitidine, warfarin and talindol were used as low permeability, high permeability and P-gp efflux, controls respectively.</p>", "links"=>[], "tags"=>["mic", "compound", "substrate", "3 D pharmacophores", "metabolite", "approach", "Collaborative Drug Discovery TB database", "pbs", "SRI 58", "mRNA level perturbations", "cc", "1 h incubation", "mouse liver microsomes", "mtb", "exhibit quantifiable blood levels", "event Bayesian models", "Bayesian Machine Learning Models", "bas", "Mycobacterium tuberculosis Drug Discovery Integrated", "molecule"], "article_id"=>1590149, "categories"=>["Uncategorised"], "users"=>["Sean Ekins", "Peter B. Madrid", "Malabika Sarker", "Shao-Gang Li", "Nisha Mittal", "Pradeep Kumar", "Xin Wang", "Thomas P. Stratton", "Matthew Zimmerman", "Carolyn talcott", "Pauline Bourbon", "Mike Travers", "Maneesh Yadav", "Joel S. Freundlich"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0141076.t003", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Physiochemical_and_ADME_data_/1590149", "title"=>"Physiochemical and ADME data.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-10-30 02:47:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/2391883"], "description"=>"<p>Molecule structures are in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0141076#pone.0141076.s001\" target=\"_blank\">S1 Data</a>.</p>", "links"=>[], "tags"=>["mic", "compound", "substrate", "3 D pharmacophores", "metabolite", "approach", "Collaborative Drug Discovery TB database", "pbs", "SRI 58", "mRNA level perturbations", "cc", "1 h incubation", "mouse liver microsomes", "mtb", "exhibit quantifiable blood levels", "event Bayesian models", "Bayesian Machine Learning Models", "bas", "Mycobacterium tuberculosis Drug Discovery Integrated", "molecule"], "article_id"=>1590148, "categories"=>["Uncategorised"], "users"=>["Sean Ekins", "Peter B. Madrid", "Malabika Sarker", "Shao-Gang Li", "Nisha Mittal", "Pradeep Kumar", "Xin Wang", "Thomas P. Stratton", "Matthew Zimmerman", "Carolyn talcott", "Pauline Bourbon", "Mike Travers", "Maneesh Yadav", "Joel S. Freundlich"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0141076.t002", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Structures_and_activities_of_the_quinoxaline_di_N_oxide_family_nd_not_determined_/1590148", "title"=>"Structures and activities of the quinoxaline di-<i>N-</i>oxide family (nd = not determined).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-10-30 02:47:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/2391879"], "description"=>"<p>Structures of quinoxaline di-<i>N-</i>oxides with the most promising antitubercular activities and selectivities.</p>", "links"=>[], "tags"=>["mic", "compound", "substrate", "3 D pharmacophores", "metabolite", "approach", "Collaborative Drug Discovery TB database", "pbs", "SRI 58", "mRNA level perturbations", "cc", "1 h incubation", "mouse liver microsomes", "mtb", "exhibit quantifiable blood levels", "event Bayesian models", "Bayesian Machine Learning Models", "bas", "Mycobacterium tuberculosis Drug Discovery Integrated", "molecule"], "article_id"=>1590145, "categories"=>["Uncategorised"], "users"=>["Sean Ekins", "Peter B. Madrid", "Malabika Sarker", "Shao-Gang Li", "Nisha Mittal", "Pradeep Kumar", "Xin Wang", "Thomas P. Stratton", "Matthew Zimmerman", "Carolyn talcott", "Pauline Bourbon", "Mike Travers", "Maneesh Yadav", "Joel S. Freundlich"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0141076.g003", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Structures_of_quinoxaline_di_N_oxides_with_the_most_promising_antitubercular_activities_and_selectivities_/1590145", "title"=>"Structures of quinoxaline di-<i>N-</i>oxides with the most promising antitubercular activities and selectivities.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-10-30 02:47:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/2391877"], "description"=>"<p>D. best fit to indole-3-acetamide pharmacophore of BAS7571651, E best fit to lipoamide shape of BAS7571651. The pharmacophores consist of hydrogen bond acceptors (green) hydrogen bond donors (purple) and hydrophobic features (blue). The van der Waals surface was used to limit the number of compounds retrieved when screening the vendor library.</p>", "links"=>[], "tags"=>["mic", "compound", "substrate", "3 D pharmacophores", "metabolite", "approach", "Collaborative Drug Discovery TB database", "pbs", "SRI 58", "mRNA level perturbations", "cc", "1 h incubation", "mouse liver microsomes", "mtb", "exhibit quantifiable blood levels", "event Bayesian models", "Bayesian Machine Learning Models", "bas", "Mycobacterium tuberculosis Drug Discovery Integrated", "molecule"], "article_id"=>1590143, "categories"=>["Uncategorised"], "users"=>["Sean Ekins", "Peter B. Madrid", "Malabika Sarker", "Shao-Gang Li", "Nisha Mittal", "Pradeep Kumar", "Xin Wang", "Thomas P. Stratton", "Matthew Zimmerman", "Carolyn talcott", "Pauline Bourbon", "Mike Travers", "Maneesh Yadav", "Joel S. Freundlich"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0141076.g001", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Initial_pharmacophore_Bayesian_model_derived_hits_A_chemical_structures_in_vitro_antitubercular_activity_and_B_best_fit_to_menadione_pharmacophore_of_BAS04912643_C_best_fit_to_menadione_pharmacophore_of_B_BAS00623753_grey_/1590143", "title"=>"Initial pharmacophore/Bayesian model-derived hits: A) chemical structures, <i>in vitro</i> antitubercular activity, and B) best fit to menadione pharmacophore of BAS04912643, C) best fit to menadione pharmacophore of B. BAS00623753 (grey).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-10-30 02:47:31"}

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  • {"unique-ip"=>"14", "full-text"=>"14", "pdf"=>"4", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"1", "year"=>"2019", "month"=>"12"}
  • {"unique-ip"=>"11", "full-text"=>"9", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"2"}
  • {"unique-ip"=>"3", "full-text"=>"1", "pdf"=>"4", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"3"}
  • {"unique-ip"=>"9", "full-text"=>"11", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"4"}
  • {"unique-ip"=>"10", "full-text"=>"10", "pdf"=>"4", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"5"}
  • {"unique-ip"=>"5", "full-text"=>"4", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"2", "cited-by"=>"0", "year"=>"2020", "month"=>"6"}

Relative Metric

{"start_date"=>"2015-01-01T00:00:00Z", "end_date"=>"2015-12-31T00:00:00Z", "subject_areas"=>[]}
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