Designing Focused Chemical Libraries Enriched in Protein-Protein Interaction Inhibitors using Machine-Learning Methods
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{"title"=>"Designing focused chemical libraries enriched in protein-protein interaction inhibitors using machine-learning methods", "type"=>"journal", "authors"=>[{"first_name"=>"Christelle", "last_name"=>"Reynès", "scopus_author_id"=>"15056514600"}, {"first_name"=>"Hélène", "last_name"=>"Host", "scopus_author_id"=>"35074078000"}, {"first_name"=>"Anne Claude", "last_name"=>"Camproux", "scopus_author_id"=>"6603868516"}, {"first_name"=>"Guillaume", "last_name"=>"Laconde", "scopus_author_id"=>"7801431682"}, {"first_name"=>"Florence", "last_name"=>"Leroux", "scopus_author_id"=>"16157683700"}, {"first_name"=>"Anne", "last_name"=>"Mazars", "scopus_author_id"=>"6603108576"}, {"first_name"=>"Benoit", "last_name"=>"Deprez", "scopus_author_id"=>"6602099867"}, {"first_name"=>"Robin", "last_name"=>"Fahraeus", "scopus_author_id"=>"55907977100"}, {"first_name"=>"Bruno O.", "last_name"=>"Villoutreix", "scopus_author_id"=>"7004018746"}, {"first_name"=>"Olivier", "last_name"=>"Sperandio", "scopus_author_id"=>"18635304300"}], "year"=>2010, "source"=>"PLoS Computational Biology", "identifiers"=>{"isbn"=>"10.1371/journal.pcbi.1000695", "pmid"=>"20221258", "doi"=>"10.1371/journal.pcbi.1000695", "pui"=>"358620667", "issn"=>"1553734X", "sgr"=>"77950839899", "scopus"=>"2-s2.0-77950839899"}, "id"=>"543798c2-792a-3cf9-8fba-095e6e4c1c35", "abstract"=>"Protein-protein interactions (PPIs) may represent one of the next major classes of therapeutic targets. So far, only a minute fraction of the estimated 650,000 PPIs that comprise the human interactome are known with a tiny number of complexes being drugged. Such intricate biological systems cannot be cost-efficiently tackled using conventional high-throughput screening methods. Rather, time has come for designing new strategies that will maximize the chance for hit identification through a rationalization of the PPI inhibitor chemical space and the design of PPI-focused compound libraries (global or target-specific). Here, we train machine-learning-based models, mainly decision trees, using a dataset of known PPI inhibitors and of regular drugs in order to determine a global physico-chemical profile for putative PPI inhibitors. This statistical analysis unravels two important molecular descriptors for PPI inhibitors characterizing specific molecular shapes and the presence of a privileged number of aromatic bonds. The best model has been transposed into a computer program, PPI-HitProfiler, that can output from any drug-like compound collection a focused chemical library enriched in putative PPI inhibitors. Our PPI inhibitor profiler is challenged on the experimental screening results of 11 different PPIs among which the p53/MDM2 interaction screened within our own CDithem platform, that in addition to the validation of our concept led to the identification of 4 novel p53/MDM2 inhibitors. Collectively, our tool shows a robust behavior on the 11 experimental datasets by correctly profiling 70% of the experimentally identified hits while removing 52% of the inactive compounds from the initial compound collections. We strongly believe that this new tool can be used as a global PPI inhibitor profiler prior to screening assays to reduce the size of the compound collections to be experimentally screened while keeping most of the true PPI inhibitors. PPI-HitProfiler is freely available on request from our CDithem platform website, www.CDithem.com.", "link"=>"http://www.mendeley.com/research/designing-focused-chemical-libraries-enriched-proteinprotein-interaction-inhibitors-using-machinelea", "reader_count"=>127, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>6, "Researcher"=>43, "Student > Doctoral Student"=>4, "Student > Ph. D. Student"=>40, "Student > Postgraduate"=>2, "Student > Master"=>12, "Other"=>6, "Student > Bachelor"=>7, "Lecturer"=>1, "Professor"=>6}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>6, "Researcher"=>43, "Student > Doctoral Student"=>4, "Student > Ph. D. Student"=>40, "Student > Postgraduate"=>2, "Student > Master"=>12, "Other"=>6, "Student > Bachelor"=>7, "Lecturer"=>1, "Professor"=>6}, "reader_count_by_subject_area"=>{"Engineering"=>1, "Unspecified"=>1, "Biochemistry, Genetics and Molecular Biology"=>13, "Agricultural and Biological Sciences"=>46, "Medicine and Dentistry"=>2, "Neuroscience"=>1, "Pharmacology, Toxicology and Pharmaceutical Science"=>3, "Business, Management and Accounting"=>1, "Chemical Engineering"=>1, "Chemistry"=>44, "Computer Science"=>14}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>2}, "Neuroscience"=>{"Neuroscience"=>1}, "Chemistry"=>{"Chemistry"=>44}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>46}, "Computer Science"=>{"Computer Science"=>14}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>13}, "Unspecified"=>{"Unspecified"=>1}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>3}, "Chemical Engineering"=>{"Chemical Engineering"=>1}}, "reader_count_by_country"=>{"Canada"=>1, "Netherlands"=>2, "South Korea"=>1, "Romania"=>2, "United States"=>1, "Japan"=>1, "China"=>1, "Denmark"=>1, "United Kingdom"=>2, "Portugal"=>1, "Germany"=>4, "Spain"=>1}, "group_count"=>4}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/861458"], "description"=>"<p>The two collections were filtered with FAFDrugs2 for ADMET properties, and the resulting drug-like databases were profiled using PPI-HitProfiler (D.T.1 and D.T.2 versions) to estimate the size reduction. The percentage of reduction within the table is calculated with respect to the drug-like version of the collections.</p>", "links"=>[], "tags"=>["ppi-hitprofiler"], "article_id"=>531905, "categories"=>["Biochemistry"], "users"=>["Christelle Reynès", "Hélène Host", "Anne-Claude Camproux", "Guillaume Laconde", "Florence Leroux", "Anne Mazars", "Benoit Deprez", "Robin Fahraeus", "Bruno O. Villoutreix", "Olivier Sperandio"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000695.t002", "stats"=>{"downloads"=>4, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Effect_of_PPI_HitProfiler_on_the_size_of_two_commercial_collections_/531905", "title"=>"Effect of PPI-HitProfiler on the size of two commercial collections.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-03-05 00:31:45"}
  • {"files"=>["https://ndownloader.figshare.com/files/860179"], "description"=>"<p>The calculations were run using 7 physico-chemical, molecular weight, octanol/water partition coefficient, topological polar surface area, number of Hydrogen bond donors and acceptors, the number of rotatable bonds and the number of rigid bonds. PPI inhibitors are represented as red disks, and non-PPI inhibitors are represented as black circles.</p>", "links"=>[], "tags"=>["validation"], "article_id"=>530627, "categories"=>["Biochemistry"], "users"=>["Christelle Reynès", "Hélène Host", "Anne-Claude Camproux", "Guillaume Laconde", "Florence Leroux", "Anne Mazars", "Benoit Deprez", "Robin Fahraeus", "Bruno O. Villoutreix", "Olivier Sperandio"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000695.g001", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Principal_Component_Analysis_PCA_on_the_learning_and_validation_data_sets_/530627", "title"=>"Principal Component Analysis (PCA) on the learning and validation data sets.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-03-05 00:10:27"}
  • {"files"=>["https://ndownloader.figshare.com/files/861425"], "description"=>"<p>Results of the application of PPI-HitProfiler on topologically diverse PubChem BioAssay results and on the CDithem screening of the p53/MDM2 interaction. All data sets were previously filtered with FAF-Drugs2 using the same parameters as for the learning data set. The total number of inactive compounds (TN + FP). active compounds (TP + FN). remaining inactives (TN). and remaining actives (TP). are used to calculate the sensitivity and specificity of PPI-HitProfiler on each data set.</p><p>TP: number of PPI inhibitors correctly classified.</p><p>FP: number of non-PPI inhibitors classified as PPI-inhibitors.</p><p>TN: number of non-PPI inhibitors correctly classified.</p><p>FN: number of PPI inhibitors classified as non-PPI inhibitors.</p><p>Sensitivity = TP/(TP + FN).</p><p>Specificity = TN/(TN + FP).</p>", "links"=>[], "tags"=>["hts"], "article_id"=>531876, "categories"=>["Biochemistry"], "users"=>["Christelle Reynès", "Hélène Host", "Anne-Claude Camproux", "Guillaume Laconde", "Florence Leroux", "Anne Mazars", "Benoit Deprez", "Robin Fahraeus", "Bruno O. Villoutreix", "Olivier Sperandio"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000695.t003", "stats"=>{"downloads"=>7, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_PPI_HitProfiler_evaluation_on_HTS_results_/531876", "title"=>"PPI-HitProfiler evaluation on HTS results.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-03-05 00:31:16"}
  • {"files"=>["https://ndownloader.figshare.com/files/860376"], "description"=>"<p>Plot calculated on the 623 molecules of the learning data set: 66 PPI-inhibitors (dark filled triangles) +557 non-PPI inhibitors (dark circles). The two thresholds of the decision tree N°1 are RDF070m>13.31 and UI>3.95. This plot first highlights the poor correlation between RDF070m and UI (r<sup>2</sup> = 0.34). Secondly, this shows that most of the PPI-inhibitors are either above the RDF070m threshold (13.31) or above the UI threshold (3.95).</p>", "links"=>[], "tags"=>["rdf070m", "ui"], "article_id"=>530822, "categories"=>["Biochemistry"], "users"=>["Christelle Reynès", "Hélène Host", "Anne-Claude Camproux", "Guillaume Laconde", "Florence Leroux", "Anne Mazars", "Benoit Deprez", "Robin Fahraeus", "Bruno O. Villoutreix", "Olivier Sperandio"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000695.g003", "stats"=>{"downloads"=>1, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Correlation_Plot_between_RDF070m_and_UI_descriptors_/530822", "title"=>"Correlation Plot between RDF070m and UI descriptors.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-03-05 00:13:42"}
  • {"files"=>["https://ndownloader.figshare.com/files/861019"], "description"=>"<p>The calculations were run using 7 physico-chemical, molecular weight, octanol/water partition coefficient, topological polar surface area, number of Hydrogen bond donors and acceptors, the number of rotatable bonds and the number of rigid bonds. PPI inhibitors are represented as red disks, and non-PPI inhibitors are represented as black circles.</p>", "links"=>[], "tags"=>["biochemistry/bioinformatics", "biochemistry/drug discovery", "biochemistry/small molecule chemistry", "chemical biology/small molecule chemistry"], "article_id"=>531463, "categories"=>["Biochemistry"], "users"=>["Christelle Reynès", "Hélène Host", "Anne-Claude Camproux", "Guillaume Laconde", "Florence Leroux", "Anne Mazars", "Benoit Deprez", "Robin Fahraeus", "Bruno O. Villoutreix", "Olivier Sperandio"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000695.g008", "stats"=>{"downloads"=>1, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Principal_Component_Analysis_PCA_on_the_various_screening_results_AID1499_AID595_AID875_AID811_AID1330_CDithem_/531463", "title"=>"Principal Component Analysis (PCA) on the various screening results (AID1499, AID595, AID875, AID811, AID1330, CDithem).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-03-05 00:24:23"}
  • {"files"=>["https://ndownloader.figshare.com/files/861158"], "description"=>"<p>2D sketch of the 4 new inhibitors of the p53/MDM2 interaction identified by our CDithem fluorescence polarization assay along with their potency (pIC<sub>50</sub>) and their RDF070m and UI values.</p>", "links"=>[], "tags"=>["sketch", "inhibitors", "cdithem", "fluorescence", "polarization", "assay", "potency", "rdf070m", "ui"], "article_id"=>531592, "categories"=>["Biochemistry"], "users"=>["Christelle Reynès", "Hélène Host", "Anne-Claude Camproux", "Guillaume Laconde", "Florence Leroux", "Anne Mazars", "Benoit Deprez", "Robin Fahraeus", "Bruno O. Villoutreix", "Olivier Sperandio"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000695.g009", "stats"=>{"downloads"=>2, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_2D_sketch_of_the_4_new_inhibitors_of_the_p53_MDM2_interaction_identified_by_our_CDithem_fluorescence_polarization_assay_along_with_their_potency_pIC_50_and_their_RDF070m_and_UI_values_/531592", "title"=>"2D sketch of the 4 new inhibitors of the p53/MDM2 interaction identified by our CDithem fluorescence polarization assay along with their potency (pIC<sub>50</sub>) and their RDF070m and UI values.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-03-05 00:26:32"}
  • {"files"=>["https://ndownloader.figshare.com/files/861305"], "description"=>"<p>The application domain of PPI-HitProfiler has been evaluated using a Principal Component Analysis (PCA) on the 623 molecules of the learning data (red) set and the 357 initial E-Dragon descriptors that were used to construct the decision trees. The graph represents the 3 first axis of the PCA (40% of the variance) which have been used to calculate the coordinates of the 1,645 molecules of the ChemDiv subset (green) and the 3,060 molecules of the Asinex Subset (blue). A good overlap between the three subsets (Red, Green, and Blue) can be observed which indicates that the molecules from the Asinex and ChemDiv subsets stand within the applicability domain of PPI-HitProfiler and that the focused library resulting from the two subsets is meaningful.</p>", "links"=>[], "tags"=>["biochemistry/bioinformatics", "biochemistry/drug discovery", "biochemistry/small molecule chemistry", "chemical biology/small molecule chemistry"], "article_id"=>531753, "categories"=>["Biochemistry"], "users"=>["Christelle Reynès", "Hélène Host", "Anne-Claude Camproux", "Guillaume Laconde", "Florence Leroux", "Anne Mazars", "Benoit Deprez", "Robin Fahraeus", "Bruno O. Villoutreix", "Olivier Sperandio"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000695.g010", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Applicability_domain_of_PPI_HitProfiler_/531753", "title"=>"Applicability domain of PPI-HitProfiler.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-03-05 00:29:13"}
  • {"files"=>["https://ndownloader.figshare.com/files/860861"], "description"=>"<p>The calculations were run using 7 physico-chemical, molecular weight, octanol/water partition coefficient, topological polar surface area, number of Hydrogen bond donors and acceptors, the number of rotatable bonds and the number of rigid bonds. PPI inhibitors are represented as red disks, and non-PPI inhibitors are represented as black circles.</p>", "links"=>[], "tags"=>["biochemistry/bioinformatics", "biochemistry/drug discovery", "biochemistry/small molecule chemistry", "chemical biology/small molecule chemistry"], "article_id"=>531306, "categories"=>["Biochemistry"], "users"=>["Christelle Reynès", "Hélène Host", "Anne-Claude Camproux", "Guillaume Laconde", "Florence Leroux", "Anne Mazars", "Benoit Deprez", "Robin Fahraeus", "Bruno O. Villoutreix", "Olivier Sperandio"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000695.g007", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Principal_Component_Analysis_PCA_on_the_various_screening_results_AID1434_AID689_AID1018_AID1417_AID432_/531306", "title"=>"Principal Component Analysis (PCA) on the various screening results (AID1434, AID689, AID1018, AID1417, AID432).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-03-05 00:21:46"}
  • {"files"=>["https://ndownloader.figshare.com/files/860533"], "description"=>"<p>The RDF070m values have been calculated on 4 cocrystallized PPI inhibitors of the following PPIs: ICAM1/LFA, IL-2/IL-2Rα, p53/MDM2, and BIR3-Xiap/Smac. All 4 values are above the DTs threshold 13.31. The UI values are also indicated, as well as the planarity of the binding pocket (calculated from the PROTORP server, <a href=\"http://www.bioinformatics.sussex.ac.uk/protorp/\" target=\"_blank\">http://www.bioinformatics.sussex.ac.uk/protorp/</a>). Also, on the panel below the calculated RDF070m values for 4 experimentally identified inhibitors (cyan) and 4 inactive compounds (green) of the CBFb/CBFa interaction taken from PubChemBioassay AID1434.</p>", "links"=>[], "tags"=>["molecular", "descriptor"], "article_id"=>530983, "categories"=>["Biochemistry"], "users"=>["Christelle Reynès", "Hélène Host", "Anne-Claude Camproux", "Guillaume Laconde", "Florence Leroux", "Anne Mazars", "Benoit Deprez", "Robin Fahraeus", "Bruno O. Villoutreix", "Olivier Sperandio"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000695.g004", "stats"=>{"downloads"=>4, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Effect_of_molecular_shape_on_descriptor_RDF070m_/530983", "title"=>"Effect of molecular shape on descriptor RDF070m.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-03-05 00:16:23"}
  • {"files"=>["https://ndownloader.figshare.com/files/860671"], "description"=>"<p>This figure shows that correlations between RDF070m and MW are significant only for compounds below 400. At higher MW, RDF070m performs better than MW.</p>", "links"=>[], "tags"=>["rdf070m", "mw", "ppi", "inhibitors", "non-ppi"], "article_id"=>531116, "categories"=>["Biochemistry"], "users"=>["Christelle Reynès", "Hélène Host", "Anne-Claude Camproux", "Guillaume Laconde", "Florence Leroux", "Anne Mazars", "Benoit Deprez", "Robin Fahraeus", "Bruno O. Villoutreix", "Olivier Sperandio"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000695.g005", "stats"=>{"downloads"=>1, "page_views"=>17, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Correlation_plot_between_RDF070m_and_MW_on_the_learning_data_set_66_PPI_inhibitors_Red_disks_557_non_PPI_inhibitors_black_circles_/531116", "title"=>"Correlation plot between RDF070m and MW on the learning data set (66 PPI inhibitors (Red disks) +557 non-PPI inhibitors (black circles)).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-03-05 00:18:36"}
  • {"files"=>["https://ndownloader.figshare.com/files/861498"], "description"=>"<p>Representation of the different optimized machine learning models, two decision trees, and three SVM models. Se (sensitivity), Sp (specificity) and EF (Enrichment) values are given for both, the learning data set (66 PPI inhibitors +557 non-PPI inhibitors) and the validation data set (26 PPI inhibitors +2,000 non-PPI inhibitors). (10-FCV = 10-Fold Cross Validation).</p>", "links"=>[], "tags"=>["machine-learning"], "article_id"=>531949, "categories"=>["Biochemistry"], "users"=>["Christelle Reynès", "Hélène Host", "Anne-Claude Camproux", "Guillaume Laconde", "Florence Leroux", "Anne Mazars", "Benoit Deprez", "Robin Fahraeus", "Bruno O. Villoutreix", "Olivier Sperandio"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000695.t001", "stats"=>{"downloads"=>1, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Prediction_results_of_the_five_best_machine_learning_models_/531949", "title"=>"Prediction results of the five best machine-learning models.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-03-05 00:32:29"}
  • {"files"=>["https://ndownloader.figshare.com/files/861536"], "description"=>"<p>SVM optimized parameters.</p>", "links"=>[], "tags"=>["optimized"], "article_id"=>531988, "categories"=>["Biochemistry"], "users"=>["Christelle Reynès", "Hélène Host", "Anne-Claude Camproux", "Guillaume Laconde", "Florence Leroux", "Anne Mazars", "Benoit Deprez", "Robin Fahraeus", "Bruno O. Villoutreix", "Olivier Sperandio"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000695.t004", "stats"=>{"downloads"=>1, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_SVM_optimized_parameters_/531988", "title"=>"SVM optimized parameters.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-03-05 00:33:08"}
  • {"files"=>["https://ndownloader.figshare.com/files/860288"], "description"=>"<p>The two decision trees share the same two descriptors RDF070m and UI. The two thresholds for RDF070m are identical (≥13.31) for D.T.1 and D.T.2 while the UI thresholds are different, ≥3.95 and ≥4.13 respectively for D.T.1 and D.T.2. The values for the corresponding sensitivity (Se) and specificity (Sp) are indicated for each DT.</p>", "links"=>[], "tags"=>["trees", "ppi", "inhibitors", "557", "non-ppi"], "article_id"=>530732, "categories"=>["Biochemistry"], "users"=>["Christelle Reynès", "Hélène Host", "Anne-Claude Camproux", "Guillaume Laconde", "Florence Leroux", "Anne Mazars", "Benoit Deprez", "Robin Fahraeus", "Bruno O. Villoutreix", "Olivier Sperandio"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000695.g002", "stats"=>{"downloads"=>4, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Representation_of_the_two_decision_trees_D_T_1_panel_a_and_D_T_2_panel_b_on_the_learning_data_set_66_PPI_inhibitors_557_non_PPI_inhibitors_/530732", "title"=>"Representation of the two decision trees D.T.1 (panel a) and D.T.2 (panel b) on the learning data set (66 PPI inhibitors + 557 non-PPI inhibitors).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-03-05 00:12:12"}
  • {"files"=>["https://ndownloader.figshare.com/files/427333", "https://ndownloader.figshare.com/files/427342", "https://ndownloader.figshare.com/files/427349", "https://ndownloader.figshare.com/files/427356"], "description"=>"<div><p>Protein-protein interactions (PPIs) may represent one of the next major classes of therapeutic targets. So far, only a minute fraction of the estimated 650,000 PPIs that comprise the human interactome are known with a tiny number of complexes being drugged. Such intricate biological systems cannot be cost-efficiently tackled using conventional high-throughput screening methods. Rather, time has come for designing new strategies that will maximize the chance for hit identification through a rationalization of the PPI inhibitor chemical space and the design of PPI-focused compound libraries (global or target-specific). Here, we train machine-learning-based models, mainly decision trees, using a dataset of known PPI inhibitors and of regular drugs in order to determine a global physico-chemical profile for putative PPI inhibitors. This statistical analysis unravels two important molecular descriptors for PPI inhibitors characterizing specific molecular shapes and the presence of a privileged number of aromatic bonds. The best model has been transposed into a computer program, PPI-HitProfiler, that can output from any drug-like compound collection a focused chemical library enriched in putative PPI inhibitors. Our PPI inhibitor profiler is challenged on the experimental screening results of 11 different PPIs among which the p53/MDM2 interaction screened within our own CDithem platform, that in addition to the validation of our concept led to the identification of 4 novel p53/MDM2 inhibitors. Collectively, our tool shows a robust behavior on the 11 experimental datasets by correctly profiling 70% of the experimentally identified hits while removing 52% of the inactive compounds from the initial compound collections. We strongly believe that this new tool can be used as a global PPI inhibitor profiler prior to screening assays to reduce the size of the compound collections to be experimentally screened while keeping most of the true PPI inhibitors. PPI-HitProfiler is freely available on request from our CDithem platform website, <a href=\"http://www.CDithem.com\">www.CDithem.com</a>.</p></div>", "links"=>[], "tags"=>["designing", "focused", "libraries", "enriched", "protein-protein", "inhibitors", "machine-learning", "methods"], "article_id"=>144344, "categories"=>["Biochemistry"], "users"=>["Christelle Reynès", "Hélène Host", "Anne-Claude Camproux", "Guillaume Laconde", "Florence Leroux", "Anne Mazars", "Benoit Deprez", "Robin Fahraeus", "Bruno O. Villoutreix", "Olivier Sperandio"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1000695.s001", "https://dx.doi.org/10.1371/journal.pcbi.1000695.s002", "https://dx.doi.org/10.1371/journal.pcbi.1000695.s003", "https://dx.doi.org/10.1371/journal.pcbi.1000695.s004"], "stats"=>{"downloads"=>21, "page_views"=>22, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Designing_Focused_Chemical_Libraries_Enriched_in_Protein_Protein_Interaction_Inhibitors_using_Machine_Learning_Methods/144344", "title"=>"Designing Focused Chemical Libraries Enriched in Protein-Protein Interaction Inhibitors using Machine-Learning Methods", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2010-03-05 01:12:24"}
  • {"files"=>["https://ndownloader.figshare.com/files/860750"], "description"=>"<p>Ui<sub>Aspirin</sub> = 3.17: aspirin has a total of 21 bonds, such that B = 21, 13 single bonds that have a contribution of 1 to b, and 8 double and aromatic bonds that have a contribution of 2 to b, such that the multiple bond count b is equal to b = 13x1+8x2 - 21 = 8. Thus Ui = log<sub>2</sub>(1+b) = log<sub>2</sub>(1+8) = 3.17.</p>", "links"=>[], "tags"=>["ui"], "article_id"=>531201, "categories"=>["Biochemistry"], "users"=>["Christelle Reynès", "Hélène Host", "Anne-Claude Camproux", "Guillaume Laconde", "Florence Leroux", "Anne Mazars", "Benoit Deprez", "Robin Fahraeus", "Bruno O. Villoutreix", "Olivier Sperandio"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000695.g006", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Calculation_of_UI_on_Aspirin_/531201", "title"=>"Calculation of UI on Aspirin.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-03-05 00:20:01"}

PMC Usage Stats | Further Information

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Relative Metric

{"start_date"=>"2010-01-01T00:00:00Z", "end_date"=>"2010-12-31T00:00:00Z", "subject_areas"=>[{"subject_area"=>"/Biology and life sciences/Neuroscience", "average_usage"=>[290, 563, 717, 832, 936, 1036, 1112, 1185, 1252, 1329, 1409, 1471, 1534, 1591, 1646, 1708, 1776, 1840, 1903, 1953, 2006, 2073, 2139, 2193, 2247, 2308, 2370, 2440, 2489, 2546, 2600, 2659, 2705, 2764, 2843, 2917, 2963, 3003, 3062, 3126, 3188, 3253, 3294, 3346, 3402, 3467, 3538, 3593, 3642]}, {"subject_area"=>"/Engineering and technology", "average_usage"=>[318, 573, 707, 808, 912, 1021, 1093, 1150, 1214, 1302, 1370, 1431, 1494, 1568, 1629, 1698, 1775, 1829, 1889, 1954, 2007, 2060, 2128, 2170, 2208, 2247, 2311, 2357, 2410, 2453, 2525, 2602, 2660, 2715, 2764, 2818, 2863, 2905, 2970, 3018, 3072, 3127, 3179, 3241, 3298, 3341, 3384, 3433, 3474]}]}
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