{"id":"W2066506901","doi":"10.1002/chem.201003402","title":"Combining Glycomimetic and Multivalent Strategies toward Designing Potent Bacterial Lectin Inhibitors","year":2011,"lang":"en","type":"article","venue":"Chemistry - A European Journal","topic":"Glycosylation and Glycoproteins Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":95,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Isothermal titration calorimetry; Chemistry; Galactosides; Glycoconjugate; Dissociation constant; Molecular binding; Combinatorial chemistry; Conjugate; Docking (animal); Avidity; Stereochemistry; Galectin; Lectin; Click chemistry; Bioorthogonal chemistry; Glycosylation; Biochemistry; Molecule; Glycoside; Organic chemistry","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000187854,0.0005749563,0.0003439293,0.0002456944,0.00008714219,0.0004535658,0.0003534597,0.0002908192,0.0004533285],"category_scores_gemma":[0.0001510499,0.0001493073,0.000187153,0.0002901982,0.0002201777,0.0002807035,0.0003843857,0.0004257624,0.0003355017],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004953156,"about_ca_system_score_gemma":0.000212451,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003267998,"about_ca_topic_score_gemma":0.0006287849,"domain_scores_codex":[0.9998719,0.00002243095,0.00001132384,0.00002874033,0.00003555682,0.00003011464],"domain_scores_gemma":[0.9999415,0.00000854984,0.00001796335,0.000005687567,0.000007940839,0.00001825196],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00007172683,0.00004229999,0.00007166489,0.00006442337,0.000008757363,0.00005622918,0.00001296584,0.0007459525,0.9929288,0.0003234649,0.00003606442,0.005637555],"study_design_scores_gemma":[0.00003459305,0.0006090914,0.0004325765,0.000006757528,0.00003005527,0.0002040497,0.00001227348,0.002428503,0.9912896,0.0001034928,0.004839147,0.000009800914],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9622146,0.005102415,0.02762426,0.0001458967,0.00004632005,0.0002751108,0.0002642298,0.0002192963,0.004107984],"genre_scores_gemma":[0.9633872,0.004137224,0.029305,0.0001390943,0.0000279136,0.0001383462,0.0003180328,0.00004305075,0.002504232],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0005749563,"threshold_uncertainty_score":0.003593802,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0396813204372218,"score_gpt":0.2511819635071125,"score_spread":0.2115006430698907,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}