{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006337598,0.0002014981,0.0001425754,0.00003743095,0.0002294008,0.0001967655,0.0002287247,0.00006484627,0.0002360631],"category_scores_gemma":[0.0001440917,0.0001864866,0.00008958009,0.00006321524,0.0001301685,0.00001511915,0.0001887477,0.0003625509,0.00001552341],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002121714,"about_ca_system_score_gemma":0.0001122061,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004991928,"about_ca_topic_score_gemma":4.395442e-7,"domain_scores_codex":[0.9986013,0.00023848,0.0003273769,0.000303027,0.0001916513,0.0003382196],"domain_scores_gemma":[0.9992572,0.000008980088,0.0001497762,0.0001891569,0.0001196887,0.0002751663],"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":[0.0002439594,0.00005762424,0.000424741,0.00003454708,0.00004731123,0.0001257997,0.0004599708,0.000008885234,0.9927782,0.000002633509,0.0004262257,0.005390038],"study_design_scores_gemma":[0.002191555,0.0004430445,0.003711142,0.00009352939,0.00002323695,0.001055094,0.001537455,0.0001280601,0.9825603,0.00004654445,0.007836028,0.0003739967],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9878044,0.0002930124,0.002570322,0.00005332841,0.0001757788,0.0000991791,0.000002554343,0.00001880416,0.008982684],"genre_scores_gemma":[0.996411,0.0001120257,0.002221635,0.00002561998,0.0005120975,0.000003087885,0.00001806814,0.00004046876,0.0006560348],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01021795,"threshold_uncertainty_score":0.7604705,"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."}}