{"id":"W2060095573","doi":"10.1021/jm010096n","title":"Design and Synthesis of Matrix Metalloproteinase Inhibitors Guided by Molecular Modeling. Picking the S<sub>1</sub>Pocket Using Conformationally Constrained Inhibitors","year":2001,"lang":"en","type":"article","venue":"Journal of Medicinal Chemistry","topic":"Protease and Inhibitor Mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Chemistry; Hydroxamic acid; AutoDock; Selectivity; Molecular model; Stereochemistry; Combinatorial chemistry; Matrix metalloproteinase; Matrix metalloproteinase inhibitor; Binding pocket; Proline; Binding site; Enzyme inhibitor; Enzyme; Structure–activity relationship; Biochemistry; Amino acid; In vitro; Gene; Catalysis","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001230359,0.0002417176,0.0003584521,0.00005402338,0.0001027247,0.00002585652,0.0002383931,0.0002186187,0.00001322994],"category_scores_gemma":[0.0005229908,0.0001815569,0.0001682325,0.0001140508,0.0001870374,0.00002452565,0.00007664448,0.000265734,2.933597e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003693106,"about_ca_system_score_gemma":0.000329206,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007028705,"about_ca_topic_score_gemma":1.270502e-7,"domain_scores_codex":[0.9980473,0.0001005381,0.0008650065,0.0001899998,0.0005526053,0.0002445663],"domain_scores_gemma":[0.9984619,0.00005270193,0.000716714,0.0002092008,0.0003821246,0.0001773444],"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":[0.000183107,0.00005387904,0.00001180242,0.00008088239,0.0001481886,0.00004500919,0.00003810941,0.001400437,0.9967665,0.000005501923,0.0005128055,0.0007537531],"study_design_scores_gemma":[0.0008943643,0.0001703497,4.649051e-7,0.000311079,0.0001812453,0.00137083,0.0002965088,0.005036228,0.9912536,0.0001476926,0.0001573152,0.0001803517],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9409075,0.004100736,0.05433454,0.0002963225,0.00008062853,0.0001955457,0.000008308798,0.000004672427,0.00007179221],"genre_scores_gemma":[0.996522,0.0003676878,0.002552448,0.0001743771,0.0003350013,0.000007529395,0.000009776928,0.00002349075,0.000007651171],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05561459,"threshold_uncertainty_score":0.7403674,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01342496417219989,"score_gpt":0.2472501643556214,"score_spread":0.2338252001834215,"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."}}