{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003260308,0.00063825,0.0008048943,0.0002738929,0.0001897793,0.0006108228,0.0005179479,0.0003512093,0.001253356],"category_scores_gemma":[0.0003771337,0.0003731201,0.0003827687,0.0003683575,0.0002241068,0.0002507319,0.0001996554,0.0006286594,0.000762578],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005870932,"about_ca_system_score_gemma":0.0006393995,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009214099,"about_ca_topic_score_gemma":0.001670052,"domain_scores_codex":[0.9998499,0.00003240395,0.00001492775,0.00002944584,0.00005008026,0.00002322574],"domain_scores_gemma":[0.9998991,0.00002035702,0.00002859482,0.00000923433,0.0000158831,0.00002673755],"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":[0.0006245286,0.0002006888,0.000388738,0.0004192853,0.00006312582,0.0003782438,0.00009490779,0.05110215,0.9111557,0.004516619,0.001158314,0.02989765],"study_design_scores_gemma":[0.0005128407,0.001361427,0.0005003267,0.00004041571,0.0001091414,0.0004884906,0.00004682125,0.08091973,0.8893312,0.0007615696,0.02586854,0.00005962731],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7007228,0.01055204,0.2694637,0.0006029082,0.0001822026,0.002037721,0.002751573,0.001667513,0.01201947],"genre_scores_gemma":[0.7338943,0.008716206,0.2479472,0.000195932,0.00002957442,0.001053769,0.001987047,0.0001894827,0.005986398],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001253356,"threshold_uncertainty_score":0.004259646,"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."}}