{"id":"W2944359800","doi":"10.3390/molecules24091803","title":"Pharmaceutical Applications of Molecular Tweezers, Clefts and Clips","year":2019,"lang":"en","type":"review","venue":"Molecules","topic":"DNA and Nucleic Acid Chemistry","field":"Biochemistry, Genetics and Molecular Biology","cited_by":57,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Molecular tweezers; CLIPS; Molecular recognition; Nanotechnology; Tweezers; Computational biology; Chemistry; Computer science; Biology; Supramolecular chemistry; Materials science; Molecule; Artificial intelligence","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00009000842,0.0003403596,0.0007254189,0.00005188645,0.00003047972,0.00001660488,0.0003393617,0.0004646221,0.00001928517],"category_scores_gemma":[0.00002669847,0.0003152887,0.0003215896,0.0001025249,0.0001791012,0.000001584955,0.0002801769,0.0002004555,0.00002522675],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001061638,"about_ca_system_score_gemma":0.000120381,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001559087,"about_ca_topic_score_gemma":1.959713e-7,"domain_scores_codex":[0.9986115,0.00005947704,0.0003937526,0.0005510615,0.0001462227,0.0002380229],"domain_scores_gemma":[0.9989928,0.00001955716,0.000209767,0.0006074214,0.00005007609,0.0001203598],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001220623,0.00009266288,0.00001070004,0.01366492,0.0003833197,0.000008684841,0.000006044269,7.273544e-7,0.06371081,0.0002906399,0.001168502,0.9206508],"study_design_scores_gemma":[0.0001721488,0.00004382601,8.114531e-7,0.0005318348,0.000513411,0.00004384475,0.000006692248,0.000002123944,0.02834313,0.00002511052,0.9700096,0.0003074466],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0006746976,0.9951753,0.0008802736,0.00001247275,0.00004492302,0.0004790926,0.0001168679,0.00001247878,0.002603879],"genre_scores_gemma":[0.0007945817,0.9973105,0.0006038203,0.00007356852,0.000107327,0.00009517405,0.0004821465,0.0000685405,0.0004643127],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9688411,"threshold_uncertainty_score":0.9999299,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02529121632478946,"score_gpt":0.3376524438323267,"score_spread":0.3123612275075372,"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."}}