{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003187657,0.0009195624,0.0008353119,0.001874962,0.0002508952,0.000670889,0.0005915848,0.0008810469,0.002124711],"category_scores_gemma":[0.0002908896,0.0003228625,0.0004158831,0.001194001,0.000478604,0.0009686776,0.0005179718,0.001301462,0.001213184],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004920351,"about_ca_system_score_gemma":0.0004156027,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005227064,"about_ca_topic_score_gemma":0.0008117542,"domain_scores_codex":[0.9998316,0.00002215873,0.00001601557,0.00004321723,0.00006345492,0.00002361337],"domain_scores_gemma":[0.999907,0.00004730979,0.00001617788,0.000003931735,0.00001690638,0.000008635199],"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":[0.00008486096,0.0001386505,0.0001462914,0.01920512,0.00007685935,0.0003547247,0.0001130403,0.0009327028,0.03095749,0.01917571,0.01530157,0.913513],"study_design_scores_gemma":[0.00001490505,0.0001675386,0.0003593491,0.0009859833,0.00004321586,0.001079684,0.00003677254,0.0001873468,0.007866629,0.001516538,0.9877175,0.00002459622],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0006106853,0.995023,0.0006398267,0.0001210998,0.0002110928,0.00001262531,0.00002586712,0.00001676878,0.003339121],"genre_scores_gemma":[0.004160454,0.991904,0.0009077424,0.0001877639,0.0001507924,0.00002694905,0.00005216418,0.000004114876,0.00260608],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.002124711,"threshold_uncertainty_score":0.007107794,"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."}}