{"id":"W3187230085","doi":"10.3390/molecules26164767","title":"In Silico Identification of Tripeptides as Lead Compounds for the Design of KOR Ligands","year":2021,"lang":"en","type":"article","venue":"Molecules","topic":"Receptor Mechanisms and Signaling","field":"Biochemistry, Genetics and Molecular Biology","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada Research Chairs; University of Toronto","funders":"Ministero della Salute","keywords":"In silico; Virtual screening; Tripeptide; Computational biology; Identification (biology); Drug discovery; Lead (geology); Workflow; Chemistry; Novelty; DrugBank; Pharmacology; Combinatorial chemistry; Computer science; Drug; Bioinformatics; Peptide; Medicine; Biology; Biochemistry; Database","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":[0.0003727873,0.000745404,0.001133346,0.0005587768,0.000274237,0.0008009631,0.0005605149,0.0004686587,0.002821889],"category_scores_gemma":[0.0007551148,0.0003310583,0.0005913741,0.000443531,0.0002199072,0.0003277386,0.0003522985,0.0004725843,0.0004236135],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003741491,"about_ca_system_score_gemma":0.0008684571,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009687491,"about_ca_topic_score_gemma":0.001728188,"domain_scores_codex":[0.9998729,0.00004593249,0.000006642088,0.00001879347,0.00002917159,0.00002645097],"domain_scores_gemma":[0.9996916,0.0001967866,0.00003259207,0.00001514255,0.00003466148,0.00002923776],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004947467,0.00028258,0.001810817,0.0004254711,0.0001179021,0.0003740659,0.0000338865,0.9548147,0.02416665,0.002649038,0.0006536323,0.01417654],"study_design_scores_gemma":[0.0001089008,0.0005634008,0.0003450144,0.00002308822,0.00009984984,0.00006747473,0.00004321468,0.9841956,0.01220777,0.0009917924,0.001339347,0.0000144811],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8714888,0.002978405,0.1078223,0.0003909102,0.00009250418,0.000311464,0.001362586,0.0008353753,0.01471775],"genre_scores_gemma":[0.9300016,0.001554652,0.06508839,0.0001215906,0.00001740698,0.0003608362,0.001044308,0.00009493756,0.001716319],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002821889,"threshold_uncertainty_score":0.009440184,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01885294350119008,"score_gpt":0.2716546394763502,"score_spread":0.2528016959751601,"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."}}