{"id":"W4392288047","doi":"10.5530/pj.2024.16.8","title":"Predictive Simulation and Functional Insights of Serotonin Transporter: Ligand Interactions Explored through Database Analysis","year":2024,"lang":"en","type":"article","venue":"Pharmacognosy Journal","topic":"Receptor Mechanisms and Signaling","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Innovation Cluster (Canada)","funders":"","keywords":"Serotonin transporter; Serotonin Plasma Membrane Transport Proteins; Transporter; Paroxetine; Ligand (biochemistry); Chemistry; Serotonin; In silico; Hydrogen bond; Stereochemistry; Biochemistry; Receptor; Molecule","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":[0.000645548,0.0006295986,0.0008345926,0.0007643335,0.000613,0.0008803545,0.001206422,0.001265109,0.003566194],"category_scores_gemma":[0.00242273,0.0004035516,0.00077209,0.0009863154,0.0003838321,0.0006739862,0.0004926717,0.0007094601,0.0003527077],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001121613,"about_ca_system_score_gemma":0.001624472,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03007873,"about_ca_topic_score_gemma":0.02104789,"domain_scores_codex":[0.9997829,0.00008269288,0.00001264566,0.00002773174,0.00005192793,0.00004213762],"domain_scores_gemma":[0.9987179,0.0009624219,0.00006051177,0.00005732961,0.0001390888,0.00006277712],"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":[0.00005829958,0.0000530384,0.001477771,0.0000398006,0.00002781292,0.00007440341,0.00002774301,0.9928517,0.0003104195,0.002314614,0.0003907669,0.002373634],"study_design_scores_gemma":[0.000009974116,0.000007966451,0.0001247702,0.000002267311,0.000003781991,0.000004521359,0.00001320339,0.9987668,0.00009358612,0.0008176888,0.0001535166,0.000001939103],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9012365,0.0009998393,0.06826759,0.00149267,0.0001088111,0.000153953,0.003419781,0.001168658,0.02315224],"genre_scores_gemma":[0.9576035,0.0004065743,0.03702158,0.0001820747,0.00002112127,0.0002309287,0.002722208,0.0001022353,0.001709758],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03007873,"threshold_uncertainty_score":0.0598073,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03564194442728115,"score_gpt":0.32635496690729,"score_spread":0.2907130224800089,"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."}}