{"id":"W2947303722","doi":"10.1002/minf.201900024","title":"Molecular Modelling of Potential Candidates for the Treatment of Depression","year":2019,"lang":"en","type":"article","venue":"Molecular Informatics","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Pharmacophore; Quantitative structure–activity relationship; Computational biology; Serotonin transporter; Docking (animal); Mode of action; Isoquinoline; Chemistry; Pharmacology; Serotonin; Stereochemistry; Biology; Medicine; Receptor; Biochemistry","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":[0.0002291888,0.0005782471,0.0008291403,0.0005745796,0.0003253648,0.0009115952,0.0006976916,0.001055528,0.009381132],"category_scores_gemma":[0.0005429781,0.0003055609,0.000621092,0.000602495,0.0002926652,0.0003259736,0.000332479,0.0004640743,0.001033876],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007133142,"about_ca_system_score_gemma":0.0008968948,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005089136,"about_ca_topic_score_gemma":0.003546982,"domain_scores_codex":[0.9999013,0.00003619952,0.000003989478,0.00001132731,0.00002800762,0.00001920597],"domain_scores_gemma":[0.9998612,0.00007740694,0.00001730826,0.00000764506,0.00002510807,0.00001125799],"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":[0.00008110749,0.00003890381,0.0003540675,0.00007596474,0.00001714742,0.0000784607,0.00001105371,0.9919708,0.00176614,0.002520132,0.0004035535,0.002682652],"study_design_scores_gemma":[0.00004571305,0.00007654912,0.0001819984,0.00001019784,0.00001343896,0.0000147483,0.00001449881,0.9966424,0.0007262307,0.0008570029,0.001411665,0.000005662588],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.707559,0.004827208,0.1820878,0.002140655,0.0003255171,0.0005186312,0.005629124,0.001121462,0.09579059],"genre_scores_gemma":[0.9700344,0.001053899,0.02131404,0.0001152896,0.00002097192,0.0002820166,0.001067722,0.00005177236,0.006059988],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009381132,"threshold_uncertainty_score":0.03138304,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01543643617665239,"score_gpt":0.2676626665438475,"score_spread":0.2522262303671951,"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."}}