{"id":"W3195509796","doi":"10.1021/acs.jcim.1c00856","title":"Toward Reducing hERG Affinities for DAT Inhibitors with a Combined Machine Learning and Molecular Modeling Approach","year":2021,"lang":"en","type":"article","venue":"Journal of Chemical Information and Modeling","topic":"Receptor Mechanisms and Signaling","field":"Biochemistry, Genetics and Molecular Biology","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Libin Cardiovascular Institute of Alberta; University of Calgary","funders":"National Institute on Drug Abuse; Canadian Institutes of Health Research; National Institutes of Health; University of Calgary","keywords":"hERG; Quantitative structure–activity relationship; Dopamine transporter; Pharmacology; Computational biology; Chemistry; Dopamine; Potassium channel; Medicine; Dopaminergic; Neuroscience; Biology; Stereochemistry; Internal medicine","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":[0.0006529631,0.001062981,0.0008479899,0.0004756126,0.0002658843,0.0006582378,0.0007545455,0.0007023832,0.00100462],"category_scores_gemma":[0.001042039,0.0004079336,0.0008852109,0.0004714365,0.0002747434,0.0008950096,0.0004134796,0.001075183,0.0002218915],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000623393,"about_ca_system_score_gemma":0.0009794581,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002595698,"about_ca_topic_score_gemma":0.002973729,"domain_scores_codex":[0.9997465,0.00009040857,0.00001634116,0.00003314237,0.00008867082,0.00002491085],"domain_scores_gemma":[0.9996336,0.0002102785,0.00006536877,0.00002563713,0.00005318113,0.00001195664],"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":[0.00006746813,0.0001402387,0.0007718046,0.0001757417,0.0000616741,0.00005673015,0.00001698587,0.9621016,0.01919648,0.003825251,0.0002695227,0.01331649],"study_design_scores_gemma":[0.00001061381,0.00007177725,0.0001120533,0.000006486448,0.00002008796,0.00001065385,0.000004848341,0.9946373,0.003967521,0.0006849082,0.0004675285,0.000006211931],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3453009,0.006019544,0.6331613,0.001417213,0.0001100058,0.0003373612,0.001029979,0.001001845,0.0116218],"genre_scores_gemma":[0.8782918,0.003229366,0.1158147,0.0002975615,0.0000396257,0.0004036255,0.0006938317,0.00007760541,0.001151902],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002595698,"threshold_uncertainty_score":0.005161226,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01937859092752102,"score_gpt":0.2345288547400764,"score_spread":0.2151502638125554,"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."}}