{"id":"W2538276222","doi":"10.1007/s10822-016-9986-0","title":"Compilation and physicochemical classification analysis of a diverse hERG inhibition database","year":2016,"lang":"en","type":"article","venue":"Journal of Computer-Aided Molecular Design","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":41,"is_retracted":false,"has_abstract":false,"ca_institutions":"Glycemic Index Laboratories","funders":"","keywords":"hERG; Lipophilicity; chEMBL; Chemistry; Drug discovery; Quantitative structure–activity relationship; Computational biology; Combinatorial chemistry; Computational chemistry; Stereochemistry; Biophysics; Biology; Biochemistry; Potassium channel","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009840642,0.0001522265,0.0004020877,0.0006372045,0.00004271349,0.00007194903,0.0003527406,0.0000533881,0.000005039697],"category_scores_gemma":[0.0001316405,0.0001191951,0.0002200815,0.0009415328,0.00007052409,0.0008698301,0.0001840377,0.0001018856,0.000001597189],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008016192,"about_ca_system_score_gemma":0.0001406003,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001542201,"about_ca_topic_score_gemma":2.125151e-7,"domain_scores_codex":[0.9977901,0.0005675721,0.0006393177,0.0002788308,0.0005798854,0.0001443012],"domain_scores_gemma":[0.9975184,0.0007124734,0.0007876713,0.0003485921,0.0004887111,0.0001442139],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001075492,0.0002598698,0.0003289639,0.00002357339,0.0007021905,0.00008265764,0.0002289081,0.06324996,0.8374645,0.0256893,0.0003010015,0.07156158],"study_design_scores_gemma":[0.001228997,0.0002996315,0.01727547,0.0001767372,0.000443024,0.00007938396,0.00001041418,0.8439768,0.1258649,0.01037761,0.00004192525,0.0002251096],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1972066,0.00006881161,0.802063,0.00042622,0.0001029098,0.00009203971,0.00001580127,0.00001466853,0.000009963111],"genre_scores_gemma":[0.6468415,0.00001861145,0.3529889,0.0000892445,0.00004494737,0.000001270696,0.000008329908,0.000005839202,0.000001388541],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7807269,"threshold_uncertainty_score":0.4860635,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04396314257522196,"score_gpt":0.3007815199154171,"score_spread":0.2568183773401951,"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."}}