{"id":"W2034555045","doi":"10.1007/s10822-009-9309-9","title":"Development of QSAR models for microsomal stability: identification of good and bad structural features for rat, human and mouse microsomal stability","year":2009,"lang":"en","type":"article","venue":"Journal of Computer-Aided Molecular Design","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":false,"ca_institutions":"Women's Health Research Institute","funders":"","keywords":"Quantitative structure–activity relationship; In silico; Drug discovery; Classifier (UML); Metabolic stability; Computer science; Stability (learning theory); Feature selection; Artificial intelligence; Identification (biology); Machine learning; Computational biology; Bayesian probability; Data mining; Bioinformatics; Biology; Biochemistry; Gene","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":[0.0008394091,0.0008576542,0.0008124711,0.0005864023,0.0001492713,0.0004405132,0.0006390467,0.0003701192,0.0008867004],"category_scores_gemma":[0.001953748,0.0002209033,0.001097533,0.0004430436,0.0002304959,0.0004360616,0.0003272079,0.0008760039,0.000217769],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005587467,"about_ca_system_score_gemma":0.0005491763,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001817932,"about_ca_topic_score_gemma":0.002220692,"domain_scores_codex":[0.9998349,0.00005907976,0.00001136117,0.00002532268,0.00004609372,0.00002322415],"domain_scores_gemma":[0.9992898,0.0004142497,0.0001486372,0.0000338674,0.00009020453,0.00002325687],"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":[0.0009597504,0.000329339,0.008079821,0.0005823474,0.0003773266,0.0002084117,0.00007241748,0.8675211,0.0717164,0.004534991,0.001772491,0.04384565],"study_design_scores_gemma":[0.00009190819,0.0006350032,0.002772522,0.00001798834,0.0001525775,0.00008355003,0.00002436972,0.976854,0.01729235,0.001314337,0.0007356447,0.00002569399],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.7010212,0.003621635,0.2868804,0.0007239926,0.00004240318,0.0004325599,0.003802692,0.0010284,0.002446707],"genre_scores_gemma":[0.9678141,0.00100918,0.02866462,0.00009349293,0.00001157038,0.0003260652,0.001456936,0.000050033,0.0005740703],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.001817932,"threshold_uncertainty_score":0.004439294,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03892183850764915,"score_gpt":0.305450469251546,"score_spread":0.2665286307438968,"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."}}