{"id":"W4313816625","doi":"10.1002/minf.202200186","title":"A comparison between 2D and 3D descriptors in QSAR modeling based on bio‐active conformations","year":2023,"lang":"en","type":"article","venue":"Molecular Informatics","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia Hospital","funders":"","keywords":"Quantitative structure–activity relationship; Cheminformatics; Random forest; Protein Data Bank (RCSB PDB); Molecular descriptor; Lasso (programming language); Computer science; Set (abstract data type); Artificial intelligence; Test set; Data mining; Pattern recognition (psychology); Machine learning; Chemistry; Computational chemistry; Stereochemistry","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":[0.003480259,0.0009034429,0.001285922,0.002451316,0.0003520932,0.001384185,0.0008375471,0.0007327533,0.001179655],"category_scores_gemma":[0.006194551,0.0003256503,0.001597325,0.002773718,0.0005212374,0.001352194,0.0008763398,0.0008517206,0.0004388849],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006818989,"about_ca_system_score_gemma":0.0008140834,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004274156,"about_ca_topic_score_gemma":0.002890326,"domain_scores_codex":[0.9981712,0.0009486988,0.0001533425,0.0001995583,0.0004366922,0.00009044595],"domain_scores_gemma":[0.9964843,0.002597417,0.0002477904,0.0003757687,0.0002440917,0.00005062053],"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":[0.0004393353,0.0001350822,0.006574923,0.0002758519,0.0001952296,0.00007244364,0.00005331755,0.9249422,0.002910662,0.005101214,0.0007731951,0.05852651],"study_design_scores_gemma":[0.00003931988,0.0002207239,0.002339981,0.00003518717,0.00004084112,0.00005010209,0.00002702347,0.9903165,0.00159711,0.003759568,0.0015397,0.00003401973],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.434488,0.009096988,0.5406067,0.0009829961,0.0001652394,0.0003068022,0.00510168,0.00150382,0.007747726],"genre_scores_gemma":[0.897436,0.002666413,0.09473334,0.0001666508,0.0000562229,0.0002719541,0.003928561,0.0001784557,0.0005622532],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004274156,"threshold_uncertainty_score":0.01840556,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05288652475164202,"score_gpt":0.337118736541096,"score_spread":0.284232211789454,"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."}}