{"id":"W2032245355","doi":"10.1021/jm0497141","title":"A Comparison of Methods for Modeling Quantitative Structure−Activity Relationships","year":2004,"lang":"en","type":"article","venue":"Journal of Medicinal Chemistry","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":231,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University; Dalhousie University","funders":"","keywords":"Quantitative structure–activity relationship; Partial least squares regression; Chemistry; Test set; Artificial neural network; Molecular descriptor; Artificial intelligence; Biological system; Pattern recognition (psychology); Machine learning; Computer science; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002124988,0.0001219158,0.0004330316,0.00008695879,0.00008118893,0.00002221439,0.0005165945,0.00008227258,0.000004220908],"category_scores_gemma":[0.002165444,0.0001041975,0.0001603883,0.0002733317,0.00006314427,0.000362156,0.00006271141,0.0004969899,1.418964e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001393037,"about_ca_system_score_gemma":0.000512686,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005347735,"about_ca_topic_score_gemma":6.566258e-7,"domain_scores_codex":[0.998466,0.0001869098,0.0006504467,0.0001611574,0.0003934108,0.0001421003],"domain_scores_gemma":[0.9967458,0.001579414,0.0007944358,0.0001853763,0.000581134,0.0001138355],"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":[0.0001760694,0.0001516783,0.0002201696,0.0001831333,0.00009682604,0.000003510724,0.001886292,0.8203168,0.1529574,0.008290133,0.00002963792,0.01568833],"study_design_scores_gemma":[0.0009639189,0.0001932151,0.0003459689,0.0001776038,0.00004918483,0.0001171123,0.0003855157,0.7139613,0.1853828,0.09828078,0.00003695183,0.0001056365],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3173473,0.0004959193,0.6813678,0.0005004624,0.0001634882,0.00004975097,0.000002624017,0.000006176913,0.00006651408],"genre_scores_gemma":[0.5160359,0.000002728611,0.4838795,0.000009481793,0.00006527495,7.273102e-7,6.106516e-7,0.0000037814,0.000001978736],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.1986886,"threshold_uncertainty_score":0.424905,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.171726358508559,"score_gpt":0.4889138672521705,"score_spread":0.3171875087436115,"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."}}