{"id":"W2135817448","doi":"10.1038/sj.bjp.0707515","title":"Towards the development of universal, fast and highly accurate docking/scoring methods: a long way to go","year":2007,"lang":"en","type":"review","venue":"British Journal of Pharmacology","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":509,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Docking (animal); Computer science; Virtual screening; Drug discovery; Drug development; Data science; Machine learning; Artificial intelligence; Bioinformatics; Drug; Pharmacology; Medicine; Biology","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00550632,0.0003063463,0.001353857,0.0005407545,0.0001714868,0.0001790213,0.001858447,0.0001368454,0.00002154405],"category_scores_gemma":[0.0001981598,0.0002689262,0.000306521,0.0008647499,0.000110528,0.0004187736,0.001061948,0.0007954558,0.00000507969],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003038,"about_ca_system_score_gemma":0.001354092,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001192549,"about_ca_topic_score_gemma":0.0000123282,"domain_scores_codex":[0.995608,0.001657791,0.001496625,0.0003844931,0.0004569103,0.0003961746],"domain_scores_gemma":[0.9959281,0.001915438,0.001375513,0.0001336906,0.000440351,0.0002069169],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00000998918,0.00004363311,4.228139e-7,0.0008208319,0.0003397656,0.000467437,0.0004563898,0.0007074343,0.00002547038,0.0002255653,0.0005506473,0.9963524],"study_design_scores_gemma":[0.0006083756,0.0001283082,0.0001903341,0.005564206,0.0004848969,0.008323358,0.00004352839,0.0008236832,0.0005863718,0.0002642935,0.9825409,0.0004417566],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0001860005,0.503598,0.4945639,0.000080948,0.001205426,0.0002331923,0.000004615004,0.000009360764,0.0001184999],"genre_scores_gemma":[0.00008850567,0.5976304,0.4018228,0.0001331857,0.0002611479,0.000007020576,0.000001197078,0.00002129164,0.00003450051],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9959106,"threshold_uncertainty_score":0.9999763,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1160150546054236,"score_gpt":0.4481378550656607,"score_spread":0.3321228004602371,"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."}}