{"id":"W1560397137","doi":"10.1023/a:1015941316283","title":"Identifying potential binding modes and explaining partitioning behavior using flexible alignments and multidimensional scaling","year":2001,"lang":"en","type":"article","venue":"Journal of Computer-Aided Molecular Design","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Property (philosophy); Similarity (geometry); Projection (relational algebra); Multidimensional scaling; Computer science; Identification (biology); Biological system; Scaling; Molecular dynamics; Scheme (mathematics); Algorithm; Artificial intelligence; Statistical physics; Data mining; Chemistry; Mathematics; Computational chemistry; Physics; Machine learning; Biology; Mathematical analysis; Geometry; Image (mathematics)","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001576446,0.0002486931,0.0003718325,0.000502834,0.0003606559,0.0005676553,0.0003300401,0.00007518544,0.000002772121],"category_scores_gemma":[0.00005387111,0.0002590861,0.0001253856,0.0003394691,0.00006562075,0.001405509,0.0004183422,0.0002496146,0.00000106871],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009541247,"about_ca_system_score_gemma":0.0001331929,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006886542,"about_ca_topic_score_gemma":8.960113e-8,"domain_scores_codex":[0.9972727,0.0005316336,0.0007106223,0.000398053,0.0007263566,0.0003606441],"domain_scores_gemma":[0.9985018,0.0003190376,0.0005277297,0.0001891068,0.0002286096,0.0002336922],"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":[0.0000419868,0.00008923788,0.0005405818,0.00001716122,0.00009809421,0.001174322,0.0003536845,0.7622661,0.2081675,0.003067947,0.000009027126,0.0241744],"study_design_scores_gemma":[0.001005986,0.000139638,0.001499137,0.0003451445,0.00008373244,0.003810118,0.00007308418,0.9624892,0.02693853,0.003328274,0.000008868189,0.000278315],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4639316,0.0002953559,0.5354139,0.00003565208,0.0002037097,0.00009393199,4.348795e-7,0.00002138686,0.000004059311],"genre_scores_gemma":[0.498933,0.00002374523,0.5008903,0.00006165851,0.00007373891,0.000001847138,6.796033e-7,0.00001329626,0.000001725443],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2002231,"threshold_uncertainty_score":0.9999861,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06534173495527576,"score_gpt":0.3315578560303082,"score_spread":0.2662161210750325,"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."}}