{"id":"W853235284","doi":"10.1021/acs.jcim.5b00333","title":"Detection of Binding Site Molecular Interaction Field Similarities","year":2015,"lang":"en","type":"article","venue":"Journal of Chemical Information and Modeling","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pharmacophore; Computational biology; Similarity (geometry); Function (biology); Drug repositioning; Computer science; Drug discovery; Data mining; Binding site; Biological system; Field (mathematics); Chemistry; Bioinformatics; Drug; Artificial intelligence; Biology; Stereochemistry; Mathematics; Genetics; Biochemistry","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":[],"consensus_categories":[],"category_scores_codex":[0.0004153838,0.00004358013,0.00009205892,0.0001522159,0.00001269693,0.0000688416,0.00009087464,0.00003463241,5.724069e-7],"category_scores_gemma":[0.0002569993,0.00003968483,0.00004104489,0.00009474402,0.00000666722,0.002239406,0.00005414134,0.0001195821,8.153218e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003335479,"about_ca_system_score_gemma":0.00004560755,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004744814,"about_ca_topic_score_gemma":8.824874e-8,"domain_scores_codex":[0.9992888,0.00002209534,0.0003897604,0.00003116119,0.0002178603,0.00005029679],"domain_scores_gemma":[0.999262,0.00007460956,0.0002391739,0.00004670535,0.0003208751,0.00005660674],"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":[0.00008955742,0.00001979378,0.00002304495,0.00005064726,0.00002292434,0.000001374352,0.002948731,0.7970117,0.1146176,0.00195733,0.00006370029,0.08319353],"study_design_scores_gemma":[0.0001819863,0.00003599755,8.61877e-7,0.00003109386,0.000003401283,0.00004584875,0.0001316019,0.7965293,0.2007479,0.002146701,0.0001120431,0.00003329479],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.458181,0.0000272684,0.5414416,0.0001341441,0.00009775929,0.0000114991,2.362856e-7,0.000004029148,0.0001024661],"genre_scores_gemma":[0.9647673,0.000009403187,0.03503222,0.0001640833,0.00002394114,3.123914e-7,0.000001018536,0.00000116242,5.213751e-7],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5065863,"threshold_uncertainty_score":0.1623515,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04271206218100553,"score_gpt":0.3148889242752347,"score_spread":0.2721768620942291,"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."}}