{"id":"W2014066416","doi":"10.1007/s10822-006-9091-x","title":"Ultrafast de novo docking combining pharmacophores and combinatorics","year":2007,"lang":"en","type":"article","venue":"Journal of Computer-Aided Molecular Design","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":false,"ca_institutions":"Emergent BioSolutions (Canada)","funders":"Universität Hamburg","keywords":"Pharmacophore; Docking (animal); Computer science; Combinatorial chemistry; Computational biology; Chemistry; Stereochemistry; Biology","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":[0.004337293,0.0002599722,0.000412361,0.0004891325,0.0001448072,0.0003588499,0.0009798617,0.00008434033,0.000002175982],"category_scores_gemma":[0.0001409474,0.0002590934,0.0001679823,0.00063001,0.00007048136,0.0006252972,0.0002704193,0.0004665946,0.000002206653],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001566201,"about_ca_system_score_gemma":0.0003189703,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002745009,"about_ca_topic_score_gemma":1.512929e-7,"domain_scores_codex":[0.9971173,0.0005830083,0.0007872913,0.0003042615,0.0007287121,0.0004793786],"domain_scores_gemma":[0.9968885,0.001512559,0.0005755203,0.0002685982,0.0004093713,0.0003455067],"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":[0.0002254669,0.0005353679,0.0009265967,0.00008440165,0.0004173703,0.006406051,0.002394111,0.6386897,0.1272907,0.108087,0.0004814224,0.1144618],"study_design_scores_gemma":[0.00364973,0.001044453,0.00453785,0.0002704072,0.00008273769,0.007010088,0.0000464378,0.648791,0.236704,0.09659591,0.0006182799,0.0006490789],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2831202,0.0004105431,0.7153073,0.0002131816,0.0007153355,0.0001057085,2.877129e-7,0.00003797797,0.0000894063],"genre_scores_gemma":[0.61735,0.00002898335,0.3820374,0.0004473805,0.0001166409,6.580002e-7,2.554096e-7,0.00001630212,0.000002425984],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.3342297,"threshold_uncertainty_score":0.9999861,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01956909225155882,"score_gpt":0.3029252603312046,"score_spread":0.2833561680796458,"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."}}