{"id":"W2072370289","doi":"10.1002/jps.21086","title":"Combined 4D‐Fingerprint and Clustering Based Membrane‐Interaction QSAR Analyses for Constructing Consensus Caco‐2 Cell Permeation Virtual Screens","year":2007,"lang":"en","type":"article","venue":"Journal of Pharmaceutical Sciences","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"National Institute of General Medical Sciences; National Institutes of Health","keywords":"Quantitative structure–activity relationship; Permeation; Molecular descriptor; Fingerprint (computing); Chemistry; Biological system; Similarity (geometry); Cluster analysis; Artificial intelligence; Membrane; Computer science; Stereochemistry; Biochemistry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000881043,0.0009272896,0.0017455,0.001396923,0.0004894779,0.0006907537,0.0009045683,0.0005981883,0.001710262],"category_scores_gemma":[0.002094759,0.0003973652,0.001042317,0.0009717409,0.0002688555,0.0005134453,0.0007873842,0.0005937347,0.000411448],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006271136,"about_ca_system_score_gemma":0.001125338,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003979663,"about_ca_topic_score_gemma":0.004228876,"domain_scores_codex":[0.9994833,0.0001803904,0.00003247992,0.00006863892,0.0001758806,0.00005922363],"domain_scores_gemma":[0.9992356,0.0003791862,0.00005858514,0.00009211984,0.0002031632,0.00003124205],"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":[0.0009110332,0.0003166281,0.003960387,0.0004059511,0.0002146855,0.0001953009,0.00009918568,0.5779418,0.2975924,0.003439923,0.001013762,0.113909],"study_design_scores_gemma":[0.00004012245,0.0001287366,0.0008803027,0.00000444023,0.00004336932,0.00002750713,0.00002025822,0.9619731,0.03578811,0.0006571582,0.0004110938,0.00002583932],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5246226,0.0005427736,0.4683044,0.0001152585,0.00002233834,0.0002923016,0.001149546,0.00257582,0.002374956],"genre_scores_gemma":[0.8146774,0.0002075104,0.1824192,0.00004201477,0.000005943003,0.000218957,0.001736417,0.000159007,0.0005335767],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003979663,"threshold_uncertainty_score":0.007912993,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1451772011238818,"score_gpt":0.4580578326093229,"score_spread":0.3128806314854411,"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."}}