{"id":"W1990762022","doi":"10.1016/j.taap.2010.09.010","title":"A unified algorithm for predicting partition coefficients for PBPK modeling of drugs and environmental chemicals","year":2010,"lang":"en","type":"article","venue":"Toxicology and Applied Pharmacology","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":137,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"National Research Council Canada; Natural Sciences and Engineering Research Council of Canada; Agence Française de Sécurité Sanitaire de l'Environnement et du Travail","keywords":"Physiologically based pharmacokinetic modelling; Algorithm; Chemistry; Adipose tissue; Partition coefficient; Macro; Pharmacokinetics; Organic chemicals; Computer science; Organic chemistry; Bioinformatics; Environmental chemistry; 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":[0.0004897898,0.0001071923,0.0002007723,0.00006117137,0.0001332999,0.00001162377,0.0001640786,0.0001267751,0.00000812404],"category_scores_gemma":[0.00001533728,0.0001130105,0.00002967223,0.00005288948,0.0001630675,0.00007291046,0.0001550732,0.0001364309,3.629152e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001183216,"about_ca_system_score_gemma":0.00003452336,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":6.804825e-7,"about_ca_topic_score_gemma":3.259111e-7,"domain_scores_codex":[0.9991045,0.00003922825,0.0002313335,0.0003417265,0.00006239345,0.0002208115],"domain_scores_gemma":[0.9989597,0.0007723121,0.0001002463,0.00007723282,0.00001956351,0.00007091401],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001350586,0.0002481451,0.00004820011,0.00004690431,0.0000586936,3.945188e-7,0.0006638342,0.01446685,0.854841,0.02162161,0.00007449087,0.1077948],"study_design_scores_gemma":[0.00154632,0.00009021958,0.00004373748,0.000001496471,0.00003123546,0.000007368263,0.00005673646,0.8789901,0.1092524,0.009684596,0.0002048385,0.0000909303],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4980521,0.00001835004,0.5011243,0.0001168116,0.0002057204,0.0003942933,0.00003199267,0.00001783946,0.00003855764],"genre_scores_gemma":[0.856423,0.00000659341,0.1429143,0.0002993061,0.00008344335,0.0002358369,0.00002419181,0.000006685932,0.000006588824],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8645232,"threshold_uncertainty_score":0.4608436,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01750159597603387,"score_gpt":0.3088822499139313,"score_spread":0.2913806539378974,"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."}}