{"id":"W4327979840","doi":"10.26434/chemrxiv-2023-rnc4l","title":"Novel Pharmacokinetics Profiler (PhaKinPro): Model Development, Validation, and Implementation as a Web-Tool for Triaging Compounds with Undesired PK Profiles","year":2023,"lang":"en","type":"preprint","venue":"ChemRxiv","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia Hospital","funders":"National Center for Advancing Translational Sciences; National Institute of General Medical Sciences; NIH Office of the Director; National Institutes of Health","keywords":"DrugBank; Pharmacokinetics; Bioavailability; Cmax; ADME; Drug development; Pharmacology; Physiologically based pharmacokinetic modelling; Drug discovery; Chemistry; Drug; Computational biology; Medicine; Biology; 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":[0.002238161,0.001477859,0.001239092,0.0007099779,0.0003301385,0.001159939,0.001873657,0.0007563802,0.01118786],"category_scores_gemma":[0.003292584,0.0008059442,0.001012785,0.0004819402,0.0002730122,0.0009871465,0.0009222386,0.001743294,0.003983431],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006375102,"about_ca_system_score_gemma":0.002677563,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003707714,"about_ca_topic_score_gemma":0.003233034,"domain_scores_codex":[0.9995708,0.0001366303,0.0000511262,0.00008408178,0.0001248804,0.0000324401],"domain_scores_gemma":[0.9985912,0.0008239257,0.0001360831,0.0001609585,0.0002221933,0.00006549307],"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":[0.002188993,0.001030861,0.01305608,0.00260968,0.000961922,0.001005591,0.0003513161,0.638373,0.03034621,0.01493824,0.06864773,0.2264903],"study_design_scores_gemma":[0.0001922829,0.0001912414,0.0007672122,0.00004599902,0.00007091084,0.0001609795,0.00001683551,0.9651006,0.01093884,0.002370641,0.02010159,0.00004289744],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04739719,0.0007114292,0.7814696,0.0006395006,0.0002383903,0.001116885,0.0191191,0.1414847,0.007823113],"genre_scores_gemma":[0.2883343,0.001739801,0.6662708,0.0007266099,0.0001019249,0.004234497,0.02077303,0.01040135,0.007417806],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01118786,"threshold_uncertainty_score":0.03742707,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1081067868377187,"score_gpt":0.3836205690490447,"score_spread":0.2755137822113261,"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."}}