{"id":"W4327555752","doi":"10.1021/acs.jcim.2c01373","title":"Using Machine Learning To Predict Partition Coefficient (Log<i>P</i>) and Distribution Coefficient (Log<i>D</i>) with Molecular Descriptors and Liquid Chromatography Retention Time","year":2023,"lang":"en","type":"article","venue":"Journal of Chemical Information and Modeling","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Innovation and Technology Fund; Hong Kong Government; Innovation and Technology Commission","keywords":"Partition coefficient; Chemistry; Analyte; Chromatography; Retention time; Octanol; Distribution (mathematics); Partition (number theory); Analytical Chemistry (journal); Mathematics","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":[0.002057186,0.001479022,0.001190369,0.001758219,0.0002437478,0.001236582,0.0008231323,0.001185384,0.0008876696],"category_scores_gemma":[0.005625731,0.0003496792,0.001255815,0.00152795,0.0004423328,0.001318564,0.0005912988,0.001438119,0.0004895568],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009039143,"about_ca_system_score_gemma":0.0009962057,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004225961,"about_ca_topic_score_gemma":0.003107489,"domain_scores_codex":[0.9991562,0.0003287972,0.0000631449,0.0001957563,0.0001929525,0.00006317604],"domain_scores_gemma":[0.9973727,0.001860808,0.00043543,0.0001079541,0.0001802366,0.0000428747],"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":[0.0002570331,0.0004357295,0.01167511,0.0001842201,0.0003090905,0.0000789783,0.00002556242,0.8625649,0.004950808,0.001251447,0.0009134759,0.1173536],"study_design_scores_gemma":[0.000006516825,0.00005906534,0.0006647256,0.000006947054,0.00002039854,0.00002015749,0.000002781039,0.9966627,0.001670188,0.0006440182,0.0002324035,0.00001019875],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.323851,0.004130541,0.6640781,0.00093142,0.0001301018,0.0001853637,0.0009637609,0.002582329,0.003147456],"genre_scores_gemma":[0.9176403,0.001090011,0.07831713,0.0003248173,0.00007577585,0.0001911817,0.0009704036,0.00007328697,0.001317039],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004225961,"threshold_uncertainty_score":0.01087952,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02306168832052962,"score_gpt":0.2655526638927191,"score_spread":0.2424909755721895,"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."}}