{"id":"W1991909351","doi":"10.1007/s11095-007-9412-3","title":"Predicting the Solubility of the Anti-Cancer Agent Docetaxel in Small Molecule Excipients using Computational Methods","year":2007,"lang":"en","type":"article","venue":"Pharmaceutical Research","topic":"Drug Solubulity and Delivery Systems","field":"Pharmacology, Toxicology and Pharmaceutics","cited_by":108,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal; BioPhage Pharma (Canada); University of Toronto","funders":"","keywords":"Docetaxel; Solubility; Chemistry; Excipient; Molecular dynamics; Chromatography; Computational chemistry; Organic chemistry; Cancer; Medicine","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":[0.000400407,0.0004021031,0.0006600416,0.000465586,0.000369222,0.0007425326,0.0004877204,0.0009391558,0.0009867445],"category_scores_gemma":[0.001466013,0.0004496113,0.0007335127,0.0004198202,0.0003162731,0.0006361376,0.0002558583,0.0005117152,0.0001570443],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007980121,"about_ca_system_score_gemma":0.001542146,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01301505,"about_ca_topic_score_gemma":0.008873624,"domain_scores_codex":[0.9999135,0.00002703707,0.000006279101,0.00001289059,0.00002797967,0.00001229484],"domain_scores_gemma":[0.999168,0.0006711337,0.0000509963,0.00002150585,0.00006390382,0.00002435417],"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":[0.00003642878,0.00002426687,0.0007939597,0.00002605345,0.00001413151,0.00003088278,0.000005869542,0.9957144,0.0007694452,0.0004813024,0.0001028204,0.002000523],"study_design_scores_gemma":[0.000006977762,0.000009088731,0.00006778359,8.228821e-7,0.000003728457,0.000003160121,0.000002185685,0.9992464,0.0004483501,0.0001624401,0.0000478692,0.000001232706],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9453482,0.0004723315,0.04857831,0.0004601739,0.00003371179,0.00005389205,0.0004713541,0.0002999961,0.004282009],"genre_scores_gemma":[0.9844196,0.0002017299,0.01394723,0.00003870447,0.00001003636,0.00006140174,0.0002405133,0.00003188264,0.001048739],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01301505,"threshold_uncertainty_score":0.02587861,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6327525557782577,"score_gpt":0.6484099683070088,"score_spread":0.01565741252875108,"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."}}