{"id":"W2395351616","doi":"10.1007/978-1-60761-931-4_17","title":"GLARE: A Tool for Product-Oriented Design of Combinatorial Libraries","year":2010,"lang":"en","type":"article","venue":"Methods in molecular biology","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Merck Canada Inc. (Canada)","funders":"","keywords":"Key (lock); Computer science; Reagent; Selection (genetic algorithm); Software; Product (mathematics); Matching (statistics); Combinatorial chemistry; Theoretical computer science; Chemistry; Mathematics; Programming language; Artificial intelligence; Organic chemistry; Operating system","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":[0.0027859,0.00319933,0.002132708,0.002604198,0.0007153411,0.002199644,0.003283581,0.001273924,0.0456588],"category_scores_gemma":[0.003250805,0.001701789,0.002369219,0.002287393,0.0008205625,0.001569069,0.001808222,0.00282697,0.01361369],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006768926,"about_ca_system_score_gemma":0.001559412,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008987812,"about_ca_topic_score_gemma":0.001233278,"domain_scores_codex":[0.9989827,0.0002706707,0.00008229146,0.0001424025,0.0004384885,0.00008341925],"domain_scores_gemma":[0.9987512,0.0008035976,0.0001104711,0.0001419461,0.0001457246,0.00004709436],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001201595,0.0004233307,0.001435277,0.005790799,0.0006427726,0.001111311,0.000343174,0.0951527,0.05359061,0.06905982,0.1617351,0.6095135],"study_design_scores_gemma":[0.001475171,0.0006110417,0.0007133562,0.0004280674,0.0003520014,0.001165367,0.00009083926,0.3399296,0.07345431,0.09943449,0.4820667,0.0002791498],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001790484,0.0004079066,0.9163033,0.0001116668,0.00007817907,0.0002988257,0.003101981,0.07330333,0.004604378],"genre_scores_gemma":[0.01718479,0.001108451,0.9576575,0.0003080273,0.00004654971,0.00194729,0.007315006,0.009444251,0.004988196],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0456588,"threshold_uncertainty_score":0.1527439,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02670892429519233,"score_gpt":0.3759236962905738,"score_spread":0.3492147719953815,"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."}}