{"id":"W591730751","doi":"10.1371/journal.pbio.1002164","title":"Open Access Target Validation Is a More Efficient Way to Accelerate Drug Discovery","year":2015,"lang":"en","type":"article","venue":"PLoS Biology","topic":"Protein Degradation and Inhibitors","field":"Biochemistry, Genetics and Molecular Biology","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministero dello Sviluppo Economico; Fundação de Amparo à Pesquisa do Estado de São Paulo; Ontario Ministry of Economic Development and Innovation; Wellcome Trust; Genome Canada; CHDI Foundation; Innovative Medicines Initiative; GlaxoSmithKline; Pfizer; Eli Lilly and Company","keywords":"Drug discovery; General partnership; Incentive; Scarcity; Biology; Process (computing); Data science; Business; Computer science; Bioinformatics; Economics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002275363,0.0001538321,0.0001683262,0.00006458846,0.00006553932,0.0002440415,0.0009429077,0.0001214587,0.00006919596],"category_scores_gemma":[0.0001923061,0.0001275719,0.00004521769,0.0001547075,0.00005225962,0.00002755994,0.001320453,0.00006799631,0.00009439031],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000278532,"about_ca_system_score_gemma":0.0001050498,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008749386,"about_ca_topic_score_gemma":0.00001016607,"domain_scores_codex":[0.9988647,0.000104766,0.0002107139,0.0004793935,0.00009394609,0.0002464602],"domain_scores_gemma":[0.9992151,0.000007355142,0.00008860317,0.0003859951,0.0001417668,0.0001611658],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004530802,0.0003327884,0.008737619,0.00001575982,0.0001010839,0.000002399267,0.0004070369,0.0003938065,0.8575298,0.0005454926,0.1302166,0.00126456],"study_design_scores_gemma":[0.0004861992,0.0002059097,0.0004320799,0.000008799871,0.000007661187,0.000001987831,0.00006746345,0.000159681,0.9086194,0.0002383059,0.08956975,0.000202754],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9903252,0.000141043,0.002264634,0.003859793,0.0003171919,0.0006070587,0.00008959272,0.00001881767,0.002376646],"genre_scores_gemma":[0.9926349,0.00001593937,0.0008213137,0.003626559,0.0002607344,0.000127566,0.0006297304,0.00001895397,0.001864293],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05108962,"threshold_uncertainty_score":0.5202232,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06445218054785974,"score_gpt":0.3545875644790109,"score_spread":0.2901353839311512,"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."}}