{"id":"W4231458620","doi":"10.26434/chemrxiv.12996665","title":"A Comprehensive Discovery Platform for Organophosphorus Ligands for Catalysis","year":2021,"lang":"en","type":"preprint","venue":"ChemRxiv","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; Canadian Institute for Advanced Research; University of Toronto","funders":"Division of Chemistry; Natural Resources Canada; Natural Sciences and Engineering Research Council of Canada; Fonds de recherche du Québec – Nature et technologies; Advanced Research Projects Agency; Defense Advanced Research Projects Agency; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Deutsche Forschungsgemeinschaft; National Science Foundation; Compute Canada; École de technologie supérieure; University of Toronto; University of Utah; Government of Ontario; AstraZeneca; Ministère de l'Économie, de la Science et de l'Innovation - Québec","keywords":"Blueprint; Computer science; Workflow; Intuition; Chemical space; Catalysis; Biochemical engineering; Chemistry; Artificial intelligence; Combinatorial chemistry; Engineering; Drug discovery; Database; Cognitive science; Organic chemistry","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0009608754,0.0008021627,0.0008378476,0.0009500323,0.0005139406,0.001162472,0.001033272,0.0007545304,0.003816518],"category_scores_gemma":[0.001151255,0.0004111116,0.000692777,0.001263239,0.0003115132,0.001100905,0.00098011,0.00103857,0.001308118],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008243364,"about_ca_system_score_gemma":0.001461152,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001666938,"about_ca_topic_score_gemma":0.00250062,"domain_scores_codex":[0.9995566,0.00006735093,0.00002120302,0.00009419669,0.0002198507,0.00004080287],"domain_scores_gemma":[0.9996996,0.00009173521,0.00003342869,0.00009715891,0.0000535419,0.00002458904],"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":[0.00112478,0.0006828603,0.007059786,0.001163823,0.0004321755,0.0005009168,0.0001676773,0.5025524,0.2070208,0.06128343,0.04326577,0.1747456],"study_design_scores_gemma":[0.0002467818,0.0005026968,0.00178413,0.00004022932,0.00009235573,0.0001306956,0.00005439487,0.7961366,0.1314365,0.02483708,0.0446612,0.00007731932],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5426925,0.004412529,0.3568942,0.001908565,0.0002142736,0.0003595893,0.03450877,0.03147908,0.02753056],"genre_scores_gemma":[0.7083836,0.002186477,0.2526373,0.0003364267,0.00005105633,0.0005809591,0.03051121,0.0009513741,0.004361646],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003816518,"threshold_uncertainty_score":0.01276749,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02673192777899917,"score_gpt":0.2850189676736468,"score_spread":0.2582870398946476,"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."}}