{"id":"W4254697769","doi":"10.26434/chemrxiv.14609670","title":"Learning Structure Activity Relationship (SAR) of the Wittig Reaction from Genetically-Encoded Substrates","year":2021,"lang":"en","type":"preprint","venue":"ChemRxiv","topic":"vaccines and immunoinformatics approaches","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Bone and Joint Health Institute; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Machine Intelligence Institute","keywords":"Biotinylation; Pharmacophore; Wittig reaction; Artificial intelligence; Biology; Computational biology; Chemistry; Stereochemistry; Computer science; Molecular biology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001211219,0.0002349758,0.0002539725,0.00002075615,0.0001091622,0.00006038167,0.0003276457,0.0005728249,0.00002594812],"category_scores_gemma":[0.0002765376,0.0001863222,0.0002143676,0.00008610072,0.00006833571,0.000006007352,0.0005279275,0.0007427715,6.153284e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001987516,"about_ca_system_score_gemma":0.0001872878,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006652995,"about_ca_topic_score_gemma":0.00002938913,"domain_scores_codex":[0.9989392,0.00009001946,0.0003149686,0.0003427411,0.0001553099,0.000157731],"domain_scores_gemma":[0.9986395,0.00003288448,0.0004022531,0.0007439466,0.0001424089,0.00003904408],"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":[0.0000318103,0.0000329681,0.02020062,0.00009029609,0.000131564,2.168575e-7,0.0002150112,0.001415224,0.9772208,0.00001303794,0.00006552895,0.0005829442],"study_design_scores_gemma":[0.0002000911,0.00002865348,0.1154644,0.00007325011,0.00007883531,0.000003166781,0.0002471656,0.001146616,0.8818654,0.0004633974,0.0002233348,0.0002056103],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9978318,0.000689907,0.0003076274,0.0001100133,0.0002708503,0.0001894146,0.00001600455,0.00001076479,0.0005736417],"genre_scores_gemma":[0.9974419,0.0001705885,0.001186513,0.00001910135,0.000187641,0.000008061325,0.0007807139,0.00002479853,0.0001806379],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09535532,"threshold_uncertainty_score":0.7597998,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01915226966029794,"score_gpt":0.2331500924596479,"score_spread":0.21399782279935,"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."}}