{"id":"W2992521905","doi":"10.1101/863506","title":"A yeast platform for high-level synthesis of natural and unnatural tetrahydroisoquinoline alkaloids","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Microbial Natural Products and Biosynthesis","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Division of Molecular and Cellular Biosciences; Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies; Concordia University; National Science Foundation","keywords":"Tetrahydroisoquinoline; Benzylisoquinoline; Natural product; Chemical space; Yeast; Moiety; Chemistry; Stereochemistry; Combinatorial chemistry; Biosynthesis; Drug discovery; Biochemistry; Gene","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":[0.0004838449,0.0005356031,0.0003500661,0.0002645383,0.0001775331,0.0005762195,0.0003706487,0.0003082279,0.001188793],"category_scores_gemma":[0.0002116326,0.0001828651,0.0003068823,0.000307468,0.0002473076,0.000245365,0.0005721347,0.0008389634,0.001487995],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002576325,"about_ca_system_score_gemma":0.0002748386,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005570127,"about_ca_topic_score_gemma":0.0007181749,"domain_scores_codex":[0.9997689,0.00004800421,0.00002460095,0.00005131387,0.00008074567,0.00002645422],"domain_scores_gemma":[0.9998744,0.00002516361,0.00002477715,0.00003469248,0.00001707962,0.00002397538],"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":[0.00005605623,0.00003244116,0.0001011896,0.00004955776,0.000009329653,0.00006865914,0.00002437187,0.0003055946,0.9956397,0.0005093883,0.00013544,0.003068304],"study_design_scores_gemma":[0.00001301736,0.000120023,0.0003352879,0.00000748466,0.00001128845,0.0001314271,0.00001999142,0.001853128,0.9892147,0.0001430167,0.008140833,0.000009735777],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7554741,0.003736723,0.2229644,0.0006144787,0.0002773381,0.0002835846,0.005540164,0.003437955,0.007671229],"genre_scores_gemma":[0.8859399,0.001947178,0.09891745,0.00007866856,0.00002852253,0.0001365763,0.005955837,0.000568172,0.006427618],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001188793,"threshold_uncertainty_score":0.003976882,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01686696597606227,"score_gpt":0.2236521472084231,"score_spread":0.2067851812323608,"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."}}