{"id":"W4386306196","doi":"10.1080/17460441.2023.2251400","title":"Applications of machine learning in microbial natural product drug discovery","year":2023,"lang":"en","type":"review","venue":"Expert Opinion on Drug Discovery","topic":"Microbial Natural Products and Biosynthesis","field":"Medicine","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; Discovery Centre","funders":"Natural Sciences and Engineering Research Council of Canada; Weston Family Foundation","keywords":"Drug discovery; Business process discovery; Chemical space; Computer science; Natural product; Process (computing); Artificial intelligence; Machine learning; Biochemical engineering; Computational biology; Data science; Biology; Bioinformatics; Engineering; Work in process","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":[0.001348571,0.0007015089,0.001138715,0.001720481,0.0001708764,0.001106141,0.0008588588,0.001329722,0.002517245],"category_scores_gemma":[0.002096887,0.0002127644,0.0009047867,0.001758388,0.0006372535,0.001131402,0.000567479,0.002600964,0.001349002],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001062915,"about_ca_system_score_gemma":0.001048635,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009957678,"about_ca_topic_score_gemma":0.001359876,"domain_scores_codex":[0.9993953,0.0002061412,0.00004306534,0.00009022103,0.0002224114,0.00004293612],"domain_scores_gemma":[0.9986495,0.0009518262,0.00009077487,0.00003147783,0.0002321639,0.00004422394],"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":[0.00005218241,0.00006548987,0.0002818107,0.01079535,0.0002259164,0.000158301,0.00005065609,0.002654924,0.0009532387,0.01404059,0.02273854,0.947983],"study_design_scores_gemma":[0.00004393973,0.0002525482,0.00130285,0.006657059,0.0002328816,0.001036551,0.00007022473,0.003250626,0.001738083,0.02542158,0.9599281,0.00006558531],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0003219167,0.9915361,0.002732362,0.002233207,0.0005549569,0.00001767306,0.00003984407,0.00003195183,0.002531796],"genre_scores_gemma":[0.005197407,0.9904269,0.002058444,0.0009279263,0.0005330591,0.00002313841,0.00006478858,0.000006191867,0.0007620552],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.002517245,"threshold_uncertainty_score":0.008421063,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02813201569267489,"score_gpt":0.3281669745174177,"score_spread":0.3000349588247428,"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."}}