{"id":"W4388025982","doi":"10.1039/d3ra06831e","title":"Challenges in natural product-based drug discovery assisted with <i>in silico</i> -based methods","year":2023,"lang":"en","type":"review","venue":"RSC Advances","topic":"Microbial Natural Products and Biosynthesis","field":"Medicine","cited_by":58,"is_retracted":false,"has_abstract":true,"ca_institutions":"Structural Genomics Consortium; University of Toronto","funders":"LifeArc; Alexander von Humboldt-Stiftung; Bill and Melinda Gates Foundation","keywords":"In silico; Drug discovery; Natural product; Computer science; Product (mathematics); Data science; Biochemical engineering; Field (mathematics); Natural Product Research; Natural (archaeology); Computational biology; Management science; Engineering; Bioinformatics; Chemistry; Biology; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001151919,0.0007523645,0.002854113,0.0006770839,0.0000574142,0.00004558683,0.0003248335,0.0002134616,0.00001209742],"category_scores_gemma":[0.0007210972,0.0004386521,0.0003688696,0.001506864,0.0001656708,0.0002672762,0.00005575294,0.001094978,0.00002061197],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002741373,"about_ca_system_score_gemma":0.0006789882,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004804063,"about_ca_topic_score_gemma":0.001196915,"domain_scores_codex":[0.9961489,0.0006143454,0.0008525801,0.001403524,0.0003829004,0.0005977865],"domain_scores_gemma":[0.997611,0.0009562553,0.0004515799,0.0008013302,0.00008632461,0.0000935494],"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":[0.0001951436,0.0002050702,0.000008286303,0.04308906,0.00007178341,0.0001677293,0.00001928203,0.000005628539,0.0001442435,0.00001836119,0.0001239124,0.9559515],"study_design_scores_gemma":[0.0008591044,0.0001291146,0.00008642313,0.05396122,0.0004772065,0.00003883299,0.00005084757,0.0000135938,0.002142968,0.00001240536,0.9415324,0.000695828],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00006217764,0.9949015,0.000006717483,0.002257786,0.0004896757,0.001862923,0.00004416276,0.0001407051,0.000234327],"genre_scores_gemma":[0.0004168561,0.9892158,0.008204988,0.0001447233,0.0003897832,0.00006450848,0.0002144495,0.000125323,0.001223593],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9552557,"threshold_uncertainty_score":0.9998065,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08267115744434121,"score_gpt":0.3844384334847309,"score_spread":0.3017672760403897,"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."}}