{"id":"W2210079610","doi":"10.1021/ci050480j","title":"Can ‘Bacterial-Metabolite-Likeness' Model Improve Odds of ‘in Silico' Antibiotic Discovery?","year":2006,"lang":"en","type":"article","venue":"Journal of Chemical Information and Modeling","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"In silico; Quantitative structure–activity relationship; Antimicrobial; Computational biology; Similarity (geometry); Computer science; Artificial intelligence; Machine learning; Chemistry; Biology; Biochemistry; Microbiology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001519132,0.0004759943,0.000632761,0.0004966969,0.00010947,0.0004670157,0.0007131879,0.0006435451,0.0008272976],"category_scores_gemma":[0.004513228,0.0001746794,0.0004424879,0.0003992569,0.0003797305,0.001171436,0.000417429,0.0006373197,0.0002700759],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004688947,"about_ca_system_score_gemma":0.0004360369,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007753238,"about_ca_topic_score_gemma":0.00122019,"domain_scores_codex":[0.9996057,0.0002162515,0.00001976424,0.0000534786,0.00007619138,0.00002870707],"domain_scores_gemma":[0.9987,0.0008793057,0.0001775348,0.0001016651,0.0001047301,0.00003667906],"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":[0.000273368,0.0002020358,0.008144933,0.0001426237,0.00008766929,0.00009666559,0.00004945848,0.9127614,0.02113261,0.006232487,0.0003684611,0.05050821],"study_design_scores_gemma":[0.00001139314,0.0001431993,0.0007337083,0.000004154788,0.00001338153,0.0000357817,0.000005563545,0.9929569,0.003067284,0.002681378,0.0003385102,0.00000872455],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4891545,0.0006562859,0.5027452,0.0009940052,0.00003413553,0.0001190851,0.0003226281,0.0007816708,0.005192535],"genre_scores_gemma":[0.9500303,0.0002966698,0.04841309,0.0002163512,0.00001840641,0.00008266183,0.0002884164,0.00003025327,0.0006237882],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001519132,"threshold_uncertainty_score":0.008033991,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01450227788237061,"score_gpt":0.2646014912158303,"score_spread":0.2500992133334597,"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."}}