{"id":"W4281675596","doi":"10.1021/acs.molpharmaceut.2c00029","title":"Machine Learning Study of Metabolic Networks<i>vs</i>ChEMBL Data of Antibacterial Compounds","year":2022,"lang":"en","type":"article","venue":"Molecular Pharmaceutics","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Ministerio de Ciencia e Innovación; Eusko Jaurlaritza; Ministerio de Ciencia, Innovación y Universidades","keywords":"chEMBL; Linear discriminant analysis; Antibacterial activity; Artificial intelligence; Random forest; Machine learning; Antibiotics; Antibiotic resistance; Pseudo amino acid composition; Bacteria; Chemistry; Mathematics; Computational biology; Algorithm; Computer science; Biology; Drug discovery; Biochemistry; Genetics","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":[0.0006727772,0.0004336218,0.0002551224,0.002090472,0.0002931539,0.0004977002,0.0003946964,0.0004321838,0.001816878],"category_scores_gemma":[0.003427336,0.00008649866,0.0005887387,0.001609607,0.0002260183,0.0006289314,0.0002295244,0.0005966102,0.0005469128],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005170137,"about_ca_system_score_gemma":0.0003908471,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00636311,"about_ca_topic_score_gemma":0.005588388,"domain_scores_codex":[0.9997134,0.00009283315,0.00002342333,0.00007873314,0.00005075982,0.00004076473],"domain_scores_gemma":[0.998473,0.0008528613,0.000244961,0.000158875,0.0002070729,0.00006320778],"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":[0.003193783,0.00111722,0.2496359,0.00105982,0.0007444478,0.000830375,0.0001856535,0.3968297,0.03837447,0.006464824,0.01710107,0.2844628],"study_design_scores_gemma":[0.00002684742,0.0002775014,0.06065649,0.00003649484,0.00007578814,0.0004032976,0.0001366515,0.9164117,0.01302841,0.004628974,0.004284968,0.00003295676],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9636159,0.0009254227,0.01348217,0.0006476077,0.00007171331,0.00003165904,0.01756212,0.0007366252,0.00292688],"genre_scores_gemma":[0.9778873,0.0002041292,0.006039309,0.00003893556,0.00001593984,0.00001984661,0.01518332,0.00003236554,0.0005788316],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00636311,"threshold_uncertainty_score":0.01265216,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07064800740377637,"score_gpt":0.368715058554633,"score_spread":0.2980670511508566,"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."}}