{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00152365,0.0001803767,0.0003950955,0.0001659437,0.0001631276,0.00004738083,0.002472858,0.00002142506,0.00003182141],"category_scores_gemma":[0.00006441606,0.0002066085,0.00007483434,0.000942812,0.00004817148,0.00023639,0.00465125,0.0004255243,9.048641e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002039214,"about_ca_system_score_gemma":0.000109749,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005457519,"about_ca_topic_score_gemma":0.000001855257,"domain_scores_codex":[0.9965477,0.001419509,0.0005105405,0.0004868641,0.0007858531,0.0002495163],"domain_scores_gemma":[0.9982241,0.0002267314,0.0003554284,0.001019237,0.0001003193,0.00007422546],"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":[0.0001336963,0.001216358,0.004483166,0.00002739937,0.0002683239,0.00006013722,0.0008775163,0.9469067,0.03367139,0.00352679,0.00006286581,0.008765643],"study_design_scores_gemma":[0.001468376,0.0002697736,0.001119185,0.000003541561,0.0001034894,0.00002256576,0.00007240391,0.9677413,0.01546113,0.0001504536,0.01339577,0.0001920106],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6433626,0.001276061,0.3540223,0.00008611318,0.000683152,0.0003234005,0.00004923141,0.00005064695,0.0001464553],"genre_scores_gemma":[0.9849046,0.00003767037,0.01469081,0.0001696609,0.00004279091,0.000009289894,0.0001095814,0.00002143869,0.00001408142],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3415421,"threshold_uncertainty_score":0.842525,"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."}}