{"id":"W3042346442","doi":"10.1007/s11095-020-02876-y","title":"Machine Learning Platform to Discover Novel Growth Inhibitors of Neisseria gonorrhoeae","year":2020,"lang":"en","type":"article","venue":"Pharmaceutical Research","topic":"vaccines and immunoinformatics approaches","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"Aegera Therapeutics (Canada)","funders":"National Institute of Allergy and Infectious Diseases; National Institute of General Medical Sciences","keywords":"Neisseria gonorrhoeae; chEMBL; Drug discovery; Workflow; In silico; Machine learning; Small molecule; Computational biology; Computer science; Virtual screening; Artificial intelligence; Chemistry; Bioinformatics; Biology; Microbiology; Biochemistry; Database","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":[0.0005814224,0.0001417094,0.0001748904,0.00006810616,0.000104161,0.00004583493,0.0003487262,0.00008824388,0.00008269853],"category_scores_gemma":[0.000927227,0.0001202096,0.00008708426,0.0003786601,0.00007374705,0.00001386467,0.0006373888,0.0004631347,0.00006198312],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001669458,"about_ca_system_score_gemma":0.00009008351,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003518465,"about_ca_topic_score_gemma":0.000001884512,"domain_scores_codex":[0.9987159,0.00004455287,0.0003108026,0.0002651799,0.0002041631,0.0004593444],"domain_scores_gemma":[0.9991998,0.00004776923,0.00003937091,0.0001690216,0.0001866642,0.0003573783],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007148294,0.0001151035,0.004479264,0.0001692359,0.00006555839,4.751533e-7,0.0003438045,0.0002039199,0.9875786,0.00124357,0.001136025,0.003949672],"study_design_scores_gemma":[0.001426183,0.001059796,0.001206278,0.00002457547,0.00001638401,0.000006408125,0.0004041821,0.02168648,0.7749248,0.00005418891,0.1989098,0.0002810304],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9881598,0.0007843854,0.002569919,0.00305554,0.00006707551,0.0004507364,0.00003512748,0.00001599016,0.004861471],"genre_scores_gemma":[0.9982866,0.0002121389,0.0005474836,0.000451926,0.000203452,0.00002009915,0.00006604623,0.00002705056,0.0001852102],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2126538,"threshold_uncertainty_score":0.4902007,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1468942048881985,"score_gpt":0.3966117669413303,"score_spread":0.2497175620531318,"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."}}