{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000295824,0.0004970657,0.000433133,0.0005530819,0.0002054797,0.000639593,0.0003557812,0.0003856421,0.002873291],"category_scores_gemma":[0.00044657,0.0001506753,0.0004946464,0.0003211495,0.00009000394,0.0003002406,0.0003123718,0.0006283125,0.000882762],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003277221,"about_ca_system_score_gemma":0.0005632409,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006896956,"about_ca_topic_score_gemma":0.001083208,"domain_scores_codex":[0.9998993,0.00001443836,0.000005504362,0.00002662016,0.00003693369,0.00001727755],"domain_scores_gemma":[0.9999055,0.00003499633,0.00001685999,0.000008902638,0.00002067581,0.0000131797],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001507569,0.001775492,0.01112956,0.001068818,0.0005171151,0.0006675617,0.00007339387,0.09770937,0.3322113,0.01041527,0.03635294,0.5065717],"study_design_scores_gemma":[0.0001977876,0.0009875188,0.004361673,0.00006401255,0.0002619128,0.0002788383,0.00004200945,0.7804423,0.1528671,0.007585316,0.05286758,0.00004402191],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5961805,0.00941572,0.2965396,0.003330791,0.0009909276,0.0008996631,0.02232249,0.03182303,0.03849733],"genre_scores_gemma":[0.7074513,0.003940305,0.2472652,0.0008610665,0.0001695725,0.0005508193,0.02479822,0.0003222506,0.01464115],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002873291,"threshold_uncertainty_score":0.009612143,"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."}}