{"id":"W2023313300","doi":"10.1371/journal.pcbi.1004074","title":"Machine Learning Assisted Design of Highly Active Peptides for Drug Discovery","year":2015,"lang":"en","type":"article","venue":"PLoS Computational Biology","topic":"Chemical Synthesis and Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":64,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Fonds de Recherche du Québec - Santé; Compute Canada","keywords":"Computer science; Leverage (statistics); Drug discovery; Heuristics; Machine learning; Artificial intelligence; Biological data; Bioinformatics; Biology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005951234,0.0007829444,0.0007837557,0.0005823081,0.0002811151,0.0006967899,0.0006695502,0.0007150413,0.004086494],"category_scores_gemma":[0.000851846,0.0005063682,0.0006925045,0.0007877575,0.000385495,0.0007166198,0.0006454546,0.001248069,0.001787352],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006434948,"about_ca_system_score_gemma":0.001028482,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003991262,"about_ca_topic_score_gemma":0.0009433095,"domain_scores_codex":[0.9998052,0.00004518954,0.00001205937,0.0000404276,0.0000659491,0.00003130654],"domain_scores_gemma":[0.9998381,0.00005920252,0.00003106725,0.00002455584,0.00002562431,0.00002137484],"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":[0.0004834314,0.0003894202,0.0008863501,0.001121212,0.0001225087,0.0004436867,0.00007167176,0.5548304,0.236399,0.04488034,0.005586057,0.1547859],"study_design_scores_gemma":[0.0002429181,0.0005343544,0.0002282841,0.00005193353,0.00005554396,0.0001759115,0.00002598595,0.8615167,0.09329463,0.02074339,0.02308717,0.00004315599],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08578525,0.002755562,0.8955514,0.0006385648,0.0002559496,0.0005095449,0.001061475,0.002820683,0.01062151],"genre_scores_gemma":[0.3560226,0.003200142,0.6332296,0.0003465399,0.00004752503,0.0008440885,0.001418024,0.0004135916,0.004477898],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004086494,"threshold_uncertainty_score":0.01367068,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03447533993846412,"score_gpt":0.2673522571288562,"score_spread":0.2328769171903921,"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."}}