{"id":"W2811196319","doi":"10.1111/febs.14598","title":"Deciphering the mechanism of potent peptidomimetic inhibitors targeting plasmepsins – biochemical and structural insights","year":2018,"lang":"en","type":"article","venue":"FEBS Journal","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; Indian Institute of Technology Bombay; National Cancer Institute; National Institutes of Health; European Synchrotron Radiation Facility; Department of Biotechnology, Ministry of Science and Technology, India; Takeda Science Foundation","keywords":"Peptidomimetic; Mechanism (biology); Computational biology; Chemistry; Biology; Biochemistry; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005427781,0.0001211359,0.0001463357,0.0000891684,0.0003232131,0.0001953378,0.0005046937,0.00004156526,0.000008826822],"category_scores_gemma":[0.0001874763,0.00007937094,0.00007113199,0.000234233,0.0001431473,0.0003454952,0.0002934845,0.0002458991,0.000003205895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004348547,"about_ca_system_score_gemma":0.00009238093,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005003892,"about_ca_topic_score_gemma":0.000001226975,"domain_scores_codex":[0.9987007,0.0001810832,0.0003364136,0.000194312,0.0003845772,0.0002029088],"domain_scores_gemma":[0.9990895,0.0002661416,0.0002109959,0.0001610503,0.0001670882,0.0001052019],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002283804,0.0001502355,0.001655069,0.00008885738,0.0004198712,0.0001322214,0.03012845,0.01490256,0.5492407,0.2662908,0.003728216,0.1330347],"study_design_scores_gemma":[0.000814195,0.0002834162,0.008033813,0.000129496,0.00002599261,0.001155731,0.0003563771,0.5019287,0.2836154,0.2029709,0.0003544147,0.0003316144],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.698427,0.0002067333,0.3002017,0.0002930777,0.0007025469,0.00004853272,5.370865e-7,0.0000130802,0.0001067314],"genre_scores_gemma":[0.8761765,0.00000906287,0.1233024,0.00006790032,0.0004298742,7.857729e-7,2.743722e-7,0.00000690104,0.000006267298],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4870262,"threshold_uncertainty_score":0.3236653,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01335343799645722,"score_gpt":0.2651007753689709,"score_spread":0.2517473373725136,"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."}}