{"id":"W2890418198","doi":"10.1371/journal.pone.0200769","title":"Virtual screening using covalent docking to find activators for G245S mutant p53","year":2018,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Allard Foundation","keywords":"In silico; Virtual screening; Mutant; Docking (animal); Activator (genetics); Small molecule; Computational biology; Chemistry; Drug discovery; Database search engine; Biology; Biochemistry; Gene; Medicine; Computer science; Search engine","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":[0.0005092514,0.0001535396,0.000215351,0.0001649879,0.0003192382,0.0002409979,0.0005926468,0.00004394909,0.00001887161],"category_scores_gemma":[0.000446455,0.0001621712,0.0000649882,0.0004797978,0.00005785943,0.0005135896,0.0003556038,0.00009469949,0.00002047968],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008446181,"about_ca_system_score_gemma":0.0001504563,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001134903,"about_ca_topic_score_gemma":0.000001855043,"domain_scores_codex":[0.9983559,0.00009537105,0.0002450963,0.0004769691,0.0004673865,0.0003592224],"domain_scores_gemma":[0.9985955,0.0005553933,0.0001008926,0.0003761287,0.0002241765,0.0001479417],"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":[0.0003094915,0.001716509,0.003528514,0.0001758513,0.0008024157,0.0000251622,0.01223341,0.1603205,0.6118457,0.07010815,0.0002607065,0.1386736],"study_design_scores_gemma":[0.0003128701,0.0002857958,0.001796442,0.0002421861,0.00002688496,0.000003861862,0.00003941376,0.7744253,0.2200717,0.002348684,0.0001922655,0.0002545855],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4827619,0.00000743915,0.5166,0.0001759894,0.0001273625,0.0002178303,0.000005963291,0.00005370678,0.00004984621],"genre_scores_gemma":[0.5374853,1.882176e-7,0.4619626,0.0002193649,0.0002655571,0.00001451962,0.000001410339,0.00001326175,0.00003787093],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6141048,"threshold_uncertainty_score":0.661315,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1593098393879741,"score_gpt":0.3385038169202111,"score_spread":0.179193977532237,"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."}}